# A Physical Survey of Meteoroid Streams: Comparing Cometary Reservoirs

N. Buccongello<EMAIL_ADDRESS>P. G. Brown  D. Vida  A. Pinhas  Department of
Physics and Astronomy, University of Western Ontario, London, Ontario, N6A
3K7, Canada  Western Institute for Earth and Space Exploration, University of
Western Ontario, London, Ontario, N6A 5B7, Canada  Columbus Technologies and
Services Inc. & Jacobs Space Exploration Group, NASA Marshall Space Flight
Center, Huntsville, Alabama, 35812, USA

###### Abstract

In this work, we present an optical survey of mm-sized meteoroids using the
Canadian Automated Meteor Observatory’s (CAMO) mirror tracking system. The
system tracks meteors to magnitude +7.5 through an image-intensified
telescopic system which has a spatial accuracy of $\sim$$1\text{\,}\mathrm{m}$
and a temporal resolution of $10\text{\,}\mathrm{ms}$. We analyze 41 meteors
from 13 showers with known parent bodies, recorded between 2016 and 2022. We
fit a numerical ablation and fragmentation model to our data which models
meteoroid fragmentation as erosion into $10\text{\,}\mathrm{\SIUnitSymbolMicro
m}500\text{\,}\mathrm{\SIUnitSymbolMicro m}$ constituent grains and uses the
observed wake as a hard constraint on the model parameters. We measure average
bulk meteoroid densities which are consistent with in situ measurements: 602
$\pm$ $155\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$ for Jupiter-family
and 345 $\pm$ $48\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$ for Halley-
type showers. The Geminids had the highest measured bulk density of 1387 $\pm$
$240\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$, consistent with
carbonaceous material. We fail to reproduce the high bulk density
(>$3000\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$) for Jupiter-family
meteoroids previously reported in the literature derived using fragmentation
models on data sets with fewer observational constraints. We also provide
estimates of the meteoroid grain sizes, grain mass distributions, and energy
necessary to trigger the erosion for meteoroids in the analyzed showers.

##  1 Introduction

Comets are among the most pristine bodies in our solar system and their well-
preserved primordial nuclei are direct windows as to how the earliest solar
system solids formed [Blum et al., 2022]. They also may have played a role in
water delivery and the subsequent rise of life on Earth [Thomas et al., 2007].
Meteoroids, dust particles released from comets, are also proxies for the
physical properties of their parent bodies. Thus, inferring the properties of
cometary meteoroids permits a deeper understanding of cometary nuclei, and
indirectly, the agglomeration processes that operated early in solar system
history within the protoplanetary disk. From early observations of meteors, a
physical picture of cometary meteoroids as “dust-balls” has emerged [Hawkes &
Jones, 1975], with aggregates of small silicate grains glued together similar
to the structure of interstellar dust [Greenberg & Li, 1999]. This picture of
cometary meteoroids has been largely confirmed by the findings of the Rosetta
spacecraft [Hornung et al., 2016].

Our understanding of the formation and evolutionary pathways of comets at
various length scales remains incomplete [Blum et al., 2022]. While in situ
comet observations have provided substantial information about a few nuclei
[e.g. 1P/Halley, 81P/Wild, 67P/Churyumov-Gerasimenko; Cochran et al., 2015],
such missions are few, expensive and time-consuming. There also remains a
question as to how representative these few nuclei are of their respective
cometary populations. For example, do Jupiter-family comets (from the
scattered disk) have similar properties to Halley-type or nearly isotropic
comets which formed at different locations in the solar nebula [Nesvorný et
al., 2017, Vokrouhlický et al., 2019]?

Meteoroids of cometary origin whose orbits pass close to Earth provide a
unique opportunity to survey cometary material with relative ease. For a
number of meteor showers, parent bodies are known [Jenniskens, 2006].
Therefore, if one could determine the physical properties associated with
meteoroids from a specific shower, those properties will also be diagnostic of
that shower’s parent body and lead to better constraints on the wider cometary
population.

Here we report a survey of meteoroid physical properties that samples a dozen
meteor showers, each with a known parent body. For each shower, we have
identified several mm-sized shower meteoroids which were recorded by the
mirror-tracking system of the Canadian Automated Meteor Observatory (CAMO).
The mirror tracking system captures videos of meteors at high temporal
($10\text{\,}\mathrm{ms}$) and spatial (a few meters) resolutions from two
stations.

By fitting a meteoroid ablation and fragmentation model to the observed meteor
characteristics as a function of time (lightcurve, velocity, wake) we can
forward model the data to determine the meteoroid’s physical properties, such
as its bulk density, grain size distribution and other parameters defined in
the model [Vida et al., 2023a]. Uniquely to CAMO, the wake (brightness
distribution behind a meteor due to grain release and subsequent ablation) is
used as a model constraint, permitting a more robust forward model of the
parameters. CAMO routinely records wake for more than 90% of all tracked
meteors [Subasinghe et al., 2016].

In Section 2 we provide a review of the known physical properties of meteor
showers and cometary parents. We focus on meteoroid bulk densities and grain
mass distributions and review the works which determined these values in the
literature. In Section 3 we describe the equipment used to observe and collect
data, as well as our data selection and reduction process. Section 4 explains
our modelling process and how we used the ablation model to determine the
physical properties of each meteoroid. Section 5 presents our key results,
compares them to the previous literature and discusses the implications.
Section 6 summarizes this work and highlights our main findings.

##  2 Meteoroid Physical properties: Previous Work

Our knowledge of the properties intrinsic to cometary nuclei comes from direct
measurements as well as estimates of meteoroid properties from associated
meteor showers. For the latter, a major limitation in the past has been the
temporal and spatial resolution of observations. While meteor lightcurves are
typically well measured, position measurements are limited to resolutions of
tens of meters at best and wake measurements are virtually impossible due to
limited pixel resolution and the movement of meteors across a static image
plane Shadbolt & Hawkes [1995], Borovička et al. [2007], Kikwaya et al.
[2011], Vojáček [2017]. This means that past attempts to infer meteoroid
properties via ablation modelling are likely inaccurate and/or ambiguous, as
imprecise measurement of meteor deceleration or lack of resolution to resolve
wake make it difficult to constrain one unique set of model parameters.

For example, even when Campbell-Brown et al. [2013] attempted to model ten
CAMO recorded meteors, each with very precise lightcurves and dynamics, two
different models (the erosion model from Borovička et al. [2007] and the
thermal disruption model from Campbell-Brown & Koschny [2004]) were able to
accurately predict meteoroid lightcurves and deceleration with significantly
different fragmentation processes. However, both models were unable to
simultaneously reproduce the wake. This work hopes to improve on these past
modelling attempts by also fitting the wake in order to sufficiently constrain
the model results.

In this section, a brief summary of the known physical properties of mm to cm-
sized cometary meteoroids and their parent comets is presented for both
general context and comparison. A full list of showers and their parent bodies
examined in this paper are shown in Table 3, while a full list of their
properties are listed in A and B.

###  2.1 Meteoroid physical properties: Meteor Population Groups

Bulk density is arguably the most important meteoroid physical property in
relation to parent bodies. Additionally, potential damage to satellites and
spacecraft caused by impacts is strongly affected by whether the particle is a
porous dust aggregate or a monolithic body [Drolshagen & Moorhead, 2019]. For
meteors, bulk density is difficult to determine due to complicating effects
such as fragmentation, variations in ablation behaviour and shape, as well as
differences in structure and chemical composition [Kikwaya et al., 2009]. With
these caveats in mind, attempts have been made in the past to estimate bulk
densities of various meteor populations including meteor showers and comet
nuclei.

Some of the earliest work to categorize small meteoroid populations by
physical characteristics was that of Ceplecha [1967] and Ceplecha [1968]. They
estimated the resistance to ablation of small meteoroids from Super-Schmidt
observations and found that most could be classified into 4 categories (A, B,
C and D) based on a one-dimensional strength/ablation parameter $K_{B}$. This
parameter depends on the beginning height of the meteor, its initial velocity,
and its entry angle. In general, the average meteor begin height increases and
the strength decreases from classes A to D. From this trend, Ceplecha [1988]
associated dense ordinary chondrite material with group A. Intermediate
(carbonaceous chondrite) material with smaller perihelion and aphelion close
to Jupiter (such as the Geminids) belonged to group B [Ceplecha, 1977].

Most relevant to this work, classical meteor showers with known parent comets
are contained in group C, which is further subdivided into three populations:
$C_{1}$ with ecliptic short period orbits, $C_{2}$ with randomly inclined long
period orbits and $C_{3}$ with both short-period orbits and random
inclinations [Ceplecha, 1988]. Finally, group D contains extremely soft
cometary material (such as the Draconids) [Ceplecha, 1967]. Ceplecha [1977]
later updated their findings to include data from multiple mass ranges and to
correct bulk densities that were overestimated in previous work. Ceplecha
[1988] further extended these strength populations with supplementary data
from TV observations and small camera data by introducing small changes to
$K_{B}$ accounting for the different system sensitivities. Note that this one-
dimensional approach only identified relative strength differences; the
absolute properties (like bulk density) are not explicitly estimated.

Bellot Rubio et al. [2002] later re-analysed the Super-Schmidt data and
determined quantitative estimates of bulk density for groups A, B and C using
ablation model comparisons to data. It is important to note however, that
Bellot Rubio et al. [2002] used single-body ablation theory as opposed to
quasi-continuous fragmentation, so the measured densities are likely
underestimated [Borovička, 2006]. Finally, Kikwaya et al. [2011] was able to
estimate densities for group A and C meteors using data from a number of TV
and camera systems. This work used the thermal ablation model of Campbell-
Brown & Koschny [2004] which assumed meteoroids disintegrated into constituent
grains upon being heated to a threshold temperature. Results from each study
are summarized in Table 5.10 in Kikwaya et al. [2011], and we provide an
updated version in Table 1.

Table 1: Average bulk densities ($\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$) in Ceplecha’s four meteor classes from different studies. Adapted from Kikwaya et al. [2011]. Work |  Instrument |  Group A |  Group B |  Group C |  Group D
---|---|---|---|---|---
Ceplecha (1967) |  Photographic Super-Schmidt |  4000 |  2200 |  1400 |  -
Ceplecha (1977) |  Photographic Super-Schmidt |  2100 |  1000 |  600 |  200
Ceplecha (1988) |  Photographic and TV |  1400-2700 |  650-1700 |  550-910 |  180-380
Bellot Rubio et al. (2002) |  Photographic Super-Schmidt |  2400 |  1400 |  400 |  -
Kikwaya et al. (2011) |  TV Systems |  3800 $\pm{800}$ |  - |  800 |  -

###  2.2 Meteoroid Physical Properties: Inferences from Observations of Meteor
Showers

Several works have previously attempted to directly estimate the bulk density
of meteoroids in specific meteor showers. As part of the Harvard Meteor
Program, Verniani [1967] analyzed sporadic and shower meteors captured by
Super-Schmidt cameras. They used the luminous efficiency value from Verniani
[1965] to establish a mass scale from which meteoroid densities were
determined [Verniani, 1967]. However, Ceplecha [1988] noted that this work
underestimated densities in a statistical sense due to the use of single-body
theory and a low luminous efficiency value [several times lower than what is
currently accepted; Popova et al., 2019]. As a result, the estimated meteoroid
mass was significantly increased, biasing the selection of the dataset by
removing most asteroidal-type objects.

Verniani [1969] summarized the individual estimates of mass and deceleration
for Super-Schmidt meteors by Jacchia et al. [1967] and through this
summarization, postulated that “most meteors are of cometary origin, and are
porous, crumbly objects composed of loosely conglomerate sponge-like
material”. He also noted that the dataset was not a random sample and that the
result only applies to fairly large (gram-sized) meteoroids, so the generality
of this conclusion as applied to all meteoroids has been challenged [Ceplecha
et al., 1998]. Nevertheless, it establishes the highly friable and low-density
nature of some meteoroids, particularly at smaller masses.

Babadzhanov [2002] used a model incorporating quasi-continuous fragmentation
to fit the lightcurves of photographic meteors and estimate the bulk density
of meteoroids from various showers. This work was among the first attempts at
directly measuring accurate bulk densities but failed to include the meteor
deceleration [Borovička, 2006]. Neglecting deceleration leaves ambiguity in
bulk density results, as multiple combinations of parameters can be found to
match the same lightcurve by simply changing the ablation coefficient.
Babadzhanov & Kokhirova [2009] later expanded on this work with double station
photographic observations but again neglected to include deceleration in the
model fits.

