# Search for fast radio transients using Arecibo drift-scan observations at
1.4 GHz

B. B. P. Perera,1 A. J. Smith,1 S. Vaddi,1 R. Carballo-Rubio,2 A. McGilvray,1
A. Venkataraman,1 D. Anish Roshi,1 P. K. Manoharan,1 P. Perillat,1 E. Lieb,3
D. R. Lorimer,4,5 M. A. McLaughlin,4,5 D. Agarwal,4,5 K. Aggarwal4,5 and S. M.
Ransom6

1 Arecibo Observatory, University of Central Florida, HC3 Box 53995, Arecibo,
PR 00612, USA
2 Florida Space Institute, University of Central Florida, 12354 Research
Parkway, Partnership 1, Orlando, FL 32826, USA
3 Department of Astrophysical & Planetary Sciences, University of Colorado,
Boulder, CO 80309, USA
4 Department of Physics and Astronomy, West Virginia University, Morgantown,
WV 26501, USA
5 Center for Gravitational Waves and Cosmology, West Virginia University,
Chestnut Ridge Research Building, Morgantown, WV 26505, USA
6 National Radio Astronomy Observatory, 520 Edgemont Rd., Charlottesville, VA
22903, USA E-mail<EMAIL_ADDRESS>
(Accepted XXX. Received YYY; in original form ZZZ)

###### Abstract

We conducted a drift-scan observation campaign using the 305-m Arecibo
telescope in January and March 2020 when the observatory was temporarily
closed during the intense earthquakes and the initial outbreak of the COVID-19
pandemic, respectively. The primary objective of the survey was to search for
fast radio transients, including Fast Radio Bursts (FRBs) and Rotating Radio
Transients (RRATs). We used the 7-beam ALFA receiver to observe different
sections of the sky within the declination region $\sim$(10–20) deg on 23
nights and collected 160 hours of data in total. We searched our data for
single-pulse transients, covering up to a maximum dispersion measure of 11 000
pc cm-3 at which the dispersion delay across the entire bandwidth is equal to
the 13 s transit length of our observations. The analysis produced more than
18 million candidates. Machine learning techniques sorted the radio frequency
interference and possibly astrophysical candidates, allowing us to visually
inspect and confirm the candidate transients. We found no evidence for new
astrophysical transients in our data. We also searched for emission from
repeated transient signals, but found no evidence for such sources. We
detected single pulses from two known pulsars in our observations and their
measured flux densities are consistent with the expected values. Based on our
observations and sensitivity, we estimated the upper limit for the FRB rate to
be $<$2.8$\times 10^{5}$ sky-1 day-1 above a fluence of 0.16 Jy ms at 1.4 GHz,
which is consistent with the rates from other telescopes and surveys.

###### keywords:

transients: fast radio bursts – stars: neutron – pulsars: general – pulsars:
individual: PSR J0625$+$10, J1627$+$1419

††pubyear: 2021††pagerange: Search for fast radio transients using Arecibo
drift-scan observations at 1.4 GHz–A

##  1 Introduction

FRBs are millisecond-duration energetic radio pulses that are accompanied by
large dispersion measures (DMs, where DM is the integral of the column density
of free electrons along the line-of-sight from the Earth to the source),
placing their origin at cosmological distances. The majority of initial FRBs
were discovered with the Parkes radio telescope (Lorimer et al., 2007;
Thornton et al., 2013) and since then, more than 600 FRBs have been detected
using various other telescopes111https://www.frbcat.org,222https://www.chime-
frb.ca/catalog, covering observing centre frequencies below 1.4 GHz (Spitler
et al., 2014; Masui et al., 2015; Caleb et al., 2017; Bannister et al., 2017;
Shannon et al., 2018; CHIME/FRB Collaboration et al., 2019a; Zhu et al., 2020;
Law et al., 2020; Ravi et al., 2019; Connor et al., 2020), and including some
follow-up observations at 4–8 GHz (Michilli et al., 2018; Gajjar et al.,
2018). The DMs of FRBs (currently in the range $\sim$100–3000 pc cm-3; The
CHIME/FRB Collaboration et al., 2021) are much larger than the expected DM
contributions from the Milky Way in the direction of their lines-of-sight,
indicating that the origins must be extra-galactic. The FRBs were identified
as one-time transient events until the first repeating emission was detected
with the Arecibo telescope from FRB121102 (Spitler et al., 2016). Since then,
more than 20 FRBs have been identified as repeaters (Luo et al., 2020;
CHIME/FRB Collaboration et al., 2019c, b; Kumar et al., 2019). Using follow-up
observations with the VLA, FRB121102 was localized to a star-forming region in
a dwarf galaxy at a redshift $z$ of 0.193 (Chatterjee et al., 2017), providing
further evidence for FRBs that are cosmological in origin. Since then, 17
additional FRBs have been localized to sub-arcsecond precision by radio
interferometers, associating them with host galaxies333http://frbhosts.org
with known redshifts $z\sim 0.034-0.66$ (e.g., Law et al., 2020; Marcote et
al., 2020; Macquart et al., 2020; Bannister et al., 2019; Prochaska et al.,
2019; Ravi et al., 2019; Chatterjee et al., 2017; Heintz et al., 2020; Piro et
al., 2021; Bhandari et al., 2021).

While we have evidence that FRBs are associated with host galaxies, these
galaxies have a range of properties, and the FRB origins and formation
mechanisms are still a mystery. Determining the prevalence of FRBs across
cosmic time could provide significant insight into their formation mechanisms.
The Galactic magnetar SGR J1935$+$2154 emitted FRB-like bursts during its
recent active phase (CHIME/FRB Collaboration et al., 2020; Bochenek et al.,
2020). These bursts are highly energetic, brighter than any radio bursts seen
from Galactic sources, and only a few orders of magnitude lower than the
equivalent energy from the faintest FRB (Bailes et al., 2021). This leads to
the possibility that at least some FRBs are associated with magnetars (see
Lyutikov & Popov, 2020). In order to identify a possible neutron star (NS)
origin for FRBs, periodic searches (with periods of order milliseconds to
seconds) have been carried out, but these were not successful (Zhang et al.,
2018). However, long periodicities of $\sim$16 days and $\sim$157 days have
been reported for FRB180916B (Chime/Frb Collaboration et al., 2020) and
FRB121102 (Rajwade et al., 2020; Cruces et al., 2021), respectively,
suggesting that FRB emission mechanisms perhaps align with putative binary
systems (Ioka & Zhang, 2020; Lyutikov et al., 2020), slowly spinning neutron
stars (Beniamini et al., 2020), and spin precession, including orbit-induced
precession (Yang & Zou, 2020), forced precession (Sob’yanin, 2020), and free
precession models (Levin et al., 2020). Based on the properties of host
galaxies, FRBs may also originate through superluminous supernovae and long
gamma-ray bursts (for reviews, see Xiao et al., 2021; Zhang, 2020a). Moreover,
some suggest that FRBs are produced through the magnetospheric activities of
magnetars that have been newly formed through binary NS mergers or pre-merger
NS-NS interactions (Zhang, 2020b; Wang et al., 2020). However, none of these
mechanisms are exclusively capable of explaining the origin of FRBs in
general.

Recently, Palliyaguru et al. (2021) carried out a search targeting FRBs
associated with a sample of 11 gamma-ray bursts that show evidence for the
birth of a magnetar. However, this study was unsuccessful in finding FRB-like
signals associated with these sources. To uncover the physics of these
phenomena, it is essential to increase the known FRB population, to measure a
broad range of characteristics such as their duration, emission component
structure, spectral properties, polarization, and to also achieve localization
in order to measure the properties of the host galaxy. FRBs are detected
through single-pulse search algorithms due to their non-periodic, energetic,
short-duration, and single-event nature. Many telescopes utilize real-time FRB
detection instruments with single-pulse search pipelines to improve the speed
and the efficiency of new discoveries, enabling the observations to be done
commensally with other projects and ultimately maximizing telescope time for
FRB searches and characterization studies (e.g., CHIME/FRB Collaboration et
al., 2018; Karastergiou et al., 2015; Foster et al., 2018; Surnis et al.,
2019; The CHIME/FRB Collaboration et al., 2021; Niu et al., 2021).