Bellot Rubio et al. [2002] used single-body theory to re-examine the bulk
densities of Super-Schmidt meteors and was able to estimate densities for some
shower meteors while also categorizing them into Ceplecha’s meteor groups.
This work was limited again due to the small number of data points per Super
Schmidt meteor and neglect of meteoroid fragmentation [Borovička, 2006].

Vojáček [2017] reported what is likely the most accurate and complete
measurement of mm to cm-sized meteoroid densities to date using the [Borovička
et al., 2007] erosion model to simultaneously fit meteor light curves and
deceleration. However, only a small number of shower meteors were reduced;
sporadics composed the majority of the analyzed events.

Results from each study are summarized in Table 2. Few studies have estimated
the densities of individual shower meteoroids. Hughes [1995] found a density
of 320 $\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$ for the Perseids.
Kikwaya et al. [2011] focused on sporadic meteors, but 10 of the analyzed
meteors were Perseids. It was found that they had an average density of
$420\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}820\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$.
However, it should be noted that this work measured densities much higher than
what would be expected for certain meteor populations (e.g. meteors on
Jupiter-family orbits). It is suspected that the data used in this study was
not of a high enough resolution to observe the true meteor deceleration for
such JFC meteors, so the model used was biased to higher-density meteors that
did not decelerate as significantly.

Table 2: Average bulk densities ($\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$) of meteor showers which are the focus of the current work, as determined from various sources. Showers are labelled by their official three-letter IAU Meteor Data Center code. Work |  CAP |  GEM |  LEO |  LYR |  NTA |  ORI |  PER |  STA
---|---|---|---|---|---|---|---|---
Verniani (1967) |  140 |  1060 |  600 |  390 |  260 |  250 |  290 |  280
Babadzhanov (2002) |  - |  2900 ($\pm$ 600) |  400 ($\pm$ 100) |  - |  1500 ($\pm$ 200) |  - |  1300 ($\pm$ 200) |  1500 ($\pm$ 200)
Babadzhanov and Kokhirova (2009) |  2100 |  2900 ($\pm$ 600) |  400 ($\pm$ 100) |  - |  1600 ($\pm$ 400) |  900 ($\pm$ 500) |  1200 ($\pm$ 200) |  1600 ($\pm$ 400)
Bellot Rubio et al. (2002) |  450 |  1940 |  - |  - |  420 |  - |  600 |  -
Vojacek (2017) |  - |  1788 |  - |  230 |  1967 |  - |  167 |  1967

###  2.3 Meteoroid Physical Properties: Inferences from Interplanetary Dust
Particles

The closest analogs to shower meteoroids that are available for laboratory
examination are interplanetary dust particles (IDPs) [Bradley, 2007a]. These
particles originate from a broad range of dust-producing bodies extending from
the inner asteroid belt to the Kuiper belt and in some cases are among the
most primitive astromaterials studied in a laboratory [Greenberg, 2000]. While
the specific parent bodies of IDPs are indeterminable, certain IDPs known as
chondritic porous (CP) show characteristics strongly suggestive of links to
cometary nuclei. They therefore represent the best material for comparison to
cometary meteors [Bradley, 2007b] and provide a powerful comparison for our
work.

Figure 1: An IDP from the Aircraft Collected Particle (ACP) collection (per
NASA Cosmic Dust Collection).

Figure 1 shows an IDP a few $\mu$m in size, collected in the atmosphere by
NASA aircraft. IDPs such as this one are very reminiscent in structure to
cometary meteors as both particles have been released from a (likely) cometary
parent body, are made of smaller grains and have undergone some space
weathering before eventually reaching Earth’s atmosphere.

CP IDPs usually have extremely high porosities (70%-80%) and low bulk
densities of around $500\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$ for
10-50 $\mu$m-sized grains [Greenberg, 2000], though they range anywhere from
300 to $6000\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$ [Bradley, 2007a].
CP IDPs with densities above
$3500\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$ usually contain a large
FeNi sulphide grain [Fraundorf et al., 1982] [Love et al., 1994].

Joswiak et al. [2007] used He release combined with an entry model to
distinguish high and low entry speed IDPs - the faster particles were
associated with cometary parents. This IDP population had bulk densities
ranging from 700 to $1700\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$,
providing the best laboratory measure of what might be expected for cometary
meteoroid bulk densities. Note that this cometary IDP data set, derived from
the work of Love et al. [1994], is likely biased toward higher densities as
highly porous IDPs (or cluster IDPs) are underrepresented due to their fragile
nature [Rietmeijer, 1998].

While CP IDPs are likely of cometary origin, they have some characteristics
which suggest they are not perfectly identical to cometary grains that have
been freshly released from a comet’s nucleus. IDPs have been released from
their parent body 100,000 years ago or longer, while material identified with
cometary meteor showers is likely to have only been released on shorter
timescales [1000s of years; Greenberg, 2000] due to the times required for
dynamical orbit evolution which merges showers into the sporadic background
population [Pauls & Gladman, 2005]. This means that IDPs have undergone
considerably more processing and weathering when compared to cometary dust,
leading to physical differences in both structure and porosity [Greenberg,
2000]. It has also been suggested that meteoroids could represent an
intermediate step between mm to cm-sized cometary dust particles and
$10\text{\,}\mathrm{\SIUnitSymbolMicro
m}100\text{\,}\mathrm{\SIUnitSymbolMicro m}$ IDPs [Greenberg, 2000]. The main
limitation with IDPs is that they cannot be reliably tied back to a single
parent comet [Trigo-rodriguez & Blum, 2021], though their heating history
during atmospheric entry does provide some clues into their dynamical origin
[Bradley, 2007b].

###  2.4 Parent Comet Physical Properties

For cometary nuclei, density measurements represent a global estimate of the
bulk density and macroporosity of these kilometre-sized bodies. While such
values will differ from the bulk density and microporosity inherent to mm to
cm-sized meteoroids ejected from the parent, these results are still
insightful.

There are several techniques for measuring nuclear bulk density as summarized
in Groussin et al. [2019], most of them indirect. Such techniques include
density inferences from non-gravitational effects (e.g. outgassing), radar
observations, tidal disruption and inversion of shape models based on
lightcurves.

Among these techniques, the most accurate is a spacecraft flyby. Volume
measurements can be made through optical imaging, though these rely on derived
shape models which are usually incomplete as not all sides of the comet
nucleus can be observed at once [Weissman et al., 2008]. Mass measurements
have also been attempted by measuring the comet’s gravitational effects on
spacecraft, but this is also challenging as the small cometary mass, and
therefore weak gravitational force, does not have an appreciable effect on the
spacecraft’s high-velocity orbit [Weissman et al., 2008].

In situ measurements of this type have only been accomplished for 6 comets:
1P/Halley, 9P/Tempel, 19P/Borelly, 67P/Churyumov-Gerasamenko, 81P/Wild, and
103P/Hartley. Note there have also been “blind” flybys of comet 21P/Giacobini-
Zinner and 26P/Grigg-Skjellerup where cameras were not present on the
spacecraft [Groussin et al., 2019]. Only one of these comets has a meteor
shower directly probed in this study, 1P/Halley, which was found to have a
nucleus bulk density likely between 260 and
$1200\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$ from various studies
[Greenberg & Li, 1999, Skorov & Rickman, 1999]. By far the most accurate
nuclear density to date was made by Rosetta during its rendezvous with
67P/Churyumov-Gerasamenko, producing an estimated global bulk density of 532
$\pm$ $7\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$ [Jorda et al., 2016].

Any comet that has not been investigated with an in situ mission requires an
indirect way of measuring bulk density. This usually involves using telescopic
observations to estimate the non-gravitational forces produced from a comet’s
surface, which when combined with the comet’s orbital information, can
constrain the mass range [Weissman et al., 2008]. Volume can be constrained
from telescopic observations of dust production which give an estimate of the
free-sublimating areas [Rickman et al., 1987], however irregular shapes are
commonplace for comets, so this process can be quite difficult. Regardless,
these measurements do provide reference comparisons for our work. Cometary
nuclei with reliable bulk density measurements for parent bodies germane to
our study are summarized in A.

Some studies that required cometary nuclei bulk densities for their models
assume values based on average comet densities [e.g. Molina & Moreno, 2013,
for C/1861 G1 (Thatcher)]. We chose to not include these values as they are
not actual measurements. One notable exception is Masiero et al. [2021] and
references therein, who assumed a density of
$3000\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$ for (3200) Phaethon. This
was an informed assumption, as it was suggested in Clark et al. [2010] that
CK4 carbonaceous chondrites are material analogs for Phaethon, so this value
does have a physical basis.

From a summary of all available density measurements, Groussin et al. [2019]
found that cometary nuclei have an average bulk density of 480 $\pm$
$220\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$ and high porosity
(70-80%). They also concluded that the nuclei are very weakly bound on global
scales with tensile strengths less than $100\text{\,}\mathrm{Pa}$.

###  2.5 Comet Dust and Grain Implications

The dust particles measured in situ shortly after release from comets are also
of particular interest, as these are the same material (albeit with less
evolutionary processing) that eventually become meteor showers on Earth. This
means their bulk density, grain size range and distribution are especially
relevant for comparison to this work.

The size frequency distribution of constituent grains is a fundamental
property of meteoroids. A closely associated measure, mass index, represents
the particle mass distribution either in a meteoroid stream or the sporadic
background [Pokorný & Brown, 2016, Janches et al., 2019]. This index can be
represented in a number of ways, however we will specifically focus on the
differential mass distribution index $s$, where the difference in the number
of particles $dN$ between masses $m$ and $m+dm$ scales with $dN\propto
m^{-s}dm$ [McKinley, 1961].

When the value of $s$ nears 2, the masses are relatively equally distributed
between all particle sizes. Values above 2 signify a population where the
total mass is dominated by smaller particles, while values below 2 signify a
population with the total mass dominated by larger particles.

From the three-channel spectrometer on Vega-2, it was found that comet
1P/Halley dust has a bulk density of
$300\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$ [Krasnopolsky et al.,
1988]. Fulle et al. [2000] used a coma model applied to 1P/Halley that
incorporated a grain size distribution and data from the Optical Probe
Experiment (OPE) and the Dust Impact Detection System (DIDSY) on Giotto to
estimate a dust bulk density of
$50\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}500\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$
(with a favoured value of around
$100\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$). This study also
indicated grain masses ranging from
$1\text{\times}{10}^{-12}\text{\,}\mathrm{kg}1\text{\times}{10}^{-3}\text{\,}\mathrm{kg}$,
with a differential mass distribution $s$ = 1.53 $\pm$ 0.1 [Fulle et al.,
2000].

More recently, Rosetta used a number of instruments to measure dust near comet
67P. Firstly, the Grain Impact Analyser and Dust Accumulator (GIADA) was used
to reveal two unique dust populations which make up the vast majority of the
collected particles [Choukroun et al., 2020]. The first population being
“fluffy” particles with extremely low densities and the second being “compact”
particles with higher densities. These values were later updated after further
data acquisition, resulting in an overall dust bulk density of
$785^{+520}_{-115}$ $\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$ [Fulle et
al., 2000]. GIADA also detected a small number of particles which belong to a
third population. These particles are thought to be aggregates of minerals,
with high densities similar to Fe sulphides
($>4000\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$), though they make up a
small percentage of the total particles detected [Fulle et al., 2017].

The Cometary Secondary Ion Mass Analyzer (COSIMA) was also used in the Rosetta
mission in order to collect cometary dust particles from 67P’s coma [Hornung
et al., 2016]. It was found that the particles collected ranged in size from
$15\text{\,}\mathrm{\SIUnitSymbolMicro
m}300\text{\,}\mathrm{\SIUnitSymbolMicro m}$, though they were mostly
comprised of the larger mass grains with an equivalent mass distribution $s$ =
1.77 $\pm$ 0.1. This work also analyzed 10 example particles that were
collected by COSIMA, their sizes ranging from
$40\text{\,}\mathrm{\SIUnitSymbolMicro
m}309\text{\,}\mathrm{\SIUnitSymbolMicro m}$, and directly estimated their
bulk densities, with results ranging from
$135\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-1}302\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-1}$.
While the mean density of these example particles was
$211.5\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$, this work suggests a
value of $160\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$ for smaller
$100\text{\,}\mathrm{\SIUnitSymbolMicro m}$-sized particles.

Additionally, the Micro-Imaging Dust Analysis System (MIDAS) experiment on
Rosetta used atomic force microscopy to observe the shape of nm to
$\mu$m-sized dust [Mannel et al., 2016]. MIDAS recorded a few thousand dust
particles whose sizes ranged from $0.04\text{\,}\mathrm{\SIUnitSymbolMicro
m}8\text{\,}\mathrm{\SIUnitSymbolMicro m}$ [Kim et al., 2023]. These particles
were dominated by the larger mass grains with equivalent mass distribution $s$
= 1.22 - 1.29, though the authors note these smaller $\mu$m size particles
were likely produced by fragmentation as opposed to released by gas pressure,
meaning they may not represent the larger particles found in the coma [Kim et
al., 2023].

Using atmospheric estimates from meteor observations, Blaauw Erskine [2019]
provides a detailed literature review containing measurements of the mass
indices of meteor showers and their relevant mass/magnitude ranges. See B for
the full table. Vojáček [2017] found similar results to that of 67P when
analyzing JFC-related meteoroids with the erosion ablation model. They found
grains $5\text{\,}\mathrm{\SIUnitSymbolMicro m}$ to $2.5\text{\,}\mathrm{mm}$
in size had a mass index $s$ = 1.8. We extracted the grain ranges and mass
indices from this work and added the values to B. In general for mm-sized
sporadic meteors, Vojáček [2017] found a grain mass range approximately from
$5\text{\times}{10}^{-12}\text{\,}\mathrm{kg}3\text{\times}{10}^{-4}\text{\,}\mathrm{kg}$
with a mean mass index $s$ = 2.0.

Mass indices have also been measured for the meteoroid population from some of
the parent comets relevant to this work. These are also summarized in B. In
general, the mass distribution of the dust in cometary trails is dominated by
larger mass particles, having a mass index $s$ = 1.67 - 2.00 [Engrand et al.,
2023].

Grain masses and their distributions can help constrain or confirm models of
comet formation and subsequently planet/solar system formation. For example,
it has been shown that the peak mass of dust ejected from 67P is between 1 cm
and 1 m, and cm-sized particles are abundant on the surface of the comet [Blum
et al., 2017]. The presence of these primordial “pebble” sized particles,
their measured mass distribution, and the predicted/measured gas emission from
67P are all consistent with comet formation expected in the pebble accretion
solar system formation model due to streaming instability followed by
gravitational collapse [Trigo-rodriguez & Blum, 2021].

##  3 Equipment and Data Reduction

All data used in this work was collected by the Canadian Automated Meteor
Observatory (CAMO) mirror tracking system. In the following section, we
provide a brief overview of this system. Further hardware, software and
analysis details can be found in Weryk et al. [2013] and Vida et al. [2021a].

###  3.1 Canadian Automated Meteor Observatory (CAMO)

CAMO is a fully-automated video meteor detection facility originally designed
for simultaneous radar and video observations. CAMO now has several optical
instrument suites, including influx cameras, EMCCD systems, spectral cameras,
and a mirror tracking system. Collectively these instruments are designed to
address specific goals, such as measuring meteoroid mass influx at Earth
[Campbell-Brown et al., 2016], providing constraints for meteor ablation
modelling [Campbell-Brown et al., 2013] and collecting simultaneous optical
meteor data to compare with radar measurements by the Canadian Meteor Orbit
Radar [Brown & Weryk, 2020].

CAMO is composed of two sites located in Ontario, approximately
$45\text{\,}\mathrm{km}$ apart in the townships of Tavistock (43.26420∘N,
80.77209∘W, $329\text{\,}\mathrm{m}$) and Elginfield (43.19279∘N, 81.31565∘W,
$324\text{\,}\mathrm{m}$). Each site has identical systems whose pointings
overlap in the atmosphere, ensuring triangulation for a large sample of
meteors.

The CAMO mirror tracking system has been operational since 2009. It is
designed to record mm-sized meteors with high-temporal and spatial precision.
When sky conditions are dark and clear at both CAMO stations, a high-speed
image-intensified wide-field camera (34∘ $\times$ 34∘ field of view, operated
at 80 frames per second) is used to quickly detect meteors (within
$0.1\text{\,}\mathrm{s}$). Once detected, a meteor track is formed based on
real-time detection over eight frames. Using positional information from this
initial track, dual-axis galvanometers position mirrors to follow the meteor
in flight. This optical scanner directs the meteor image into a telescope
equipped with an image intensifier and high-speed camera (1.5∘ $\times$ 1.5∘)
with a limiting sensitivity near +7 magnitude. This telescopic narrow-field
camera has an effective astrometric precision of 1 arc second and is operated
at 100 frames per second. This provides high-precision deceleration
measurements limited only by meteor morphology and not pixel scale [Vida et
al., 2021b]. A schematic of the CAMO wide-field and narrow-field mirror
tracking system is shown below in Figure 2 (adapted from Vida et al. [2021a]).

Figure 2: Block Diagram of the CAMO Mirror tracking system. Reproduced with
permission from [Vida et al., 2021a].

Due to its tracking capability and a higher resolution than traditional
optical systems [Koten et al., 2019], CAMO allows routine measurements of
meteor wake. More than 90% of all CAMO-tracked meteors show significant wake
[Subasinghe et al., 2016]. Therefore, the wake can be used as an additional
constraint in ablation modelling, allowing more robust estimates of model
parameters for CAMO-recorded meteors [Armitage & Campbell-Brown, 2020]. This
makes the CAMO mirror system a unique instrument as it simultaneously measures
a high-precision trajectory, dynamics, photometry, and wake. The details of
the methodology, calibration, and measurement approach used in this study are
given in C.

##  4 Modelling Methodology

###  4.1 Selection of Data

The dataset examined in this paper is comprised of 1912 CAMO-tracked meteor
events, collected between 2010-2022, from 13 different showers associated with
12 unique parent comets. Only the best 41 meteors were chosen and modelled
after being manually inspected to determine suitability for detailed data
reduction.

Suitability was based on each event’s fulfillment of multiple criteria. Only
those meteors were chosen which:

  1. 1.

Were associated with a shower where the parent body is known, shower
association was done using the data from the Global Meteor Network Vida et al.
[2021b].

  2. 2.

Were recorded entirely in the field of view for all four cameras (both the
wide and narrow-field cameras at both locations).

  3. 3.

The entirety of the meteor’s lightcurve was observed. Note that the narrow-
field camera is about 2 magnitudes more sensitive than the wide-field camera
but tracking only begins after a $\sim 0.1$ s delay [Vida et al., 2021a].

  4. 4.

Were well tracked by the system and the meteor stayed near the centre of the
frame for the majority of the event. This minimizes any geometric or
calibration errors which might occur near the edge of the frame.

  5. 5.

Did not show extreme amounts of gross fragmentation in order to allow valid
application of the erosion model. Rarely, meteors showed very complex
fragmentation and disruption into multiple large fragments. Such meteors are
hard to measure due to their morphology (e.g. overlapping fragments) which
severely reduces the astrometric accuracy [Vida et al., 2021a] and were
ignored.

  6. 6.

For showers with more than 5 well-suited events, preference was given to
events that occurred on the same day in order to save time making astrometric
plates which require manual work.

Once all of the foregoing filters were applied, 54 events were chosen for
reduction, distributed between various showers as shown in Table 3. Thirteen
of these were removed from the data set post reduction, as the improved
accuracy of their radiants/speeds precluded their original cometary shower
association, leaving 41 events to be modelled. Given the time-consuming nature
of the reduction process, we generally restricted the number of meteors
analysed from any one shower to five when an excess was available. Individual
trajectory, orbits and physical modelling results for all of these events are
summarized in D.

Shower Parent Body Total CAMO Recorded Events Events Modelled in this Work AUR
C/1911 (Kiess) 14 1 CAP 169P/NEAT 108 3 ELY C/1983 H1 (IRAS-Araki-Alcock) 13 2
ETA 1P/Halley 87 5 GEM (3200) Phaethon 166 5 JPE C/1979 Y1 (Bradfield) 21 1
LEO 55P/Tempel-Tuttle 32 1 LYR C/1861 G1 (Thatcher) 22 2 MON C/1971 F1
(Mellish) 1 1 NTA/STA 2P/Encke 517 9 PER 109P/Swift-Tuttle 407 5 ORI 1P/Halley
522 4 TAH 73P/Schwassmann–Wachmann 3 2 2 Total: 13 12 1912 41

Table 3: Data summary. Meteor showers are represented by their 3 letter IAU
code along with their respective parent bodies. Numbers of both observed and
modelled events are shown.

###  4.2 Meteor Ablation Model

The ablation model used in this work is an implementation of the Borovička et
al. [2007] erosion model. This model has successfully been used in the past to
interpret a larger set of mm and cm-sized meteors detected with video
instruments in Vojáček [2017] and Vojáček et al. [2019].

The model is based on the classical single-body equations [Ceplecha et al.,
1998],

$\frac{dv}{dt}=-Km^{-1/3}\rho_{a}v^{2}$ (1)

$\left(\frac{dm}{dt}\right)_{ablation}=-K\sigma m^{2/3}\rho_{a}v^{3}$ (2)

$I=-\tau\frac{v^{2}}{2}\left(\frac{dm}{dt}\right)_{ablation}+mv\frac{dv}{dt}$
(3)

where $m$ is the meteoroid mass, $v$ is the velocity, $t$ is the time,
$\rho_{a}$ is the air mass density, $\sigma$ is the ablation coefficient
[Borovička et al., 2007], $I$ is the luminous intensity, and $\tau$ is the
dimensionless luminous efficiency. We use the empirical $\tau$ model derived
by Vida et al. [2023a], which is based on measurements of faint meteors using
CAMO. The equations are rewritten in terms of the shape density coefficient
$K$, where $K=\Gamma A\rho_{m}^{-2/3}$, with $\Gamma$ being the drag
coefficient (assumed to be unity), $A$ being the shape factor (assumed 1.21
for a sphere) and $\rho_{m}$ being the meteoroid density.

The erosion model assumes that at a certain altitude, the meteoroid begins
quasi-continuous fragmentation where constituent
$\text{\,}\mathrm{\SIUnitSymbolMicro m}$ grains are gradually released, each
ablating independently [Borovička et al., 2007]. This process is called
erosion and continues until all of the meteoroid mass has been consumed.
Treating ablation as an atom-by-atom mass loss and the erosion as mass loss
through grain release, we can rewrite the total mass loss equation as a
combination of both ablation and erosion [Vida et al., 2023b]:

$\frac{dm}{dt}=\left(\frac{dm}{dt}\right)_{ablation}+\left(\frac{dm}{dt}\right)_{erosion}$
(4)

where mass loss due to erosion is described by the erosion coefficient $\eta$,
analogous to the ablation coefficient such that:

$\left(\frac{dm}{dt}\right)_{erosion}=-K\eta m^{2/3}\rho_{a}v^{3}$ (5)

The masses of grains released in erosion are distributed according to a power-
law distribution with a mass index $s$ between the minimum and maximum masses
$m_{l}$ and $m_{u}$. The grains have a fixed density at
$\rho_{\text{grain}}=$$3500\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$,
implying that no meteoroid can have a bulk density larger than this grain
density, as the porosity cannot be higher than 100%. We wanted to ensure the
meteoroids from our highest density shower (the Geminids) were well within
this upper limit to not bias results. We note that the grain density cannot be
measured from the model as identical fits can be obtained with different
values of grain density Borovička et al. [2007]. A grain density of
$3000\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$ was assumed in [Borovička
et al., 2007] when modelling Draconid meteors, however we set this value to
$3500\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$, appropriate for
refractory silicate grains and as this is closer to the grain density measured
in most meteorites [Flynn et al., 2018]. In this model, we also implicitly
assume that the porosity of the meteoroid is simply the ratio of the bulk and
grain densities.