FRBs with high DMs are potentially important as they are generally accompanied
by high redshifts and provide a unique opportunity to probe the far reaches of
the intergalactic medium. This is crucial for constraining the epochs of
hydrogen and helium reionization produced by the ignition of the earliest
stars and galaxies (see Keating et al., 2015), leading to a better
understanding of the universe in general. The largest DM observed so far is
$3038.06\pm 0.02$ pc cm-3 for FRB20180906B444https://www.chime-frb.ca/catalog,
placing its origin at a redshift $>$2 (The CHIME/FRB Collaboration et al.,
2021). Some distant FRBs with large DMs exhibit low fluences, indicating the
importance of highly sensitive telescopes in detecting those weak sources
(Lorimer, 2018; Zhang, 2018). The highly sensitive 500-m FAST telescope has
discovered four FRBs so far, all of which are accompanied by high DMs ($>$1000
pc cm-3) and low fluences ($<$0.2 Jy ms) – e.g., FRB 181017.J0036+11 has a DM
of $1845.2\pm 1$ pc cm-3 and a fluence of 0.042 Jy ms, which is the faintest
FRB detected so far (Niu et al., 2021; Zhu et al., 2020). Moreover, both FRBs
detected by the Arecibo telescope have low fluences – 0.08 and 1.2 Jy ms for
FRB141113 and FRB121102, respectively (Spitler et al., 2014; Patel et al.,
2018) – and are in the lower end of the fluence distribution of known FRBs
(see Fig. 1). In general, detecting FRBs with high redshift and low fluence is
challenging due to sensitivity limitations of telescopes. Hence, large
aperture instruments such as the FAST and Arecibo telescopes are of paramount
importance for detecting such weak sources (see Zhang, 2018). However, the
small field-of-view of such large telescope degrades the speed of the surveys.

Figure 1:  Excess DM and fluence for all known FRBs to-date. The excess DM is
obtained after subtracting the expected Galactic contribution based on the
electron density model YMW16 (Yao et al., 2017) along the line-of-sight of the
source. The upper x-axis represents the redshift and is calculated using the
method given in Ioka (2003). More details are given in Section 4. The
horizontal dashed lines represent the single-pulse sensitivity for some of the
telescopes, assuming a pulse width of 1 ms and a flat spectral index (see
Section 3). The typical center frequencies of these FRB detections and surveys
are given in parentheses.

RRATs are rotating neutron stars that emit sporadic emission that can be
discovered only through single-pulse search algorithms (McLaughlin et al.,
2006). They have much more similar emission properties to normal pulsars than
FRBs. Unlike FRBs, RRATs are located within our
Galaxy555http://astro.phys.wvu.edu/rratalog. Utilizing a large DM range in
single-pulse search algorithms covering the Galactic DMs, we can detect both
of these fast transient sources, in addition to normal or giant pulse emitting
pulsars, and this is a standard approach in many surveys (see Patel et al.,
2018; Parent et al., 2020, for details).

In this work, we carried out a drift-scan observation campaign at the Arecibo
Observatory to search for fast radio transients, including FRBs and RRATs. The
paper is organized as follows: we describe our observations, data preparation,
and the system performance in Section 2. The sensitivity of our data to single
pulses is estimated in Section 3 and we discuss our search pipeline in Section
4. The results of our single-pulse search are presented in Section 5, and the
detection of known pulsars in our data and their flux densities are described
in Section 6. Finally in Section 7, we discuss results and limits on FRB rates
based on our observations.

##  2 Observations

A series of strong earthquakes struck Puerto Rico starting on 28 December
2019, including a magnitude of 6.0 catastrophic
earthquake666https://www.usgs.gov/news/magnitude-64-earthquake-puerto-rico on
6 January 2020 in the southwest of the island. Following these events, the
Arecibo Observatory was temporarily closed for several weeks in January 2020
for safety inspections of the facility. Shortly thereafter, the COVID-19
pandemic also forced a temporary closure of the observatory site during March
2020. During both of these periods, we were able to coordinate and conduct
drift-scan observations with the 305-m Arecibo telescope with minimum
operational support by recording the data continuously as the sky drifted
across the telescope beam at the sidereal rate. We observed everyday between
20–28 January and 16–29 March, resulting in 23 days in total. Each day, we
started observations around 20:00 AST (Atlantic Standard Time) and continued
taking data for about 8–10 hours. The observation program and the time
duration on each day are presented in Fig. 2. We further note that this
campaign was conducted during the downtime of the telescope until its normal
operations resumed. Therefore, we could not collect more data after this
period.

Figure 2:  The observation program of the drift-scan campaign. We observed 9
and 14 days consecutively in January and March, respectively. Each day, we
started the observation between 18:00–21:00 AST, except on March 26 where we
started at 23:15 AST due to technical difficulties. We observed for 8–10 hours
each day except only 4.5 hours on March 24 due to data storage issues.

The primary goal of our campaign was to search for new transients in the
Arecibo sky. The transient searches typically require high time and frequency
resolution. FRBs were mainly detected at $\sim$1.4 GHz frequencies until the
recent CHIME telescope detections at $\sim$600 MHz777https://www.chime-
frb.ca/catalog (The CHIME/FRB Collaboration et al., 2021). We carried out our
observations using the Arecibo L-band Feed Array888http://www.naic.edu/alfa/
(ALFA) receiver with a centre frequency of 1375 MHz and a bandwidth of 322 MHz
with 960 frequency channels, resulting in a spectral channel size of 0.335
MHz. The two polarization channels were summed together, and we recorded the
total intensity data using the Mock spectrometers at a sampling rate of 65
$\mu$s. We note that this setup is very similar to the regular observing
configuration of the PALFA survey999http://www2.naic.edu/alfa/pulsar, which
has discovered several fast
transients101010http://www.naic.edu/~palfa/newpulsars, including both Arecibo-
discovered FRBs and also 20 RRATs (Spitler et al., 2014; Patel et al., 2018;
Deneva et al., 2009; Parent et al., 2021).

The field-of-view (FoV) is important in regular searching campaigns as it can
enhance the instantaneous sky coverage and thus, the survey speed. The ALFA
receiver was a seven-beam feed-array which observed seven pixels on the sky
simultaneously. Each beam had a full-width-half-maximum (FWHM) power of
approximately $3^{\prime}.35$, corresponding to an instantaneous FoV of 0.022
deg2 across the 7 pixels (see Cordes et al., 2006; Spitler et al., 2014). With
this FWHM beamwidth, the sky transit time between the half power points of the
beam was approximately 13 s. Therefore, the ALFA receiver in drift-scan mode
provided rapid sky coverage.

With the limited operational support at the observatory in January, we were
not able to move the receiver in azimuth and zenith angles. Rather we kept it
at the same position (with the exception of January 28), resulting in
observing at the same zenith angle. During the March observations, we were
able to change the receiver position in azimuth by one degree each day in
order to scan different parts of the sky. In addition, the receiver was
positioned at a parallactic angle offset of 19∘ in all our observations to
scan the sky without leaving gaps between the beams (see Fig 3 top panel) and
then unchanged during data-taking. Fig. 3 bottom panel shows the observed
sections of the sky during the two observing sequences. Several known pulsars
and FRBs happened to be within the ALFA beams, and they are marked in the
figure. We discuss the detectability of these sources in Section 6.

Figure 3:  Top: The circles represent the position and the FWHM power of the
ALFA receiver beams with respect to the sky at the beginning of the 16 March
observation. The color stripes represent the first 60 s of the observation,
showing no gaps across the sky between the beams during the scan. Bottom: The
sky regions covered during our drift-scan observations displayed in Galactic
coordinates. The blue shaded area represents the portion of the sky visible to
the Arecibo telescope within its declination range from $-1^{\circ}$ and
$+37.5^{\circ}$. The coverage of January and March observations are marked in
dark gray and light gray, respectively. The known pulsars and FRBs within the
ALFA beams are marked in different colors, and the offsets from the centre of
the closest observed beam are given in parentheses in units of arc-minutes
(see Section 6 for details of the detection of these sources).

###  2.1 Data preparation

The data were recorded in PSRfits
format111111https://www.atnf.csiro.au/research/pulsar/psrfits_definition/Psrfits.html
as 16-bit integers, and the lower and upper half of the frequency bands were
recorded separately as two individual files. Since our single-pulse searching
software supports only 8-bit integers, we compressed the original 16-bit data
into 8-bit using
psrfits2psrfits121212https://github.com/juliadeneva/psrfits2psrfits and then
merged the two frequency bands using
combine_mocks131313https://github.com/demorest/psrfits_utils to produce the
full bandwidth of 322 MHz. As required by our single-pulse search pipeline,
the data were then converted into the filterbank format using
digifil141414http://dspsr.sourceforge.net/index.shtml (van Straten & Bailes,
2011). All these tools are commonly used in pulsar data preparation, handling,
and processing.

We investigated the radio frequency interference (RFI) environment across the
frequency band. By inspecting the dynamic spectra of the data (see Fig. 4 for
example), we identified persistent RFI in a large number of frequency channels
between 1250 and 1300 MHz. In addition, the level of the RFI below 1250 MHz
was dynamic and varied over our observation time. Thus, we decided to ignore
and excise the frequency channels below 1300 MHz across all our observations.
We also identified strong RFI around 1330 and 1350 MHz and excised the
relevant channels. The RFI removal reduced the usable bandwidth by roughly
one-third, resulting in an effective bandwidth of approximately 215 MHz.

Figure 4:  The dynamic spectrum of approximately 130 s of data obtained on 21
January 2020 at 23:22 AST. A high level of RFI can be seen below 1300 MHz in
general and also around 1330 and 1350 MHz. We excised the spectral channels
corresponding to these frequency ranges in the data processing to mitigate
RFI.

###  2.2 System gain and stability

We detected radio continuum emission from many background sources during their
transit across the ALFA beams. Based on their sky locations, we confirm that
these are identified radio point sources (i.e., source size is smaller than
our beam size) reported in the NVSS survey catalog
151515https://www.cv.nrao.edu/nvss (Condon et al., 1998). These compact
continuum sources were detected in almost all beams daily in our observations.
Fig. 5 shows the detection of NVSS 145131$+$134324 on March 17 in the central
beam (Beam0) of the receiver. We used these detected sources along with their
flux densities reported in the NVSS catalog to estimate the gain of the
telescope and the stability of the system throughout our observations. The
details of the gain estimation method are described in Appendix A. We
estimated the gain for all beams (except Beam5) for most observing sessions.
We ignored Beam5 in the gain calculation as one of its polarization channels
was unstable and poorly behaved. However, we note that Beam5 was included in
the single-pulse search analysis described in Section 4. Our system
performance analysis determined that the overall system was stable throughout
the campaign, while showing a slight difference in gain between the January
and March observing sessions. The average gain of the central beam, Beam0, was
estimated to be $8.2\pm 0.5$ K Jy-1 and that of the other beams was $7.1\pm
0.8$ K Jy-1 in March observations, which is consistent with the typical system
performance of the ALFA
receiver161616http://www.naic.edu/%7Ephil/mbeam/mbeam.html. In January, the
average gain of Beam0 and the other beams were $7.5\pm 0.3$ and $5.6\pm 0.8$ K
Jy-1, respectively. We attribute the lower gain to the inactivity of the tie-
down cables of the telescope throughout the January observations, which
resulted in defocusing compared to the optimal telescope setup. However, we
note that even during this period the system sensitivity was still very high
compared to other telescopes in the world, with the exception of the 500-m
FAST telescope.

Figure 5:  The detection of continuum background source NVSS 145131$+$134324
on March 17 in the central beam. The y-axis is scaled as the ratio of the
source temperature and the system temperature. The source has a known flux
density of 692 mJy at 1.4 GHz and was used to estimate the gain of the
telescope as described in Section 2.2.

##  3 Sensitivity to single pulses

From radiometer noise considerations, the peak flux density of a single pulse,

$S_{i}=\frac{\beta(T_{sys}+T_{sky})(S/N)_{b}}{GW_{i}}\sqrt{\frac{W_{b}}{n_{p}\Delta
f}},$ (1)

where $T_{\rm sys}$ is the system temperature at the observing frequency,
$T_{\rm sky}$ is the sky temperature in the direction of the telescope
pointing, $\beta$ is the factor of sensitivity loss due to digitization,
$(S/N)_{\rm b}$ is the signal-to-noise ratio of the broadened pulse, $G$ is
the telescope gain, $\Delta f$ is the observing bandwidth, $n_{p}$ is the
number of summed polarization channels, $W_{i}$ is the intrinsic pulse width,
and $W_{b}$ is the broadened pulse width (see Cordes & McLaughlin, 2003; Patel
et al., 2018). The pulse can be broadened due to various reasons such as
dispersion smearing within the frequency channel, scattering, etc. Assuming a
telescope gain of $8$ K Jy-1 (see Section 2.2) and typical values for the
system $T_{\rm sys}+T_{\rm sky}\approx 30$ K, $\Delta f=215$ MHz, $n_{p}=2$,
and $\beta=1.1$ (Patel et al., 2018), we can rewrite the above expression as
$S_{i}=0.006(S/N)/\sqrt{W}$ Jy for pulse width $W_{i}=W_{b}=W$ in units of
milliseconds.

The above expression gives a theoretical estimate of the single-pulse
sensitivity assuming Gaussian noise in the data. However, the sensitivity in
real data can deviate significantly from this formalism due primarily to the
presence of RFI and non-Gaussian noise. Based on simulations using PALFA data,
Patel et al. (2018) found that the sensitivity of their pipeline to single
pulses was degraded by many factors depending on the dispersion measure and
the pulse width of the detection. They determined a degradation factor of
approximately 1.5 for a pulse with a DM of 1000 pc cm-3 and a width of 5 ms.
Since our observation configuration is similar to that of PALFA, we assume the
same degradation factor in our study as a conservative value. Assuming a
$S/N=8$ and $W=5$ ms, the single-pulse sensitivity in our data is then
estimated to be 0.032 Jy, leading to a fluence ($\equiv S_{i}W$) of
approximately 0.16 Jy ms. This indicates that the high sensitivity of our data
is capable of detecting low fluence FRBs as well as weak single pulses from
RRATs.

Following the above method, we estimated the single-pulse sensitivity for
different telescopes, assuming a pulse width of 1 ms (see Fig. 1). We note
that the telescopes operate at different frequencies in general. Therefore, we
assumed a flat spectral index in the calculation (see Section 7.5 in The
CHIME/FRB Collaboration et al., 2021, for discussion). This estimation shows
that the Arecibo telescope was capable of detecting all the known FRBs to-date
and its sensitivity was second only to the 500-m FAST telescope.