The outputs of the model are the dynamics (velocity) and light curve over time
and height. The model provides two reference points for the dynamics: the
point of maximum brightness on the trail and the position of the leading
fragment at each time step [Borovička et al., 2007]. A reference point is
chosen depending on the meteor’s morphology and the way observations were
reduced (e.g. if the leading fragment was resolved and picked in the video,
then the leading fragment position provided by the model is used for fitting).
The light curve at each time step is computed by summing the total luminous
intensity (Eq. 3) of all ablating fragments from which the magnitude is
computed as:

$M=-2.5\log_{10}\frac{\sum I}{P_{0M}}\,,$ (6)

where $P_{0M}$ is the power of a zero-magnitude meteor. A value of
$P_{0M}=$840\text{\,}\mathrm{W}$$ was used, as appropriate for meteors in
CAMO’s spectral bandpass [Weryk & Brown, 2013].

The erosion model provides improved fits for both a meteor’s measured
deceleration and lightcurve when compared to previous models which instead use
sudden disruption of the simulated meteoroid [Borovička et al., 2007]. For a
more detailed explanation of all equations and parameter definitions used in
the erosion model, the reader is referred to Popova et al. [2019] and Vida et
al. [2023b].

For our modelling, a number of parameters are defined from the observed
trajectory and are well known, while others are assumed. The remaining
variables are the parameters we are trying to estimate based on the model fit
to observations. Table 4 summarizes all model quantities and their adopted
values where appropriate. While the uniqueness of model fits is not
necessarily implied, it was found that constraining the model with meteor
photometry, dynamics and wake almost always produced physical parameters
limited to a narrow range.

Parameter | Description | Type | Value/Range | Units
---|---|---|---|---
A | Shape Factor | Constant | 1.21 | -
$\Gamma$ | Drag Coefficient | Constant | 1 | -
$v_{0}$ | Initial Velocity | Constant | Measured per Event | $\text{\,}\mathrm{km}\text{\,}{\mathrm{s}}^{-1}$
$Z_{\text{c}}$ | Zenith Angle | Constant | Measured per Event | deg
$\tau$ | Luminous Efficiency | Model | Vida et al. [2023a] | -
PSF $\sigma$ | Point Spread Function | Constant | 3 | m
$\rho_{\text{grain}}$ | Grain Density | Constant | 3500 | $\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$
$\rho$ | Bulk Density | Tuned |  $\leq$ 3500 | $\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$
$m_{0}$ | Initial Mass | Tuned | - | kg
$\sigma$ | Ablation Coefficient | Tuned | - | $\text{\,}\mathrm{kg}\text{\,}{\mathrm{MJ}}^{-1}$
$h_{e}$ | Erosion Start Height | Tuned | - | km
$\eta$ | Erosion Coefficient | Tuned | - | $\text{\,}\mathrm{kg}\text{\,}{\mathrm{MJ}}^{-1}$
$m_{u}$ | Max. Grain Mass | Tuned | - | kg
$m_{l}$ | Min. Grain Mass | Tuned | - | kg
$s$ | Grain Mass Index | Tuned | 1.3-2.8 | -
Table 4: Parameters used in our ablation model. The constants were not changed
during the modelling process as these were either measured with high precision
or assumed. The tuned parameters were adjusted in order to fit the simulated
meteor to the observed data points. The luminous efficiency $\tau$ was sampled
from the empirical model of Vida et al. [2023a].

To reconstruct the wake we assumed a Gaussian point spread function (PSF). For
most events, we used an instrument-measured value of the standard deviation of
the PSF of $3\text{\,}\mathrm{m}$. However, we discovered that during the week
in 2021 when the 5 Eta-Aquariid events were recorded, the narrow field camera
was slightly out of focus. As a result, a PSF of $10\text{\,}\mathrm{m}$ was
used when modelling only these events as this value was required in order to
properly fit the observed wakes and have them match at both sites.

Finally, while initial velocity is measured for each event based on the
initial trajectory solution, in practice we do slightly adjust this value in
order to fit the observed lag. We define the lag as the distance the meteoroid
falls behind an object of constant velocity equal to the meteoroid’s initial
velocity [Subasinghe et al., 2017], effectively only showing the deceleration
of the meteoroid. The velocity usually must be increased slightly as the
narrow-field camera tracking is delayed and the meteor starts to decelerate by
the time the camera locks on. An improved orbit is computed once an initial
velocity is fit in the model.

###  4.3 Model Fitting Process

The model fitting process was performed manually and iteratively, using a
graphical user interface (GUI) that displays plots of the observed lightcurve,
deceleration and wake overlaid with simulated meteor values. The GUI allows
for a more intuitive user experience during the fitting process as all plots
can be visualized at once and in real time. The GUI also contains adjustable
meteoroid and erosion parameters for the simulated meteoroid and is shown in
Figure 3. The full details of the fitting procedure are given in Vida et al.
[2023a]. Manual fitting proceeds in a trial-and-error fashion until a set of
parameters is reached that simultaneously fits all of the observed data, as
best as the analyst feels is possible, across all observed heights. At this
point, these physical parameters are adopted as being representative of the
observed meteoroid within the model assumptions.

As a check, multiple human analysts performed independent fits. In general,
most of our modelled events were fit by at least three analysts and in some
cases as many as five.

Figure 3: An example event showing the usage of the ablation model GUI in
comparing observations (colored points) to the simulated meteor fit (solid
black line). Individual numbered subplots represent: 1) Lightcurve plot 2)
Velocity plot 3) Lag (Deceleration) plot 4) Lag residuals 5) Wake plot at a
specific altitude 6) Wake overview, providing a broad summary of the wake at
all heights 7) Meteoroid and erosion parameters 8) Wake settings, allowing for
height navigation of the wake plot

As the initial fits are done subjectively by analysts, the question remains,
what exactly constitutes an “accurate” fit? For the lag, the goodness of fit
is usually easy to define as the residuals are shown in the GUI itself (label
4 in Figure 3). From experience, we found that the upper limit for acceptable
lag residuals is around $20\text{\,}\mathrm{m}$ for any individual data point,
indicating that the along-the-track residuals are on that order while position
picks transverse to the trajectory are usually below a meter. The lag for an
example event is shown in Figure 4, with both a good and bad fit shown for
comparison.

Figure 4: Left: An example of a poor lag fit. The model fit begins to deviate
from observations below $90\text{\,}\mathrm{km}$ height, with the model
meteoroid decelerating much faster than the observed meteoroid. The lag
residuals are large, increasing up to of order $100\text{\,}\mathrm{m}$ and
show a systematic trend. Right: An example of a good lag fit. Here
deceleration is modelled well, agreeing with the observed meteor for the
entire event. Residuals are small, on the order of
$2\text{\,}\mathrm{m}3\text{\,}\mathrm{m}$ and show random scatter about the
vertical (0) line.

The corresponding lightcurve for this example event is shown in Figure 5. The
lightcurve contains data from four separate instruments, so photometric data
from each station did not always perfectly agree at all heights due to
differences in local observing conditions, thus showing the magnitude
residuals is less useful. The variance between lightcurves from the four
cameras was normally less than 0.3 magnitudes. Therefore, the simulated meteor
was fit to the lightcurve(s) with the best quality data, where observing
conditions at each station were taken into account by the analyst to decide
which lightcurve was the most physical. Analysts preferred stations with clear
skies that also accurately tracked the meteor for the entire duration of the
event.

Figure 5: Left: An example of a poor lightcurve fit. The magnitude is
underestimated at the start of the event and overestimated for the peak
magnitude until the end of the event. The lightcurve shape is clearly
inaccurate. Right: An example of a good lightcurve fit. The magnitude is
modelled well throughout the entire event and the lightcurve shape agrees
within the spread of observed data.

Deciding on the goodness of fit for the wake is more challenging as both wake
intensity and shape are simulated via the release of hundreds of grains in
many mass bins for all heights. The wake is computed as the integrated
brightness of all grains (with the PSF applied) perpendicular to the
trajectory in predefined steps, resulting in a curve of brightness as a
function of distance behind the meteoroid. The uncalibrated observed wake is
then scaled to the model wake by normalizing the total area under the model
wake curve to that of the observed curve.

Attempts to quantify the quality of the wake fit were found to be too
computationally intensive as computing the wake adds significant computational
overhead. Thus, each fit was first only performed on the light curve and the
lag, and after an initial fit was obtained, the wake was consulted to improve
the fit. Consequently, the goodness of fit of the wake was more subjective
than for the lightcurve or the dynamics. The wake at a single height for an
example event is shown in Figure 6, with both a good and bad fit shown for
comparison.

As the minimum/maximum grain sizes and the gain mass distribution have a large
impact on the wake but a much smaller impact on the lightcurve and lag, our
approach was to first get a rough estimate for the key meteoroid parameters
(e.g. bulk density, initial mass, ablation coefficient) using the lightcurve
and lag as guides, before introducing changes to the grain properties and
fitting the wake. Preference was given to fits which agreed with the
shape/distribution of the entire observed wake from both sites, as opposed to
only agreeing with the intensity of the light coming from near the head of the
meteor, as the grains were the main parameters of focus in this final stage of
fitting.

Figure 6: Top: An example of a poor wake fit (at $90.06\text{\,}\mathrm{km}$
height). The modelled peak intensity of the meteoroid is greatly
underestimated and the intensity of the wake behind the meteoroid is
overestimated. The overall wake shape does not agree with the observed meteor.
Bottom: An example of a good wake fit. Here the modelled peak intensity of the
meteoroid matches observations and the intensity of the wake is accurate at
all distances behind the meteoroid.

While model fits were always preferred if they matched observations well at
all heights, for some events this was not possible. For these more difficult
events, preference was given to fits that matched the observed data earlier in
the event, but did not match so well near the end of the event. This is
because we are mainly interested in the bulk of the initial meteoroid, which
by the end of an event, has usually been ablated, eroded and/or fragmented
leaving an unrepresentative refractory portion of the actual meteoroid
remaining. Such partial fits also reflect the relative simplicity of our model
in comparison to the complexity of some meteoroid fragmentation. We postulate
that in those cases where the wake could only be fit at one portion of the
trajectory the grain distribution changed over time, which was not modelled.

The one added complexity we allowed in the model was to permit an analyst to
choose one change in the erosion coefficient, ablation coefficient and/or bulk
density at one certain height during the meteor flight in order to improve the
model fit. This decision was informed by the observed changes in the
lightcurve of some meteors (e.g. those with double-peaked lightcurves).
However, as this introduces more complication into the model, fits were
preferred which contained as few changes as possible; we only resorted to such
a change when absolutely necessary. If two fits were similar, the first having
a change in bulk density and erosion coefficient, while the second only had an
erosion change, the second fit was preferred.

For each event, the best manual fit among those completed independently by
human analysts was chosen as a starting point for an automated refinement step
described in detail in Vida et al. [2023a]. This algorithm concentrates on the
local space surrounding the hyper dimensional point defined by the analyst-
determined parameters and minimizes a cost function determined by the light
curve and lag. In most cases this refinement step produced only a modest
change from the initial parameters; and was adopted only if the resulting
overall fit, and especially the wake, was fit as good or better than the
initial solution.

##  5 Results and Discussion

Our final dataset is comprised of 41 modelled meteors, three of which are
presented as example fits in Figure 7. All meteoroid orbits are given in Table
LABEL:tab:orbits, and their physical properties and erosion parameters are
given in D. We summarize all of our average parameter results found for each
shower in Table 5. Key findings are then shown in the graphs that follow,
where solid black error bars represent the estimated uncertainty that we have
assigned, namely the standard error of the mean. This is meant to give a sense
of the dispersion about the mean for each shower. Graphs showing the
comparison between observations and the model for each event, including the
wake across a range of heights, are given in Supplementary Materials.