##  4 Single-pulse search

Since we are interested in searching for fast transients, we performed a
standard single-pulse search analysis. We first determined the DM range to
search for according to our observation configuration. The dispersion smearing
across the full bandwidth of our data is estimated to be $1.06\times$DM ms,
where DM is in pc cm-3 (using Equation 4.7 in Lorimer & Kramer, 2012).
Therefore, the maximum DM that can be searched for in our data is $\sim$11 000
pc cm-3 in order to ensure that the entire dispersed signal covers the
bandwidth within the sky transit time of $\sim$13 s, optimizing the S/N. The
signal would only partially cover the bandwidth for DMs greater than this
value thus degrading the S/N. According to the relationship between the DM due
to intergalactic plasma and the redshift (using Equation 2 in Ioka, 2003),
this particular DM limit is equivalent to a redshift of $\sim$14.5, for
$\Omega_{m}=0.3$ and $\Omega_{\Lambda}=0.7$ (see Choudhury & Padmanabhan,
2005; Komatsu et al., 2009).

In keeping with standard procedure of transient searches, we first dedispersed
the data of each beam with trial DMs in the range of 1$-$11 000 pc cm-3,
excluding obvious local RFI with zero DM. The dedispersed data were then
averaged in frequency to generate a time series and then searched for single
pulses. In order to enhance the S/N of single pulses, the time series was
convolved with a series of box-car filters with various widths between 1 and
4096 samples (i.e. between $\sim$0.0655–270 ms in time according to our
$\sim$65.5 $\mu$s sampling time) in power-of-two increments. The information
of the selected single pulses (e.g. time stamp, DM, box-car width) above a S/N
threshold of 6 were saved for further evaluation. All the above steps were
completed using the single-pulse search software package
heimdall171717https://sourceforge.net/projects/heimdall-astro, which processes
the data in parallel using GPUs to speed up the incoherent dedispersion
(Barsdell et al., 2012). We also note that background continuum sources
mentioned in Section 2.2 were removed by subtracting running averages of two
seconds from the time series before they were searched for single pulses.

Single-pulse searches in general produce large number of candidates and
therefore, it is not feasible to inspect all of them visually. For example,
the heimdall package detected several thousand candidates for a given beam for
each hour of our data, resulting in more than 80 000 candidates for all seven
beams per day. We also note that these candidates include a high rate of false
positives due to Gaussian noise and RFI in the data. Therefore, machine
learning techniques and algorithms have been introduced in single-pulse and
pulsar searches to classify the candidates (e.g., RFI/non-astrophysical, FRB,
and pulsar candidates). For instance, the deep learning methods have been used
in pulsar search campaigns and improved the efficiency of searching procedures
significantly (see Zhu et al., 2014; Devine et al., 2016; Guo et al., 2017;
Bethapudi & Desai, 2018). Machine learning techniques have also been used in
FRB searches (Wagstaff et al., 2016; Foster et al., 2018), including
convolution neural network classification methods (Zhang et al., 2018; Connor
& van Leeuwen, 2018). After sorting the candidates using classifier models,
the high-probability ones can then be visually inspected in order to determine
whether they are real astrophysical signals.

We fed all the candidates through the FRB search software
fetch181818https://github.com/devanshkv/fetch, which uses deep neural networks
for classification of FRBs, RRATs, and RFI (Agarwal et al., 2020b; Agarwal &
Aggarwal, 2020). fetch uses single-pulse candidate information from heimdall
as described above and produces frequency-time, DM-time images as well as the
frequency averaged time series. The convolutional neural networks are applied
on these images in the classification process. The software currently includes
11 deep learning models, which were trained using known and simulated FRB,
pulsar, and real RFI signals. Agarwal et al. (2020b) reported that these
models have an accuracy above 99.5% on their test data set which consists of
real RFI and pulsar candidates. We note that fetch runs on GPUs, which
accelerates the image creation of candidates and the classification
significantly by processing several candidates in parallel. In this process,
each candidate was assigned a probability from 11 separate classifier models.
We first sorted and inspected the candidates based on their probabilities
produced by the classifier Model a, which has the highest prediction accuracy
of 99.8% (see Agarwal et al., 2020b). We also summed up the probabilities from
all 11 classification models to obtain the score (out of 11) for each
candidate and proceeded to sort and visually inspect each one.

We note that both fetch and heimdall have been used in many searches and have
also been integrated into transient search pipelines (e.g. Agarwal et al.,
2020a; Law et al., 2020). As an independent test, we applied our single-pulse
search pipeline on the raw data for RRATs J1908$+$13 and J1924$+$10 that were
recently discovered by the PALFA
collaboration191919http://www.naic.edu/~palfa/newpulsars. Our pipeline re-
detected these sources successfully with high maximum single-pulse S/N values
of 34 and 14 for J1908$+$13 and J1924$+$10, respectively. These S/N ratios are
slightly higher than the discovery-reported S/N values based on the presto-
based (single_pulse_search.py) pipeline (Ransom et al., 2002; Keith et al.,
2011; Ransom, 2011). We note that it is known that the two methods can report
slightly different S/N ratios based on their data processing frameworks; see
Aggarwal et al. (2021) and Gupta et al. (2021) for a detailed discussion.

##  5 Single-pulse candidate results

The data processing produced more than $1.8\times 10^{6}$ single-pulse
candidates with S/N$>$ 6, but we reiterate that the false positive rate of
these candidates is very high. We first sorted the candidates based on the
probability of Model a being greater than $0.5$. This criterion decreased the
number of candidates to 23 771, including only 563 with S/N$>$7\. Out of these
563, the DMs of 401 candidates indicated that they are located beyond the
Galaxy according to the YMW16 model. The visual inspection of these sorted
candidates concluded that there are no potential FRB detections. We also used
the score, which is the sum of probabilities determined from all classifier
models (see Section 4) to sort all our candidates. The number of candidates
with score$>$3 is 32 254, and 5102 of those have S/N$>$7\. Only 920 candidates
with S/N$>$7 have high enough DMs to place them outside our Galaxy, and the
visual inspection determined that there is no potential FRB detection. The S/N
distribution of our candidates is shown in Fig. 6 for different $score$ values
and probabilities of $Model~{}a$ thresholds. We also noticed that strong RFI
signals on March 28 produced many high S/N single-pulse candidates that have
DMs below 20 pc cm-3 in the search results. The classification models in
fetch, however, did not identify them as RFI and provided high probabilities.
We discarded them only after visual inspection. We also ignored the candidates
that have the same DM and are appeared simultaneously in multiple beams as
they are highly like to be due to non-astrophysical signals. We also sorted
out all candidates with Galactic DMs based on the probabilities of Model a and
also the score (see Fig. 6). We visually inspected these candidates, but none
of them showed any evidence of being a credible pulse from a RRAT or a pulsar.

Figure 6: The S/N distribution of single-pulse candidates that are placed
outside (black) or inside (orange) the Galaxy using their DMs based on the
electron density model YMW16. The top panel shows the distribution of
candidates with score$>$3 (solid) and $>$7 (dashed). The bottom panel shows
the distribution for candidates with probabilities $>$0.5 according to the
classification Model a. All the candidates were visually inspected, but no
potential FRBs or RRATs were identified.

The classifier models evaluate the candidates mainly based on their training
data sets and thus, there is a possibility that they can produce false results
(see Agarwal et al., 2020b). Therefore, we visually inspected candidates that
have detection S/N$>$7 regardless of their classifier-model-produced
probabilities, but we still did not find any evidence of a transient
detection. We note there were a few candidates with a reasonably high S/N
detection; however, further examination indicated that these are mainly due to
RFI or random noise. For example, Fig. 7(a) shows a fetch-produced plot for a
high S/N ($\approx$17), low DM candidate. By processing the data around the
same candidate independently with the standard single-pulse routine
(single_pulse_search.py) in presto (Ransom, 2011), we found that it is very
likely due to RFI (see Fig. 7(b) and (c)). We also noticed a series of single
pulses around the same candidate (with DM$<$70 pc cm-3) with slightly
different time stamps. Therefore, we performed a periodicity search using
presto to test whether this emission is from a pulsar, but the analysis
confirmed that this signal is local and due to RFI.

Figure 7: High S/N ($\approx$17) candidate (left panel) in Beam4 on March 22
with a low DM ($\approx$4.8 pc cm3). The left-middle panel shows the candidate
across the frequency band and the left-top panel shows the time series after
averaging the signal across the frequency band. The left-bottom panel shows a
color map of the pulse S/N across the DM-time space around its best reported
DM value of 4.8 pc cm3. The candidate is independently validated using the
single-pulse routine in the presto package and the DM versus time results are
shown in panel (b) and (c). The circles represent a single-pulse detection at
that particular DM and time, and the radius of the circle is scaled according
to the detected S/N. The candidate on panel (a) corresponds to a signal around
the time of $\sim$5.9 s on panel (b), which is shown in red circles. Panel (c)
shows a zoomed-in version of panel (b) around the same candidate, indicating
that the signal is not well constrained in DM and the candidate is likely due
to RFI.

Furthermore, we searched for candidates that have the same sky location
(within the beam width) and similar DM to identify repeaters. Such source can
effectively identify due to its nature of repeating pulses and thus, we used a
lower detection threshold of S/N $=$ 6 in the search. This analysis narrowed
down a few sets of matching candidates, but visual inspection confirmed that
they are likely due to noise and showed no evidence for detection of a
repeater.

The major concern of candidate selection is how to distinguish real
astrophysical signals and random noise when the candidate detection S/N is low
(e.g. $\lessapprox$8). Most of our candidates fall into this S/N region (see
Fig. 6). Yang et al. (2021) recently presented a sample of example FRB
candidates with very low S/N ratios using Parkes telescope archival data. The
only way to prove that these weak events are astrophysical by re-detecting
them in follow-up observations in the future. In order to understand the
nature of candidates produced by random noise in our data, we searched for
single-pulse candidates over ‘negative’ DM values, which are purely ‘non-
astrophysical’ candidates produced by random noise. We selected for this
purpose the $\sim$9 hr data set of Beam0 on March 25, which produced 9003
candidates with S/N$>$6 over the DM range of 1–11 000 pc cm-3 in our standard
search (described in Section 4). We fed the same data set into our pipeline
with the same DM range, but for negative values (i.e., between $-$11 000 to
$-1$ pc cm-3). This analysis produced 10 525 purely non-astrophysical
candidates with S/N$>$6, which is more than the number of candidates found in
the standard search. We also note that these non-astrophysical, random noise
candidates are visually very similar to low S/N candidates in our standard
search. This test suggests that low S/N candidates are not easily
distinguished from real astrophysical signals and random noise. Thus, we
conclude that the low S/N candidates in the distribution are due to random
noise.

##  6 Known pulsars and transients

As shown in Fig. 3, there are five known pulsars that have positions covered
by our observations. However, the single pulses of these sources were not
detected through our pipeline. Perhaps these pulses were too weak to be
detected with the noise level and our detection thresholds. In this section,
we searched for their emission by folding the data using their timing
ephemerides. The basic properties of these sources are given in Table 1. PSR
J1627$+$1419 was within our observations on March 19, and it was detected in
Beam1 with an offset of $0.5^{\prime}$ from the beam centre. This slow pulsar
has a period of 0.491 s (Foster et al., 1995) and it is bright at frequencies
$<$800 MHz with fluxes of 78, 6, and 4 mJy at 150, 430, and 774 MHz,
respectively, resulting in a spectral index of $-1.6\pm 0.3$ (Bilous et al.,
2016; Lewandowski et al., 2004; Han et al., 2009). We extracted the $\sim$13 s
data chunk in which the pulsar crossed Beam1 during its transit. We then
processed this data using the ephemeris of the pulsar (Foster et al., 1995)
via the pulsar signal processing package dspsr (van Straten & Bailes, 2011).
The pulsar emission is seen in the processed data, and the integrated pulse
profile is shown in Fig. 8 (see left panel). We then estimated the flux
density of the pulsar using the radiometer noise given in Equation 2.
Multiplying this equation by $1/G$, where $G$ is the telescope gain, we can
estimate the noise fluctuation in Jy (see Equation 7.12 in Lorimer & Kramer,
2012). Assuming $\Delta f=215$ MHz, $t_{\rm obs}=13$ s, and $G=7.1$ K Jy-1
(see Section 2.2), as well as other standard values given in Appendix A, we
scaled the amplitude of the pulse profile in mJy (see Fig. 8) and then
estimated the mean flux density. The mean flux density we obtained is $0.6\pm
0.1$ mJy, which is within the errors of the expected mean flux density of
$0.9\pm 0.4$ mJy at 1.4 GHz based on the spectral index of $-1.6\pm 0.3$
(Bilous et al., 2016).

Table 1: The properties of known pulsars and FRBs that have positions covered
by our observations. The previously reported mean flux densities $S$ at 1.4
GHz and fluences at 600 MHz of these pulsars and FRBs are given in sixth and
seventh columns, respectively. The last column noted the detectability of
these sources in our data. Note that none of these FRBs have been identified
as repeaters.

Source | Period | DM | $l$ | $b$ | $S$ | Fluence | Detected
---|---|---|---|---|---|---|---
| (ms) | (pc cm-3) | (∘) | (∘) | (mJy) | (Jy ms) |
PSR J0625$+$10 | 498 | 78 | 200.88 |  $-$0.96 | 0.09 | – | Yes
PSR J1329$+$13 | – | 12 | 338 | 73.99 | – | – | No
PSR J1627$+$1419 | 491 | 32.2 | 30.03 | 38.32 | 0.95† | – | Yes
PSR J1651$+$14 | 828 | 48 | 32.88 | 33.07 | – | – | No
PSR J1749+16 | 2312 | 59.6 | 41.21 | 20.90 | – | – | No
FRB20180906A | – | 383.46 | 217.17 | 40.61 | – | 3 | No
FRB20190131C | – | 507.76 | 241.74 | 60.05 | – | 2 | No
FRB20190214C | – | 533.11 | 20.45 | 64.93 | – | 5 | No
FRB20190224C | – | 497.4 | 203.48 | 27.2 | – | 8 | No

†The expected flux density of the pulsar estimated at 1.4 GHz based on its
measured spectral index of $-1.6$ given in Bilous et al. (2016).
---

Figure 8: Detected emission from known pulsars by folding the data using their
timing ephemerides. PSR J1627$+$1419 was within Beam1 (with an offset of
$0.5^{\prime}$ from the beam center) on March 19, and J0625$+$10 was within
Beam3 (with an offset of $1.4^{\prime}$ from the beam center) on January 27.
The top panel shows the integrated pulse profile, folded over 13 s of data,
and the bottom panel shows the emission across the frequency band. The mean
flux densities for J1627$+$1419 and J0625$+$10 are estimated to be $0.6\pm
0.1$ and $0.03\pm 0.1$ mJy, respectively. For clarity, only a portion of the
pulse phase is shown.

PSR J0625$+$10 happened to be within Beam3 on January 27 ($\sim 1.4^{\prime}$
off from the beam center). This pulsar has a spin period of $\sim$0.5 s, DM of
78 pc cm-3 (Camilo et al., 1996), and a very low flux density of 0.09 mJy at
1.4 GHz (Lazarus et al., 2015); see Table 1. We again selected a $\sim$13 s
data section during which the pulsar was within Beam3 and processed it using
the ephemeris of the pulsar. The processed data is shown in Fig. 8 (right
panel), and the observed pulse profile has a S/N of 8. The mean flux density
is estimated to be $0.03\pm 0.10$ mJy, following the same method given above,
which is within the errors of the previously reported value (see Table 8 and
also Lazarus et al., 2015).