Figure 7: An Alpha-Capricornid (top), Eta-Aquariid (middle) and Geminid (bottom) are shown as examples of the final model fits. Lightcurves, lags and two snapshots of the wake are shown for each event. The solid black line represents the simulated meteor while the colour data points are measured values from the observed meteor. Figure 8: Continued. Shower | $\rho$ | $m_{0}$ | $V_{0}$ | $\sigma$ | $h_{e}$ | $\eta$ | $h_{e2}$ | $\eta_{2}$ | $s$ | $m_{l}$ | $m_{u}$ | $E_{S}$ | $E_{V}$
---|---|---|---|---|---|---|---|---|---|---|---|---|---
| ($\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$) | (kg) | ($\text{\,}\mathrm{km}\text{\,}{\mathrm{s}}^{-1}$) | ($\text{\,}\mathrm{kg}\text{\,}{\mathrm{MJ}}^{-1}$) | (km) | ($\text{\,}\mathrm{kg}\text{\,}{\mathrm{MJ}}^{-1}$) | (km) | ($\text{\,}\mathrm{kg}\text{\,}{\mathrm{MJ}}^{-1}$) |  | (kg) | (kg) | $\text{\,}\mathrm{MJ}\text{\,}{\mathrm{m}}^{-2}$ | $\text{\,}\mathrm{MJ}\text{\,}{\mathrm{kg}}^{-1}$
CAP | 830 | 9.33E-06 | 24.755 | 0.017 | 99.505 | 0.197 | 93.147 | 0.232 | 2.46 | 7.53E-11 | 3.05E-08 | 1.50 | 1.09
|  $\pm$187 |  $\pm$2.69E-06 |  $\pm$0.376 |  $\pm$0.004 |  $\pm$0.313 |  $\pm$0.047 |  $\pm$1.553 |  $\pm$0.168 |  $\pm$0.12 |  $\pm$2.27E-11 |  $\pm$3.01E-08 |  $\pm$0.21 |  $\pm$0.26
TAH | 260 | 7.20E-05 | 16.355 | 0.020 | 92.463 | 0.091 | 88.150 | 0.410 | 2.39 | 3.47E-11 | 5.37E-10 | 1.60 | 1.22
|  $\pm$33 |  $\pm$3.20E-05 |  $\pm$0.068 |  $\pm$0.004 |  $\pm$1.581 |  $\pm$0.033 |  $\pm$0.350 |  $\pm$0.290 |  $\pm$0.32 |  $\pm$2.26E-11 |  $\pm$3.84E-10 |  $\pm$0.46 |  $\pm$0.44
ETA | 305 | 1.20E-05 | 66.740 | 0.025 | 114.738 | 0.326 | 107.151 | 0.244 | 1.69 | 6.49E-11 | 4.68E-10 | 2.78 | 3.56
|  $\pm$55 |  $\pm$1.75E-06 |  $\pm$0.275 |  $\pm$0.001 |  $\pm$0.617 |  $\pm$0.081 | - | - |  $\pm$0.27 |  $\pm$2.63E-11 |  $\pm$8.57E-11 |  $\pm$0.16 |  $\pm$0.48
LEO | 379 | 4.76E-06 | 72.106 | 0.029 | 115.739 | 1.035 | - | - | 2.21 | 8.89E-12 | 9.00E-09 | 1.00 | 1.37
| - | - | - | - | - | - | - | - | - | - | - | - | -
MON | 668 | 1.07E-05 | 42.941 | 0.019 | 113.460 | 0.039 | 90.162 | 0.006 | 2.01 | 1.03E-11 | 4.74E-09 | 0.47 | 0.34
| - | - | - | - | - | - | - | - | - | - | - | - | -
ORI | 282 | 1.18E-05 | 67.671 | 0.027 | 116.151 | 0.229 | 108.081 | 0.362 | 2.14 | 8.73E-12 | 1.70E-08 | 1.03 | 1.48
|  $\pm$52 |  $\pm$5.26E-06 |  $\pm$0.509 |  $\pm$0.003 |  $\pm$1.141 |  $\pm$0.035 |  $\pm$2.719 |  $\pm$0.205 |  $\pm$0.12 |  $\pm$1.24E-12 |  $\pm$1.10E-08 |  $\pm$0.15 |  $\pm$0.34
PER | 365 | 5.55E-06 | 59.875 | 0.027 | 111.205 | 0.340 | 104.469 | 0.079 | 2.16 | 5.24E-11 | 5.88E-08 | 1.33 | 2.22
|  $\pm$134 |  $\pm$8.58E-07 |  $\pm$0.622 |  $\pm$0.001 |  $\pm$0.512 |  $\pm$0.070 |  $\pm$0.334 |  $\pm$0.043 |  $\pm$0.21 |  $\pm$4.39E-11 |  $\pm$4.10E-08 |  $\pm$0.14 |  $\pm$0.39
AUR | 412 | 1.07E-05 | 65.490 | 0.021 | 111.757 | 0.144 | - | - | 2.35 | 2.87E-12 | 3.31E-07 | 1.51 | 1.50
| - | - | - | - | - | - | - | - | - | - | - | - | -
ELY | 860 | 2.30E-06 | 46.229 | 0.028 | 107.016 | 0.385 | - | - | 2.38 | 9.93E-12 | 7.02E-08 | 1.52 | 1.55
|  $\pm$30 |  $\pm$1.75E-07 |  $\pm$0.065 |  $\pm$0.002 |  $\pm$0.486 |  $\pm$0.045 | - | - |  $\pm$0.02 |  $\pm$6.50E-14 |  $\pm$2.98E-08 |  $\pm$0.32 |  $\pm$0.33
JPE | 284 | 1.21E-05 | 65.684 | 0.021 | 114.102 | 0.159 | 99.690 | 0.010 | 2.28 | 9.49E-12 | 2.13E-09 | 1.12 | 1.37
| - | - | - | - | - | - | - | - | - | - | - | - | -
LYR | 438 | 1.02E-05 | 48.547 | 0.035 | 110.716 | 0.146 | 100.091 | 0.015 | 2.48 | 1.02E-10 | 5.08E-09 | 1.03 | 1.41
|  $\pm$36 |  $\pm$7.73E-06 |  $\pm$0.045 |  $\pm$0.024 |  $\pm$4.699 |  $\pm$0.052 | - | - |  $\pm$0.06 |  $\pm$1.00E-10 |  $\pm$5.02E-09 |  $\pm$0.38 |  $\pm$0.91
TAU | 647 | 8.72E-06 | 29.773 | 0.027 | 104.797 | 0.062 | 90.490 | 0.223 | 2.11 | 2.39E-11 | 7.54E-08 | 1.00 | 0.87
|  $\pm$58 |  $\pm$2.24E-06 |  $\pm$0.625 |  $\pm$0.002 |  $\pm$1.141 |  $\pm$0.002 |  $\pm$2.131 |  $\pm$0.066 |  $\pm$0.13 |  $\pm$1.15E-11 |  $\pm$2.30E-08 |  $\pm$0.15 |  $\pm$0.13
GEM | 1387 | 6.82E-06 | 35.971 | 0.028 | 101.372 | 0.171 | 93.452 | 0.437 | 1.85 | 3.57E-10 | 1.72E-07 | 2.08 | 1.38
|  $\pm$240 |  $\pm$3.35E-06 |  $\pm$0.172 |  $\pm$0.002 |  $\pm$1.914 |  $\pm$0.037 |  $\pm$0.773 |  $\pm$0.205 |  $\pm$0.20 |  $\pm$1.86E-10 |  $\pm$9.39E-08 |  $\pm$0.45 |  $\pm$0.38
Table 5: Average model results for all analyzed showers. Average values are
shown with the standard error of the mean as uncertainty. $\eta_{2}$
represents the second erosion coefficient that was found after an erosion
change at height $h_{e2}$. $E_{V}$ represents the energy per unit mass needed
to begin erosion and $E_{S}$ is the energy per unit cross section needed to
begin erosion. A dash - is shown for parameters where there was only one value
or none, so the standard error of the mean could not be calculated.

###  5.1 General characteristics of observed meteors

Figure 9: The observed begin height (km) as a function of initial velocity
($\text{\,}\mathrm{km}\text{\,}{\mathrm{s}}^{-1}$). Events are colour coded by
shower.

Figure 9 shows the expected trend of higher velocity events appearing at
higher altitudes, a well-known result [Vida et al., 2018, Borovička et al.,
2019]. Variations in begin height by shower may reflect material, structural
or strength differences [Koten et al., 2004]. Our studied showers cover a
large range of regular meteor velocities and begin heights. For our shower
meteors, the one notable deviation from the general pattern is for the
Geminids which penetrate deeper, consistent with their stronger nature.

The Tau-Herculids were our slowest ($\sim$16
$\text{\,}\mathrm{km}\text{\,}{\mathrm{s}}^{-1}$) and lowest beginning shower
($\sim$91\text{\,}\mathrm{km}$$) followed by the Alpha-Capricornids, the
Taurids and then the Geminids with increasing velocities and begin heights as
expected. The single Leonid, the Orionids, the Eta Aquarids and the single
July Pegasid were our fastest
($>$65\text{\,}\mathrm{km}\text{\,}{\mathrm{s}}^{-1}$$) and highest beginning
showers ($>$109\text{\,}\mathrm{km}$$).

Figure 10: Average meteoroid initial mass (kg) for each analyzed shower. The
showers are colour coded according to their parent comet orbital population
following the classification of Levison [1996].

To provide a sense of the meteoroid mass range covered in our reductions,
Figure 10 presents the average meteoroid mass and standard error of the mean
per shower. The Eta Lyrids were the lowest mass events, averaging
$\sim$2.3\text{\times}{10}^{-6}\text{\,}\mathrm{kg}$$ while on the other end
of the spectrum, the Tau-Herculids were the highest mass events (simply due to
their low speed), averaging
$\sim$7\text{\times}{10}^{-5}\text{\,}\mathrm{kg}$$. Most showers had fairly
similar average masses in the range of 5 - 11 $\times$ 10-6 kg. Our survey
covers a little more than one order of magnitude in mass, with a
representative average being near
$\sim$1\text{\times}{10}^{-5}\text{\,}\mathrm{kg}$$. Meteoroids in this size
range provide a unique area of study, as particles of this size are too large
for dust detectors or infrared emission analysis, yet too small to be observed
optically from Earth while they are still in space [Campbell-Brown & Koschny,
2004].

Figure 11: Average meteoroid bulk density
($\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$) for each observed shower.
The showers are colour coded according to their parent comet orbital
population following the classification of Levison [1996]. The red dashed bars
represent the range of previously measured literature values - see Table 2.
(GEM maximum literature value is 3500
$\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$).

The average meteoroid bulk density for each shower is shown in Figure 11.
These values are compared to previously measured shower bulk densities from
the literature in Table 2.

Our typical values for meteoroids from cometary meteoroid streams show
densities in the
$300\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}1000\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$
range, consistent with Rosetta measurements of bulk density as well as the
bulk density of cometary nuclei in general. These densities are also
consistent with group C and D material from Ceplecha [1988] meteor classes, as
one would expect. Overall the Tau-Herculids were the lowest average density
shower ($259\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$) and the Geminids
were the highest average density shower
($1387\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$) and our only shower
consistent with group B material from Ceplecha [1988] meteor classes.

Of the showers that have previously published densities in the literature (the
Orionids, Perseids, Leonids, Lyrids, Taurids, Alpha-Capricornids and Geminids)
our densities are in agreement within uncertainty, though usually on the lower
end of the literature range.

###  5.2 Grain mass distribution

Figure 12: The average differential grain mass index $s$ for each analyzed
shower. The showers are colour coded according to their parent comet orbital
population following the classification of Levison [1996].

One outcome of our model fits is that we also determine the most likely
differential mass distribution of constituent grains. Figure 12 shows the
average differential grain mass distribution index $s$ for each shower. The
vast majority of our analyzed showers were dominated by small grains ($s$ >
2), the extreme being the Lyrids ($s$ = 2.48 $\pm$ 0.06), though the number
statistics are small. This is the opposite trend to what is seen in looking at
the larger individual meteoroid mass distribution within streams (see B) which
are almost entirely large particle dominated with $s$ < 2.

We found Halley-type showers have on average more large mass grains compared
to the Jupiter-family and external showers which tend to have more small mass
grains. However, we caution that uncertainties are quite large for some
showers due to outlier events with very high $s$ values. Furthermore, for some
showers, only one event was modelled so the standard error of the mean was
indeterminable. A larger sample size would be needed to confirm the veracity
of this trend.

We found only two showers whose meteoroids are preferably composed of larger
grains: the Eta-Aquariids ($s$ = 1.69 $\pm$0.27) and the Geminids ($s$ = 1.85
$\pm$0.20). Both of these shower’s parent bodies, (3200) Phaethon and
1P/Halley, also have mass index values for larger meteoroids with an $s$ < 2\.
The other daughter shower of 1P/Halley, the Orionids, was also found in past
literature to have $s$ < 2\. However, the Orionids were found in this work to
have an average $s$ = 2.14 $\pm$ 0.10. Given the large uncertainties and small
number statistics, these differences may not be significant.