We also noticed that PSR J1329$+$13 was within Beam0 (offset by $0.5^{\prime}$
from the beam centre) during the March 17 session based on the pulsar position
reported in Tyul’bashev et al. (2018). This is a RRAT with a DM of $12\pm 2$
pc cm-3, and its spin period is currently unknown. To identify emitted single
pulses from the pulsar in our single-pulse analysis (see Section 4), we
searched over all our candidates to find those that matched the DM and
position of the pulsar. We used a tolerance of $\pm 2$ pc cm-3 in DM to narrow
down any possible candidates and constrained the position to be within our
beam size. However, we did not find any convincing candidates that match the
pulsar DM and position. PSR J1329$+$13 was discovered at a frequency of 111
MHz with a high flux density (Tyul’bashev et al., 2018), suggesting that it
should be detected in our data. That we did not detect this pulsar could imply
that it was in the emission off state when it transited the beam. We further
note that the current position of this pulsar is poorly constrained, RA $=$
13$:$29(2) and Dec $=$ $+$13$:$44(20) (Tyul’bashev et al., 2018), in which
case perhaps it was never actually within the beam.

PSR J1749$+$16 is a 2.3 s pulsar with a DM of 59.6 pc cm-3 (Deneva et al.,
2016); see Table 1. It was in Beam6 on March 23, and while we applied the same
procedure as described above, we did not detect emission from the pulsar. We
note that J1749$+$16 is a nulling pulsar with nulls for tens of seconds,
suggesting that perhaps it was in the null state when it transited across the
beam, resulting in non-detection.

PSR J1651$+$14 is a 0.83 s pulsar with a DM of 48 pc cm-3 (Tyul’bashev et al.,
2017), and it was in Beam6 on March 19. As for other pulsars, we cropped the
data section based on the reported position of the pulsar given in Tyul’bashev
et al. (2017) and then processed the data; however, we did not detect pulsar
emission. We note that the discovery of the pulsar was very weak and carried
out at 111 MHz. No other observations have been reported for this pulsar.
Perhaps the pulsar is intrinsically weak and has a steep spectrum, so that its
emission may not be visible at 1.4 GHz with our 13 s integration time.

The FRBs given in Table 1 and also shown in Fig. 3 also happened to be within
the ALFA beams. In order to check for repeating emission from these FRBs, we
selected the single-pulse candidates produced when these sources were within
the beams and then searched it for candidates with DMs similar to these
sources. However, we could not find any repeating emission from these FRBs in
our analysis. We also note that these sources have not been reported as
repeaters (see The CHIME/FRB Collaboration et al., 2021) and thus it is
unlikely to expect detection of these sources in our data.

Finally, magnetars can also produce FRB-like fast transient signals (see
CHIME/FRB Collaboration et al., 2020; Bochenek et al., 2020; Bailes et al.,
2021). Therefore, we looked for
magnetars202020http://www.physics.mcgill.ca/~pulsar/magnetar/main.html that
were within our observations based on their sky positions (Olausen & Kaspi,
2014; Palliyaguru et al., 2021) in order to search for transient emission from
them. However, none of these sources were within our beams.

##  7 Discussion

We collected 160 hrs of drift-scan data over 23 days in January and March 2020
with the Arecibo telescope. We processed the data and searched for fast
transients, FRBs and RRATs, using a single-pulse pipeline that includes the
heimdall and fetch packages. The pipeline produced over $8\times 10^{6}$
single-pulse candidates, and the neural networks classification models in
fetch reduced this number to $\sim$24 000 “good” candidates with probabilities
$>$0.5. Out of the remaining candidates, only $<$1000 had S/N$>$7 (see Section
5). We proceeded to inspect these candidates manually, but we did not identify
any transient detections. We also searched over all of the candidates to find
repeating transients by matching their DMs and sky locations, but again we
found no evidence of detection for these sources. While there were no
transient detections, we did observe emission from two known pulsars (PSRs
J1627$+$1419 and J0625$+$10), and their measured flux densities are consistent
with the expected values (see Fig. 8 and Section 6).

We finally estimated limits on the FRB event rates based on our observations,
and compared that with other published rate estimates. Given a beam FoV of
$0.022$ deg2 and $\sim$160 hrs of observations in our campaign, we estimate
the upper limit of the FRB event rate as $<$2.8$\times 10^{5}$ sky-1 d-1 at
1.4 GHz above a fluence of 0.16 Jy ms. We also estimated the FRB rate using
the published rates based on the detections from other telescopes. Since the
Parkes telescope detected FRBs at 1.4 GHz and our observations were also at
the same frequency band, we used the Parkes FRB rate to estimate the expected
FRB rate for our observations. Thornton et al. (2013) reported FRB detections
using Parkes and estimated the rate as $10\,000$ sky-1 d-1. Keane & Petroff
(2015) reanalyzed Thornton et al. (2013) results and derived a fluence
complete event rate of 2500 sky-1 d-1 above a fluence of 2 Jy ms. As described
in Section 3, the sensitivity of our observations is estimated at a fluence of
0.16 Jy ms assuming a pulse width of 5 ms. In order to convert the sensitivity
of Parkes to Arecibo, we use the FRB power-law flux distribution $N(>F)\propto
F^{-\gamma}$, assuming a Euclidean Universe with $\gamma=3/2$, where the
sources are assumed to be non-evolving and uniformly distributed in space
(Connor et al., 2016). We also note that $\gamma$ is constrained using the
FRBs detected with the CHIME telescope to be $1.4$ (The CHIME/FRB
Collaboration et al., 2021), which is an excellent match to an Euclidean space
source distribution. Using this flux distribution for the Parkes event rate
with its own fluence given above, we scaled the FRB rate to 1.1$\times 10^{5}$
sky-1 d-1 above our fluence limit of 0.16 Jy ms. The recent FRB detections
reported by CHIME constrained the event rate to be 818 sky-1 d-1 above a
fluence of 5 Jy ms at 600 MHz (The CHIME/FRB Collaboration et al., 2021).
Using the above mentioned flux distribution with a flat spectral index, we
then scaled the event rate to be $1.4\times 10^{5}$ sky-1 d-1 above our
fluence limit of 0.16 Jy ms at 1.4 GHz. The spectral properties of FRBs are
still poorly understood and thus, a flat distribution is a valid assumption
(see Macquart et al., 2019; Farah et al., 2019; The CHIME/FRB Collaboration et
al., 2021, for discussion).

Finally, we estimated the average observing time required for an Arecibo-like
telescope to detect at least one FRB using the event rate of 1.1$\times
10^{5}$ sky-1 d-1 estimated above. Given the ALFA beam FoV of 0.022 deg2, the
average required observation time to detect one FRB is approximately 410
hours. Assuming eight hours of continuous observations every day, we need, on
average, at least 51 days of observations, which is a factor of 2.2 longer
than we spent in our campaign. Note that we conducted these observations
during the observatory closure to fill the downtime of the telescope, so that
we could not observe more than 160 hours to satisfy the above detection
requirement.

Currently, we are processing our data to search for pulsars using periodicity
searches. Even though the sky transit time within our beams is $\sim$13 s, we
emphasize that it is worth searching for pulsars in our data, and bright
pulsars, especially those with short periods, may appear with a reasonable S/N
ratio (see the known pulsar detections in our data that described in Section
8). In parallel to the periodicity search, we have included presto-based
(Ransom et al., 2002; Keith et al., 2011; Ransom, 2011) single-pulse routines
(single_pulse_search.py212121https://github.com/scottransom/presto) in our
pipeline to conduct an independent transients search analysis. The data
processing is underway, and we will relay those results in a future
publication.