Looking at in situ $s$ measurements for the mass distribution of smaller
grains, we see mixed results when compared to the literature. The dust from
67P (a comet on a JFC orbit) seems to certainly be dominated by larger size
grains ($s$ between 1.22 - 1.77 for grains between 0.04 and
$300\text{\,}\mathrm{\SIUnitSymbolMicro m}$), depending on the instrument and
the population of dust studied. As noted by Ellerbroek et al. [2019], more
than half of the actual dust that impacted any of the sensor instruments was
actually lost on impact, but the general trend of larger grains seems robust.

Vojáček [2017] found an $s$ similar to 67P for JFC meteoroid grain fits.
However, our work finds the two JFC showers we looked at do not agree with the
results from 67P, as these showers had two of the highest $s$ values of all
examined showers. Specifically, we found the Tau-Herculids had an average $s$
= 2.39 $\pm$ 0.32 and the Alpha-Capricornids had an $s$ = 2.46 $\pm$ 0.12. As
summarized in B, these showers’ parent comets 73P/Schwassmann-Wachmann and
169P/NEAT have been found to produce meteoroids which at Earth have
$s\lesssim$ 2 [Dubietis & Arlt, 2004, Egal et al., 2023].

Overall, it seems clear the Geminids and likely the Eta-Aquariids are large
grain dominated, while the Lyrids are probably small grain dominated. For the
other showers as well as comet populations as a whole, the mass index $s$ may
not be simple to constrain. The disagreement between our shower results and
the literature shows that grain mass index and the differential mass index
$s$, governing the distribution of shower meteoroids at masses orders of
magnitudes larger, are likely distinct.

###  5.3 Energy needed to trigger erosion

Figure 13: Top: The average energy per unit cross section
($\text{\,}\mathrm{MJ}\text{\,}{\mathrm{m}}^{-2}$) required to begin erosion
for each shower ($E_{S}$). Bottom: The average energy per unit mass
($\text{\,}\mathrm{MJ}\text{\,}{\mathrm{kg}}^{-1}$) required to begin erosion
for each shower ($E_{V}$). For both insets, the showers are colour coded
according to their parent comet orbital population following the
classification of Levison [1996].

Figure 13 shows a comparison of the average energy per unit mass and per unit
cross section needed to trigger erosion [Borovička et al., 2007]. Most showers
seem to require similar amounts of energy within uncertainty, between 1 and
$2\text{\,}\mathrm{MJ}$ per unit mass or cross section, to begin the erosion
process. This is interesting as the bulk density of the shower appears
uncorrelated with erosion energy. For example, the Tau-Herculids, our lowest
density shower with weak material, was found to have near identical erosion
energies to the Geminids, our highest density shower.

Our shower with the lowest erosion energy was the single Monocerotid event,
which required 2 to 3 times less energy compared to the other showers. This is
likely because of the large difference in erosion start height and observed
begin height ($\Delta h$) of $\sim$10 km for this event, leading to an
underestimation of erosion energy. We thus do not consider this value robust.
The $\Delta h$ of this event was the highest of all modelled events, leading
to an unusually high erosion start height, decreased atmospheric density and
therefore decreased calculated erosion energy.

The Eta-Aquariids were found to have the highest erosion energy in both insets
of Figure 13, despite being one of the lowest density showers. They also
require about 2.5$\times$ more energy to start erosion than the Orionids,
despite originating from the same parent body. A likely reason for this
behaviour is that the Eta-Aquariids have the shallowest entry angle of all our
analyzed showers ($\sim 20^{\circ}$), as the shower radiant is at a relatively
southern latitude. Thus the Eta-Aquariids observed by CAMO in Canada had to
pass through a lot of atmosphere before becoming visible. If the Orionids and
Eta-Aquariids really do have similar physical properties, then this finding
indicates that a better figure of merit when investigating the erosion
threshold would be the instantaneous energy, instead of the total energy
received by the meteoroid before the start of erosion.

This difference with the Orionids could mean that the concept of erosion
energy is less significant to erosion onset than the dynamic pressure which
scales with erosion start height; both the Orionids and Eta Aquriids have
similar erosion start heights. Alternatively, it is possible that the younger
age of the shower [Egal et al., 2020] compared to the Orionids could result in
higher binding energy; however without reliable age estimates of each shower
meteoroid, it is unclear if this is plausible.

The exact mechanism that triggers erosion is still unclear. It could be due to
the penetration of heat through the surface of the meteoroid, with volatiles
causing refractory grains to be ejected and/or ablation of grain bonds. In
this case, we expect grain release to occur at nearly constant erosion
energies. Alternatively, if it is due to a continuous mechanical failure of
the surface layer as the dynamic pressure on the meteoroid increases, grain
release would occur at near-constant dynamic pressures. The fact that most of
our showers show similar average erosion energies argues for the former
interpretation, while the disparity between the Orinids and the Eta-Aquariids
argues for the latter.

Hulfeld et al. [2021] used direct simulation Monte Carlo (DSMC) to model the
fragmentation and ablation of a Draconid meteoroid and used parameters which
were determined based on the erosion model to kick-start the simulation. They
found that they can explain the fragmentation purely mechanically (i.e. driven
by the dynamic pressure), however more spectral observations are needed to
determine the role of volatiles during erosion.

###  5.4 Ablation and erosion in relation to grain size

Figure 14: Top: Erosion coefficient $\eta$
($\text{\,}\mathrm{kg}\text{\,}{\mathrm{MJ}}^{-1}$) for each event as a
function of maximum grain size (kg). Bottom: Ablation coefficient $\sigma$
($\text{\,}\mathrm{kg}\text{\,}{\mathrm{MJ}}^{-1}$) for each event as a
function of maximum grain size (kg). For both plots, events are colour coded
by shower. Figure 15: The ratio of the erosion coefficient $\eta$ to the
ablation coefficient $\sigma$ as a function of maximum grain size (kg). Events
are colour coded by shower.

Removal of grains during ablation is characterized by the erosion coefficient
which indicates how easily grains can be removed from the main meteoroid and
is therefore a structural indicator. The ablation coefficient is an indicator
of the ease of removal of atoms and is linked to the material properties of
the meteoroid.

As shown in the top inset of Figure 14 the erosion coefficient $\eta$ is
independent of the grain upper mass limit; the same is true for the minimum
grain mass. It also is independent of the differential grain mass index. This
suggests that the constituent grain size plays little role in erosion.

The bottom inset of Figure 14 shows that ablation coefficient $\sigma$ is at
most very weakly positively correlated with the grain upper mass limit as
suggested by Vojáček [2017], though our results are consistent with no trend
as well.

The ratio of $\eta$/$\sigma$ is given in Figure 15, and shows a very slight
downward trend, driven by the increasing ablation coefficient. However, we do
not find this trend to be statistically significant, as linear regression
results in R2 = 0.05. This means that only 5$\%$ of the variance in the ratio
of $\eta$/$\sigma$ can be predicted or explained by the maximum grain size;
much less significant than what was originally found by Vojáček [2017].

These results suggest that it does not appear to be more difficult to release
large grains from a meteoroid, but it might be slightly harder to vaporize or
ablate large grains. This makes sense physically as the release of grains is
determined more by the breakdown of the meteoroid matrix (which we presume
remains homogenous and independent of grain size) and not the breakdown of
grains themselves. The ablation of a meteoroid consists of the vaporisation of
the entire body and its material, including the grains. Thus it is sensible to
imagine that larger grains are more difficult to vaporise.

###  5.5 Changes in $\eta$ and $\sigma$

Overall we find that a majority of events (56%) required a change in erosion
coefficient while (10%) required a change in ablation coefficient in order to
be accurately modelled. It was found that most showers contained meteoroids
which were physically concordant in terms of whether their meteoroids required
an erosion change. For example, the Alpha-Capricornids, Tau-Herculids,
Geminids, Orionids, and Perseids required erosion changes for between 67% -
100% of shower meteors, and all events which required ablation changes were
contained within these showers. Material belonging to these stated showers
appears to be somewhat heterogeneous as they seem more complex than one would
likely expect for mm-sized meteoroids, a similar conclusion as was reached by
Vojáček et al. [2019].

In contrast, the Southern Taurids, Eta-Lyrids and Eta-Aquarids required
erosion changes for only 0% - 29% of analyzed events, suggestive of less
complex, homogeneous meteoroids. This consistency in ablation behaviour within
a shower was also seen by Vojáček [2017] where the majority of the Draconids
and the Taurids they analyzed required a second stage of fragmentation. While
they find an opposite trend for the Taurids than we do, it is likely explained
by the fact that they observed Taurids orders of magnitude more massive than
in our study, and as a result are more likely to contain complex structures.

For the remaining showers, the Northern Taurids, Lyrids, Leonids, Aurigids,
Monocerotids and July Pegasids, only one or two events were modelled, so we
are unable to say whether they have a more complex structure at mm-sizes due
to small number statistics.

Our results suggest that for some showers, meteoroids are fairly homogeneous,
made up of very similar material. However, other showers consist of meteoroids
that are complex, likely containing multiple materials of varying mechanical
strengths, such as was found for the Taurids by Borovička & Spurný [2020]. It
is possible that showers which usually require a change in erosion or ablation
could have some physical differences between their outer and the inner
meteoroid material as suggested by Vojáček [2017]. This result is similar to
what Blum et al. [2022] suggests for comets themselves at larger scales.

###  5.6 Dispersion of Physical Parameters

Table 5 shows the average parameter values we found for each analyzed shower,
as well as the standard error of the mean as an indicator of the level of
heterogeneity of the shower material properties.

Vojáček [2017] found that the dispersion of some parameters, namely the
ablation and erosion coefficients increased as meteoroid mass increased. We do
not see this same trend as both our $\sigma$ and $\eta$ values did not vary
much for events within the same shower, regardless of the analyzed meteoroid
masses. The most probable reason we do not see this trend is that our
measurements cover a very narrow range of masses between
$1\text{\times}{10}^{-6}\text{\,}\mathrm{kg}1\text{\times}{10}^{-4}\text{\,}\mathrm{kg}$,
while Vojáček [2017] observed meteoroids with masses spanning over 5 orders of
magnitude.

The erosion start height and erosion change height seem to be the parameters
with the least dispersion, showing little variability between shower events (<
2%). This points to similar structural properties between meteoroids of the
same shower. Erosion start height is directly related to erosion energy as the
atmosphere becomes more dense with decreasing altitude, causing more particles
of air to collide with the meteoroid and transfer their energy [Popova et al.,
2019]. Hence if all meteoroids from a shower begin erosion at the same height,
they likely require very similar erosion energies as well, and therefore have
similar bulk strengths. This is confirmed in looking at Figure 13 where the
spread in values about the mean is relatively small for the majority of
showers. This result further supports the concept of relative homogeneity
between meteoroids in the same shower at mm-sizes.

In absolute terms, the erosion energy is not significantly different for most
showers, also suggesting commonality in the underlying meteoroid structure.
The main exception are the Geminids, known to be structurally strong and
likely distinct from cometary streams.

The bulk density, ablation coefficient, first and second erosion coefficient,
and mass index vary from the average by about 35% for our showers. These are
the key parameters that define the physical makeup of a meteoroid, again
reflecting the relative homogeneity of the material in the same shower. The
absolute value of $\sigma$ was relatively similar for all showers, ranging
between
$0.020\text{\,}\mathrm{kg}\text{\,}{\mathrm{MJ}}^{-1}0.031\text{\,}\mathrm{kg}\text{\,}{\mathrm{MJ}}^{-1}$,
a value consistent with cometary material [Ceplecha et al., 1998] and
suggesting that the material properties of shower meteoroids are even more
similar than their structural strength.

The minimum and maximum grain size were the parameters we found with the
highest scatter, showing differences of 95% relative to the mean. This is an
interesting finding as the mass distribution of the grains ($s$) remained
fairly consistent between events of the same shower. The range of the minimum
grain masses was between
$9\text{\times}{10}^{-12}\text{\,}\mathrm{kg}3\text{\times}{10}^{-10}\text{\,}\mathrm{kg}$
and for the maximum grain masses between
$9\text{\times}{10}^{-9}\text{\,}\mathrm{kg}3\text{\times}{10}^{-7}\text{\,}\mathrm{kg}$,
although this range only varied one order of magnitude within the same shower.
This may reflect the manual modelling approach as we found the grain masses
were the hardest parameter to constrain as similar solutions can be made if
other ablation properties are varied in concert with the grain mass limits.
Thus we do not view the intrashower grain mass limit differences as
significant.

###  5.7 Key Shower Comparison

A summary of our model results per shower is given in Table 5. As Vojáček
[2017] is the only other study performing comparable model fits to our work,
we compare our findings in detail to theirs. For the Geminids we found an
average bulk density of 1387 $\pm$
$240\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$ and a grain mass index $s$
= 1.85 $\pm$ 0.20, compared to an average of
$1788\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$ and $s$ = 1.90 from
Vojáček [2017]. These are similar results confirming the well-known result
that Geminid meteoroid material is intrinsically stronger than typical low-
density cometary material.

In contrast to our work, however, Vojáček [2017] found that mm-sized Taurids
are denser than the Geminids, with an average bulk density of
$1967\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$, while we find
significantly lower bulk density averaging 647 $\pm$
$58\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$, more consistent with
larger Taurids which were found to have cometary densities [Borovička &
Spurný, 2020]. We also find a slightly higher average $s$ value for the
Taurids of $s$ = 2.11 $\pm$ 0.13 compared to $s$ = 1.95 found by Vojáček
[2017], however our value is within the statistical error. These disparities
may not be entirely surprising as both the Northern and Southern Taurids can
easily be contaminated with sporadic meteoroids [Borovička & Spurný, 2020],
suggesting that some of our Taurid meteors may be sporadics.

In order to confirm our analyzed meteors were in fact Taurids, we compared the
radiants for each of our Taurid events with the observations of the Global
Meteor Network [Vida et al., 2021b]. We find that of our 9 Taurid events, 2
are NTAs (2021-11-08 04:53:19 and 2021-11-08 04:13:48), and the remaining 7
are STAs. One event is close to the border for STA radiants, but even if this
one event was a sporadic it still would not explain the characteristic
differences we find across the rest of our Taurid events. It is possible that
mm-sized Taurids contain a variety of materials with different bulk densities
and strengths. Such heterogeneity in the Taurids (and also several other
streams) is also reported among the fireball population [Borovička et al.,
2022]

This is also seen in Table 2 where both the NTAs and STAs have one of the
widest ranges in densities of previously measured literature values for any
shower
($260\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}1967\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$).
Despite this wide range of bulk densities, $s$ values from the literature for
both Taurid meteoroids and dust near the parent comet 2P/Encke point toward
material dominated by large mass grains ($s$ < 2). This differs from our
results and again suggests different processes and evolution for
meteoroids/young dust as compared to the constituent grains making up the
building blocks of meteoroids.

For the Perseids, Vojáček [2017] found extremely low densities
($100\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}250\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$),
consistent with very weak, fresh cometary material. We find an average bulk
density $\sim$ 2 times higher (365 $\pm$
$134\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$), but generally in
agreement within systematic uncertainties. Note our average density for the
shower is weighted upwards significantly because of one event (2021-08-14
07:06:31, $\rho$ = $900\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$), which
if removed as an outlier, would result in an average density of
$231\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$ for the shower, a nearly
identical result to Vojáček [2017]. The Perseid grain mass index of $s$ = 2.16
$\pm$ 0.21 also agrees within uncertainty with the value found by Vojáček
[2017] ($s$ = 2.07). We suggest a special focus should be put on the Perseids
as they are one of the showers that pose the most significant risk to
spacecraft due to their high activity and speed, making measuring an accurate
bulk density particularly important. So while we only found one higher density
Perseid, this is 20% of our data set and possibly indicative of a range of
physical properties within the stream. This is consistent with the findings of
Matlovič et al. [2019], Borovička et al. [2022].

The Lyrids produced an average bulk density of 438 $\pm$
$36\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$, which is about $\sim$2
times higher than the results of Vojáček [2017], but again similar given
systematic uncertainties. The only other study that estimated Lyrid bulk
density, Verniani [1967], found an intermediate value of
$390\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$. There are no measurements
of the bulk density of the Lyrid’s parent comet, C/1861 G1 (Thatcher). Our
conservative estimate is that the average bulk density of the Lyrids is almost
certainly <$1000\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$. Likely it is
even much lower, as the highest density meteoroid we found was
$474\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$ and both Vojáček [2017]
and Verniani [1967] point to lower densities than ours. Interestingly, our
measured grain differential mass index $s$ agrees well with Vojáček [2017]
such that the meteoroids in the shower are primarily composed of small grains.

The Lyrids also contain the one event (2020-04-22 09:00:11) which required a
bulk density change, increasing from an initial value of
$401\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$ to
$2966\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$ about a third of the way
through the trajectory. This increase is likely the sign of either a
refractory inclusion such as a Calcium and Aluminum-rich inclusion (CAI),
refractory metal elements/beads [e.g. Rudraswami et al., 2014] or possibly an
Fe sulphide refractory inclusion such as the ones found in the dust of comet
67P [Fulle et al., 2017], 81P/Wild2 [Brownlee et al., 2012] and common in
micrometeorites [e.g. Taylor et al., 2012]. Looking qualitatively at the
latter half of the event, the meteor appears as a small single body or leading
fragment consistent with such an inclusion. Finally, this higher density
inclusion would also explain the prolonged "hump" seen at the end of the
lightcurve (see the plot in Supplementary Materials). It also further supports
the notion that high-temperature condensates were widely mixed across large
radial distances in the early solar nebula as suggested by returned samples
from comet 81P/Wild2 [Brownlee, 2014].

###  5.8 Bulk Density and Orbital Population

The Tisserand parameter with respect to Jupiter (TJ) is a simple (and
approximate) means of associating an orbit with its likely small body
population [Kresák, 1972]. Orbits with TJ > 3 are more likely of asteroidal
origin, those with 2 < TJ < 3 originate from ecliptic comets, though mixing
between cometary and asteroidal populations occurs in 3 < TJ < 3.08 [Tancredi,
2014]. Objects with TJ < 2 are classified as nearly-isotropic comets (NIC).

We can also further categorize these orbits into several sub-types. Following
the taxonomy proposed by Levison [1996], we subdivide NIC orbits into external
($a>40$ AU) and Halley-type (HT) ($a<40$ AU). We divide ecliptic comets into
Encke-type orbits having TJ > 3 with a semi-major axis larger than that of
Jupiter ($a>a_{\mathrm{J}}$), and into Jupiter-family comet (JFC) orbits with
TJ < 3.

One exception to this classification are the Geminids. Though their parent
body (3200) Phaethon, if it were an active comet, would technically be on an
Encke-type orbit, we separate them into a special class as Phaethon is
compositionally unique among meteor shower parent bodies [Halliday, 1988]. The
asteroid is spectrally close to carbonaceous chondrites [MacLennan & Granvik,
2023] and possibly made of anhydrous carbonaceous material [Geem et al.,
2022].

Generally, we may expect a relationship between TJ and bulk density. Moorhead
et al. [2017] proposed that bulk densities of meteoroids are strongly
correlated with TJ, perhaps even more so than with the KB parameter from
Ceplecha [1967]. Figure 16 shows this relationship for our data and our
measurements of meteoroid bulk density as a function of the orbital population
are compared to Kikwaya et al. [2011] and Vojáček [2017] in Table 6. Overall,
our bulk densities were lower than either study for both Halley-type and
Jupiter-family meteoroids. The authors note that Poynting-Robertson
circulation of JFC material may systematically increase TJ over time above TJ
= 3. Thus the Taurid and Alpha-Capricornid meteoroids are actually JFC-
derived, despite having meteoroid orbits just over TJ = 3.

Figure 16: The Tisserand parameter with respect to Jupiter as a function of meteoroid bulk density ($\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$). Events are colour coded by shower. Work | Bulk Densities ($\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$)
---|---
| Halley-type | Jupiter-family
Kikwaya et al. [2011] | 360${}^{+400}_{-100}$ \- 1510${}^{+400}_{-900}$ | 3190 ${}^{+490}_{-480}$
Vojáček [2017] | 960 $\pm$ 220 | 850 $\pm 110$
This work | 345 $\pm$ 48 | 602 $\pm$ 155
Table 6: The average bulk densities for meteoroids of various $T_{J}$ classes
from three studies. A range of values is shown instead where the average value
is not available.

We see a slight positive correlation between bulk density and TJ, but this is
mainly due to the Geminids. There are clearly higher density streams that have
NIC/HTC-type orbits, though all have averages below
$1000\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$. We find this correlation
is significantly weaker than was found by Kikwaya et al. [2011], though that
study selected a wide range of meteoroids, mostly sporadics, not just cometary
streams with known parents. Our showers with JFC parents (e.g. Taurids, Alpha-
Capricornids) do not have unusually high bulk density, at variance with the
results of Kikwaya et al. [2011]. For example, our lowest density shower, the
Tau-Herculids is from a JFC parent. The difference we believe may be that the
increased accuracy of CAMO data and the wake it observes helped significantly
constrain the bulk densities, while the observations of many meteors in
Kikwaya et al. [2011] lacked wake and were less accurate.

High densities above $3000\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$ for
meteoroids on JFC orbits was the most surprising result of Kikwaya et al.
[2011], as cometary densities
<$1000\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$ would be expected. We
found an average density of 602 $\pm$
$155\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$ for such meteoroids, close
to the value of 850 $\pm$ $110\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$
found by Vojáček [2017]. Our model uses realistic densities of silicate grains
of $3500\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$ and sets that as a
hard limit on the bulk density, however, the model can still ascertain bulk
densities up to that point and would be able to reproduce the high densities
found by Kikwaya et al. [2011]. One reason that could explain the difference
in densities between our study and the other two is that our study only looked
at two JFC showers, the Alpha-Capricornids and the Tau-Herculids. Both of
these showers are relatively dynamically young [Jenniskens & Vaubaillon,
2010], whereas the other studies looked at sporadic meteors which are
dynamically older [Campbell-Brown & Brown, 2005]. The increased processing and
weathering of these older meteoroids might mean a decrease in porosity,
however it is unlikely this process could push bulk densities
>$3000\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$, reminiscent of
monolithic silicates. For example, IDPs (which are smaller than the mm to cm-
sized meteoroids represented in Table 6) have dynamical ages in excess of 105
years, much longer than sporadic meteoroids, and their densities only range
from
$700\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}1700\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$
[Love et al., 1994]. Moreover, the building process for larger cometary dust
suggests that meteoroid bulk densities should decrease with size [Divine et
al., 1986]. Given all these considerations, it is unlikely weathering alone
can explain the difference between our results and the results of Kikwaya et
al. [2011].

For HTC meteoroids, we find lower densities when compared to the other two
works, both of which agree fairly well with each other. This difference could
again be a bias in our sample as we are including only shower meteoroids while
the other two studies also included sporadics. Vojáček [2017] analyzed three
Perseids which had an average bulk density of only
$167\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$. Kikwaya et al. [2011]
analyzed 10 Perseids which had an average bulk density of 620 $\pm$
$200\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$. From these results, and
our results in Figure 11 it is fairly clear that the HTC shower meteors have
extremely low density. Other than the
$900\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$ Perseid and the
$668\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$ Monocerotid, all of our
modelled HT meteoroids had densities less than
$500\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$.

For meteoroids on asteroidal orbits Kikwaya et al. [2011] found densities
between
$3000\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}5000\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$.
The only “asteroidal” shower in our analysis was the Geminids, where we found
densities between
$900\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}2300\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$,
with an average value of $1387\pm$
$240\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$. Despite their asteroidal
orbits, we exclude the Taurids from this group as their parent body (2P/Encke)
is clearly a comet.

##  6 Conclusions

We have applied a numerical implementation of the erosion model from Borovička
et al. [2007] to a total of 41 mm-sized faint meteors from various meteor
showers with known cometary parent bodies. The model was fit to the
lightcurve, atmospheric deceleration, and wake observed by the Canadian
Automated Meteor Observatory’s mirror tracking system. The data were forward
modelled to measure meteoroid physical properties, which were compared to in
situ measurements of comet dust and used to characterize general properties of
cometary material from different populations (HTC, JFC, asteroidal).

Our main conclusions are:

  1. 1.

Our measured shower meteoroid bulk densities agree with both the in situ
parent comet nucleus/dust densities and the lower end of previous in-
atmosphere estimates from the literature.

  2. 2.