We conclude by noting that our transient search pipeline is currently under
modification to perform real-time searches, and it will be tested on the 12-m
telescope at the Arecibo Observatory. This telescope is undergoing an upgrade
to integrate a cooled receiver
system222222http://www.naic.edu/~phil/hardware/12meter/patriot12meter.html,232323http://www.naic.edu/ao/scientist-
user-portal/astronomy/under-development. A significant portion of the
telescope time will be dedicated to real-time transient searches, along with
commensal observations, in the future.

## Data availability

The data underlying this article will be shared on reasonable request to the
corresponding author.

## Acknowledgments

The Arecibo Observatory is operated by the University of Central Florida, Ana
G. Mendez-Universidad Metropolitana, and Yang Enterprises under a cooperative
agreement with the National Science Foundation (NSF; AST-1744119). We thank
Emilie Parent for useful discussions about some of the data processing tools.

## References

  * Agarwal & Aggarwal (2020) Agarwal D., Aggarwal K., 2020, devanshkv/fetch: Software release with the manuscript, doi:10.5281/zenodo.3905437, https://doi.org/10.5281/zenodo.3905437
  * Agarwal et al. (2020a) Agarwal D., et al., 2020a, MNRAS, 497, 352
  * Agarwal et al. (2020b) Agarwal D., Aggarwal K., Burke-Spolaor S., Lorimer D. R., Garver-Daniels N., 2020b, MNRAS, 497, 1661
  * Aggarwal et al. (2021) Aggarwal K., Agarwal D., Lewis E. F., Anna-Thomas R., Cardinal Tremblay J., Burke-Spolaor S., McLaughlin M. A., Lorimer D. R., 2021, arXiv e-prints, p. arXiv:2107.05658
  * Bailes et al. (2021) Bailes M., et al., 2021, MNRAS, 503, 5367
  * Bannister et al. (2017) Bannister K. W., et al., 2017, ApJ, 841, L12
  * Bannister et al. (2019) Bannister K. W., et al., 2019, Science, 365, 565
  * Barsdell et al. (2012) Barsdell B. R., Bailes M., Barnes D. G., Fluke C. J., 2012, MNRAS, 422, 379
  * Beniamini et al. (2020) Beniamini P., Wadiasingh Z., Metzger B. D., 2020, MNRAS, 496, 3390
  * Bethapudi & Desai (2018) Bethapudi S., Desai S., 2018, Astronomy and Computing, 23, 15
  * Bhandari et al. (2021) Bhandari S., et al., 2021, arXiv e-prints, p. arXiv:2108.01282
  * Bilous et al. (2016) Bilous A. V., et al., 2016, A&A, 591, A134
  * Bochenek et al. (2020) Bochenek C. D., Ravi V., Belov K. V., Hallinan G., Kocz J., Kulkarni S. R., McKenna D. L., 2020, Nature, 587, 59
  * CHIME/FRB Collaboration et al. (2018) CHIME/FRB Collaboration et al., 2018, ApJ, 863, 48
  * CHIME/FRB Collaboration et al. (2019a) CHIME/FRB Collaboration et al., 2019a, Nature, 566, 230
  * CHIME/FRB Collaboration et al. (2019b) CHIME/FRB Collaboration et al., 2019b, Nature, 566, 235
  * CHIME/FRB Collaboration et al. (2019c) CHIME/FRB Collaboration et al., 2019c, ApJ, 885, L24
  * CHIME/FRB Collaboration et al. (2020) CHIME/FRB Collaboration et al., 2020, Nature, 587, 54
  * Caleb et al. (2017) Caleb M., et al., 2017, MNRAS, 468, 3746
  * Camilo et al. (1996) Camilo F., Nice D. J., Shrauner J. A., Taylor J. H., 1996, ApJ, 469, 819
  * Chatterjee et al. (2017) Chatterjee S., et al., 2017, Nature, 541, 58
  * Chime/Frb Collaboration et al. (2020) Chime/Frb Collaboration et al., 2020, Nature, 582, 351
  * Choudhury & Padmanabhan (2005) Choudhury T. R., Padmanabhan T., 2005, A&A, 429, 807
  * Condon et al. (1998) Condon J. J., Cotton W. D., Greisen E. W., Yin Q. F., Perley R. A., Taylor G. B., Broderick J. J., 1998, AJ, 115, 1693
  * Connor & van Leeuwen (2018) Connor L., van Leeuwen J., 2018, AJ, 156, 256
  * Connor et al. (2016) Connor L., Lin H.-H., Masui K., Oppermann N., Pen U.-L., Peterson J. B., Roman A., Sievers J., 2016, MNRAS, 460, 1054
  * Connor et al. (2020) Connor L., et al., 2020, MNRAS, 499, 4716
  * Cordes & McLaughlin (2003) Cordes J. M., McLaughlin M. A., 2003, ApJ, 596, 1142
  * Cordes et al. (2006) Cordes J. M., et al., 2006, ApJ, 637, 446
  * Cruces et al. (2021) Cruces M., et al., 2021, MNRAS, 500, 448
  * Deneva et al. (2009) Deneva J. S., et al., 2009, ApJ, 703, 2259
  * Deneva et al. (2016) Deneva J. S., et al., 2016, ApJ, 821, 10
  * Devine et al. (2016) Devine T. R., Goseva-Popstojanova K., McLaughlin M., 2016, MNRAS, 459, 1519
  * Farah et al. (2019) Farah W., et al., 2019, MNRAS, 488, 2989
  * Foster et al. (1995) Foster R. S., Cadwell B. J., Wolszczan A., Anderson S. B., 1995, ApJ, 454, 826
  * Foster et al. (2018) Foster G., et al., 2018, MNRAS, 474, 3847
  * Gajjar et al. (2018) Gajjar V., et al., 2018, ApJ, 863, 2
  * Guo et al. (2017) Guo P., Duan F., Wang P., Yao Y., Yin Q., Xin X., 2017, arXiv e-prints, p. arXiv:1711.10339
  * Gupta et al. (2021) Gupta V., et al., 2021, MNRAS, 501, 2316
  * Han et al. (2009) Han J. L., Demorest P. B., van Straten W., Lyne A. G., 2009, ApJS, 181, 557
  * Heintz et al. (2020) Heintz K. E., et al., 2020, ApJ, 903, 152
  * Ioka (2003) Ioka K., 2003, ApJ, 598, L79
  * Ioka & Zhang (2020) Ioka K., Zhang B., 2020, ApJ, 893, L26
  * Karastergiou et al. (2015) Karastergiou A., et al., 2015, MNRAS, 452, 1254
  * Keane & Petroff (2015) Keane E. F., Petroff E., 2015, MNRAS, 447, 2852
  * Keating et al. (2015) Keating L. C., Haehnelt M. G., Cantalupo S., Puchwein E., 2015, MNRAS, 454, 681
  * Keith et al. (2011) Keith M. J., et al., 2011, MNRAS, 414, 1292
  * Komatsu et al. (2009) Komatsu E., et al., 2009, ApJS, 180, 330
  * Kumar et al. (2019) Kumar P., et al., 2019, ApJ, 887, L30
  * Law et al. (2020) Law C. J., et al., 2020, ApJ, 899, 161
  * Lazarus et al. (2015) Lazarus P., et al., 2015, ApJ, 812, 81
  * Levin et al. (2020) Levin Y., Beloborodov A. M., Bransgrove A., 2020, ApJ, 895, L30
  * Lewandowski et al. (2004) Lewandowski W., Wolszczan A., Feiler G., Konacki M., Sołtysiński T., 2004, ApJ, 600, 905
  * Lorimer (2018) Lorimer D. R., 2018, Nature Astronomy, 2, 860
  * Lorimer & Kramer (2012) Lorimer D. R., Kramer M., 2012, Handbook of Pulsar Astronomy
  * Lorimer et al. (2007) Lorimer D. R., Bailes M., McLaughlin M. A., Narkevic D. J., Crawford F., 2007, Science, 318, 777
  * Luo et al. (2020) Luo R., et al., 2020, Nature, 586, 693
  * Lyutikov & Popov (2020) Lyutikov M., Popov S., 2020, arXiv e-prints, p. arXiv:2005.05093
  * Lyutikov et al. (2020) Lyutikov M., Barkov M. V., Giannios D., 2020, ApJ, 893, L39
  * Macquart et al. (2019) Macquart J. P., Shannon R. M., Bannister K. W., James C. W., Ekers R. D., Bunton J. D., 2019, ApJ, 872, L19
  * Macquart et al. (2020) Macquart J. P., et al., 2020, Nature, 581, 391
  * Marcote et al. (2020) Marcote B., et al., 2020, Nature, 577, 190
  * Masui et al. (2015) Masui K., et al., 2015, Nature, 528, 523
  * McLaughlin et al. (2006) McLaughlin M. A., et al., 2006, Nature, 439, 817
  * Michilli et al. (2018) Michilli D., et al., 2018, Nature, 553, 182
  * Niu et al. (2021) Niu C.-H., et al., 2021, ApJ, 909, L8
  * Olausen & Kaspi (2014) Olausen S. A., Kaspi V. M., 2014, ApJS, 212, 6
  * Palliyaguru et al. (2021) Palliyaguru N. T., Agarwal D., Golpayegani G., Lynch R., Lorimer D. R., Nguyen B., Corsi A., Burke-Spolaor S., 2021, MNRAS, 501, 541
  * Parent et al. (2020) Parent E., et al., 2020, ApJ, 904, 92
  * Parent et al. (2021) Parent E., et al., 2021, arXiv e-prints, p. arXiv:2108.02320
  * Patel et al. (2018) Patel C., et al., 2018, ApJ, 869, 181
  * Piro et al. (2021) Piro L., et al., 2021, arXiv e-prints, p. arXiv:2107.14339
  * Prochaska et al. (2019) Prochaska J. X., et al., 2019, Science, 366, 231
  * Rajwade et al. (2020) Rajwade K. M., et al., 2020, MNRAS, 495, 3551
  * Ransom (2011) Ransom S., 2011, PRESTO: PulsaR Exploration and Search TOolkit (ascl:1107.017)
  * Ransom et al. (2002) Ransom S. M., Eikenberry S. S., Middleditch J., 2002, AJ, 124, 1788
  * Ravi et al. (2019) Ravi V., et al., 2019, Nature, 572, 352
  * Shannon et al. (2018) Shannon R. M., et al., 2018, Nature, 562, 386
  * Sob’yanin (2020) Sob’yanin D. N., 2020, MNRAS, 497, 1001
  * Spitler et al. (2014) Spitler L. G., et al., 2014, ApJ, 790, 101
  * Spitler et al. (2016) Spitler L. G., et al., 2016, Nature, 531, 202
  * Surnis et al. (2019) Surnis M. P., et al., 2019, Publ. Astron. Soc. Australia, 36, e032
  * The CHIME/FRB Collaboration et al. (2021) The CHIME/FRB Collaboration et al., 2021, arXiv e-prints, p. arXiv:2106.04352
  * Thornton et al. (2013) Thornton D., et al., 2013, Science, 341, 53
  * Tyul’bashev et al. (2017) Tyul’bashev S. A., et al., 2017, Astronomy Reports, 61, 848
  * Tyul’bashev et al. (2018) Tyul’bashev S. A., et al., 2018, Astronomy Reports, 62, 63
  * Wagstaff et al. (2016) Wagstaff K. L., et al., 2016, PASP, 128, 084503
  * Wang et al. (2020) Wang F. Y., Wang Y. Y., Yang Y.-P., Yu Y. W., Zuo Z. Y., Dai Z. G., 2020, ApJ, 891, 72
  * Xiao et al. (2021) Xiao D., Wang F., Dai Z., 2021, Science China Physics, Mechanics, and Astronomy, 64, 249501
  * Yang & Zou (2020) Yang H., Zou Y.-C., 2020, ApJ, 893, L31
  * Yang et al. (2021) Yang X., et al., 2021, arXiv e-prints, p. arXiv:2108.00609
  * Yao et al. (2017) Yao J. M., Manchester R. N., Wang N., 2017, ApJ, 835, 29
  * Zhang (2018) Zhang B., 2018, ApJ, 867, L21
  * Zhang (2020a) Zhang B., 2020a, Nature, 587, 45
  * Zhang (2020b) Zhang B., 2020b, ApJ, 890, L24
  * Zhang et al. (2018) Zhang Y. G., Gajjar V., Foster G., Siemion A., Cordes J., Law C., Wang Y., 2018, ApJ, 866, 149
  * Zhu et al. (2014) Zhu W. W., et al., 2014, ApJ, 781, 117
  * Zhu et al. (2020) Zhu W., et al., 2020, ApJ, 895, L6
  * van Straten & Bailes (2011) van Straten W., Bailes M., 2011, Publ. Astron. Soc. Australia, 28, 1

##  Appendix A Telescope gain and stability estimation

The telescope gain was obtained using the time series corresponding to a
spectral channel from the observations during the transit of a continuum
source. The spectral channel was selected in a way such that the data is not
affected by RFI. Since the expected source transit time within our beams is
$\sim$13 s, we average the raw data to make 1 s integration intervals. The
NVSS source catalog was used to identify the sources and obtain their flux
densities (Condon et al., 1998). The sources with flux densities greater than
100 mJy at 1.4 GHz and those fall within $1^{\prime}$ of the beam were
selected. This criteria resulted in at least one source in each beam almost
everyday. This allows us to estimate the gain every day for each beam,
determining the stability of the system. The gains were estimated using two
methods.

The first method uses the radiometer equation, where the expected root mean
square noise fluctuation

$\Delta T_{\rm sys}=\frac{T_{\rm sys}}{\sqrt{n_{\rm p}t_{\rm obs}\Delta
f}}=C\sigma_{\rm p}$ (2)

where $T_{\rm sys}$ is the system temperature, $n_{\rm p}$ is the number of
polarization channels summed, $t_{\rm obs}$ is integration time, $\Delta f$ is
observing bandwidth, and $\sigma_{\rm p}$ is the standard deviation of the
off-source region in the time series. The proportionality constant $C$ is in
units of K per count and used to scale the ordinate of the time series. In the
calculation, we used the typical value $T_{\rm sys}=28$ K of ALFA receiver,
$\Delta f=0.335$ MHz, $n_{\rm p}=2$, and $t_{\rm obs}=1$ s. The constant $C$
is then used to convert the on-source deflection in counts to units of K. The
telescope gain $G$ is the ratio of the deflection in units of K to the flux
density of the source in Jy. The direct estimate of $\sigma_{\rm p}$ from the
time series may affect by the gain variation over time and confusion noise.
Therefore, we computed $\sigma_{\rm p}$ value from the difference between the
time series from adjacent channels, leading to cancel out both these effects.

In the second method, we estimated the gain using the median of the off-source
region of the time series, which is proportional to $T_{\rm sys}$. Thus, the
ratio of $T_{\rm sys}$ to median value gives the conversion factor in units of
K per count. The on-source deflection can then be converted to K using this
factor and the telescope gain can be estimated as described above.

The gain estimated using these two methods are found to be comparable with
each other. Therefore, we averaged the gain obtained from the two methods for
each beam. We note that one of the polarization channels of Beam5 was unstable
and poorly behaved, so that it was ignored in the gain estimation.