We confirm that bulk density slightly increases as a function of $T_{J}$
[Moorhead et al., 2017] up to
$\sim$$1000\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$, but we do not
reproduce high bulk densities of meteoroids on JFC orbits
($\sim$$3000\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$) measured by
Kikwaya et al. [2011].

  3. 3.

Grain mass indices $s$ < 2 were confirmed for the Geminids and the Eta-
Aquariids, while $s$ > 2 was confirmed for the Lyrids.

  4. 4.

For most showers, meteoroids within the same shower appear to be materially
homogeneous, showing similar material properties with little variance as
reflected by very similar ablation and erosion model parameters.

  5. 5.

Individual meteoroids from some showers (CAP, TAH, GEM, ORI, and PER) appear
to have complex structural morphologies, reflecting a change in erosion or
ablation properties during flight. Some show a leading fragment morphology
while wake observations show that the distribution of eroded grains changes
during the flight. Such events were harder to model as we assumed the grain
distribution is constant as to keep the number of free parameters reasonable.

  6. 6.

Most showers had similar values of the average energy required to trigger
erosion, suggesting that grain bonds require fixed energy to be released
across showers. However, a large difference between ORI and ETA erosion
energies hints at dynamic pressure as a significant factor in the onset of
erosion.

  7. 7.

A Lyrid meteor was found which required an increase in bulk density from
$401\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$ to
$\sim$$3000\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$. This is consistent
with a refractory inclusion.

##  7 Acknowledgements

This work was supported in part by the NASA Meteoroid Environment Office under
cooperative agreement 80NSSC21M0073. PGB also acknowledges funding support
from the Natural Sciences and Engineering Research council of Canada and the
Canada Research Chairs program. We thank Z. Krzeminski for help in optical
data reduction, J. Gill, and M. Mazur for software support and camera
operations and V. Vojáček for data access.

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##  Appendix A Summary of Bulk Density Estimates of Cometary Nuclei From
Literature

Table 7: Estimated bulk densities ($\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$) of comet nuclei previously investigated by in situ spacecraft missions. (1P/Halley is moved to the next table as it is a direct comparison for this work). Comet |  Work |  Method |  Bulk Density ($\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$)
---|---|---|---
9P/Tempel |  Richardson et al. (2007) |  Analysis of Ejecta Plume |  400 (+600, -200)
|  Davidsson et al. (2007) |  Nongravitational Forces |  450 ($\pm$250)
|  Sosa $\&$ Fernández (2009) |  Nongravitational Forces |  200 ($\pm$100)
|  Thomas et al. (2013) |  Deep Impact Ejecta |  470 (+780, -240)
19P/Borelly |  Davidsson $\&$ Gutiérrez (2004) |  Nongravitational Forces |  180-300
|  Farnham $\&$ Cochran (2002) |  Nongravitational Forces |  490 (+340, -200)
21P/Giacobini-Zinner |  Trigo-Rodriguez $\&$ Llorca (2006) |  Strength/Density Interpolation |  $<$200
67P/Churyumov-Gerasimenko |  Preusker et al. (2015) |  OSIRIS Image Analysis |  535 ($\pm$35)
|  Jorda et al. (2016) |  OSIRIS Image Analysis |  532 ($\pm$7)
|  Pätzold et al. (2016) |  Gravity Field Analysis |  533 ($\pm$6)
81P/Wild |  Davidsson et al. (2006) |  Nongravitational Forces |  $\leq$600-800
|  Sosa $\&$ Fernández (2009) |  Nongravitational Forces |  300 ($\leq$800)
103P/Hartley 2 |  Thomas et al. (2013) |  MRI Image Analysis |  200-400
|  Richardson $\&$ Bowling (2014) |  Erosion Rate |  220 (+300, -80)
Table 8: Estimated bulk densities
($\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$) of parent comet nuclei for
showers analyzed in this work. (a) Preferred value, the possible range is 30 -
4900 $\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$.
Comet |  Relevant Showers |  Work |  Bulk Density ($\text{\,}\mathrm{kg}\text{\,}{\mathrm{m}}^{-3}$)
---|---|---|---
1P/Halley |  ORI and ETA |  Peale (1989) |  $1000^{(a)}$
|  |  Sagdeev et al. (1988) |  600 (+900, -400)
|  |  Skorov $\&$ Rickman (1999) |  500-1200
|  |  Greenberg $\&$ Li (1999) |  260-510
|  |  Sosa $\&$ Fernández (2009) |  500 ($\pm$300)
2P/Encke |  STA and NTA |  Harmon $\&$ Nolan (2005) |  500-1000
|  |  Sosa $\&$ Fernández (2009) |  800 ($\pm$800)
169P/NEAT |  CAP |  Kasuga et al. (2010) |  200-500
(3200) Phaethon |  GEM |  Hanuš et al. (2018) |  1670 ($\pm$470)
|  |  Masiero et al. (2021) |  3000

##  Appendix B Differential Mass Index $s$ of Shower and Comet Dust From
Literature

Table 9: Differential mass indices ($s$) for the relevant major showers (part 1). Adapted from [Blaauw Erskine, 2019], references are provided therein. (a) Note: average grain distribution mass indices were added from Vojáček [2017] for the GEM, PER, LYR, and Taurids. Shower |  $s$ |  Work |  Magnitude/Mass Range
---|---|---|---
ETA |  1.40 |  Wiess (1961) |  +1.75 to +0.5
|  2.17 ($\pm$ 0.24) |  Cevolani et al. (1998) |  limit of $10^{-8}$ to $10^{-9}$ kg
|  1.85 ($\pm$ 0.07) |  Blaauw et al. (2011a) |  $10^{-8}$ kg
ORI |  1.95 |  Schult et al. (2018) |  $10^{-8}$ kg
|  2.06 ($\pm$ 0.17) |  Cevolani et al. (1998) |  limit of $10^{-8}$ to $10^{-9}$ kg
|  1.65 ($\pm$0.04) to 0.77 ($\pm$0.06) |  Blaauw et al. (2011a) |  $10^{-8}$ kg
GEM |  2.3 |  Browne et al. (1957) |  +2.0
|  1.62 |  Browne et al. (1957) |  +7.0 to +5.0
|  2.7 |  Kresakova (1966) |  +2.5 to -3.5
|  1.5 ($\pm$ 0.3) to 1.0 ($\pm$ 0.05) |  Webster et al. (1966) |  +4.5
|  1.5 |  Belkovich et al. (2001) |  +3.0
|  1.63 ($\pm$ 0.05) |  Chenna Reddy et al. (2008) |  +3.5
|  1.64 |  Schult et al. (2018) |  $10^{-8}$ kg
|  1.71 ($\pm$ 0.02) to 0.78 ($\pm$ 0.04) |  Hughes (1973) |  $10^{-6}$ to $10^{-8}$ kg
|  2.03 ($\pm$ 0.04) |  Cevolani et al. (1998) |  limit of $10^{-8}$ to $10^{-9}$ kg
|  1.58 to 1.63 ($\pm$ 0.04) |  Blaauw et al. (2011a) |  $10^{-8}$ kg
|  $1.9^{(a)}$ |  Vojacek (2017) |  $10^{-5}$ to $10^{-10}$ kg
LEO |  1.35 ($\pm$ 0.02) to 1.48 ($\pm$ 0.05) |  Plavcoa 1968 |  +2.5 to 0
|  1.5 ($\pm$ 0.6) |  Brown et al. 1998 |  +4.5
|  1.64 ($\pm$ 0.17) |  Brown et al. 1998 |  +5.0
|  1.9 |  Koten et al. 2008 |  +5.0
|  1.91 ($\pm$ 0.08) |  Cevolani et al. 1998 |  limit of $10^{-8}$ to $10^{-9}$ kg
Table 10: Differential mass indices ($s$) for the relevant major showers (part 2). Adapted from [Blaauw Erskine, 2019], references are provided therein. (a) Note: average grain mass indices were added from Vojáček [2017] for the GEM, PER, LYR, and Taurids. Shower |  $s$ |  Work |  Magnitude/Mass Range
---|---|---|---
LYR |  2.1 $\pm$ 0.12 |  Cevolani et al. (1998) |  $10^{-8}$ to $10^{-9}$ kg
|  2.32(a) |  Vojacek (2017) |  $10^{-5}$ to $10^{-13}$ kg
TAURIDS |  1.37 $\pm$ 0.1 |  Sokolova $\&$ Sergienko (2016) |  +3
|  1.95(a) |  Vojacek (2017) |  $10^{-8}$ to $10^{-12}$ kg
PER |  1.62 |  Bel’kovich $\&$ Ishmukhametova (2006) |  +3
|  2 |  Browne et al. (1957) |  +2
|  1.59 |  Browne et al. (1957) |  +7 to +5
|  1.57 |  Belkovich et al. (1995) |  +3
|  1.45 |  Schult et al. (2018) |  $10^{-8}$ kg
|  1.74 $\pm$ 0.01 |  Cevolani et al. (1998) |  $10^{-8}$ to $10^{-9}$ kg
|  2.07(a) |  Vojacek (2017) |  $10^{-6}$ to $10^{-9}$ kg
Table 11: Differential mass indices ($s$) for the dust ejected by parent comets related to major showers in our study. Parent Comet |  Relevant Shower |  $s$ |  Work |  Mass/Size Range
---|---|---|---|---
1P/Halley |  ORI $\&$ ETA |  1.85 |  McDonnell et al. (1987) |  $10^{-13}$ to $10^{-8}$ kg
|  ORI and ETA |  1.75 ($\pm$ 0.07) |  McDonnell et al. (1991) |  $10^{-13}$ to $10^{-9}$ kg
|  ORI and ETA |  1.53 ($\pm$ 0.1) |  Fulle et al. (2000) |  $10^{-12}$ to $10^{-3}$ kg
2P/Encke |  STA and NTA |  1.73 ($\pm$ 0.067) |  Epifani et al. (2001) |  11.5 to 15 $\mu$m
|  STA and NTA |  1.87 ($\pm$ 0.1) |  Fulle (1989) |  40 $\mu$ to 20 cm
109P/Swift-Tuttle |  PER |  1.5 ($\pm$ 0.17) |  Sarmecanic et al. (1998) |  0.6 to 10 $\mu$m
73P/Schwassmann-Wachmann |  TAH |  1.85$-$2.0 |  Vaubaillon $\&$ Reach (2010) |  $10^{-15}$ to $10^{-8}$ kg

##  Appendix C Reduction Methodology

Figure 17: Vertical stack of a meteor recorded using the CAMO narrow-field
system with time increasing from top to bottom. Each frame has been rotated
until the horizontal and leading edges are aligned to display morphology and
visibility of wake. The event was a Southern Taurid recorded October 10, 2019
at 08:49:12 UTC.

###  C.1 Data Reduction: CAMO Weblog

All events investigated in this work were reduced according to the CAMO data
reduction pipeline presented in [Vida et al., 2021a]. Nevertheless, a brief
explanation of the process is given here. All events detected by CAMO on a
given night are sent to a central server where their orbits are computed with
up-to-date astrometric plates. Every morning, a new web page on the internal
CAMO weblog is generated with that night’s events, including each event’s
wide- and narrow-field videos and their initially estimated orbits. After a
manual inspection, suitable events are chosen for data reduction.

###  C.2 Data Reduction: Wide-Field Data

Flat fields for the wide-field data are created by co-adding many video frames
from a given night which are separated in time, eliminating star trails.
Astrometric plates and photometry are calibrated manually using the meteor
video data to ensure a high level of quality. Position picks on the meteor are
also done manually [Weryk et al., 2013]. The centroiding region’s position and
radius are manually chosen for each frame of the wide-field video and the
extent of the meteor is manually selected for accurate photometry, as shown in
Figure 18. Astrometric picks are run through a trajectory solver based on
[Borovička, 1990], and a preliminary heliocentric meteoroid orbit is computed.

Figure 18: Example wide-field video frame. Astrometric picks are seen as small
crosses spanning the meteor track, while the extent of the photometry mask on
the frame is represented as green highlighted pixels. Yellow circles and lines
are star positions and their residuals used to create the astrometric plate.

###  C.3 Data Reduction: Narrow-Field Data

Reduction of narrow-field data is done using the Mirfit software, shown in
Figure 19. The astrometric plate is computed during a dedicated calibration
procedure which runs every hour during which the mirrors are pointed at up to