using quasar spectra, demonstrating that an accurate photometric NB
measurement of the Ly$\alpha$ forest transmission is practical. We argue that
the most likely systematic is contamination from low-redshift interlopers in
the NB-selected LAE sample, which if not taken into account, would cause a
fictitious Ly$\alpha$ forest transmission. This may explain an offset we see
in the measured values between spectroscopically-confirmed background sources
(DEIMOS10k) and NB-selected background sources (SILVERRUSH $z\simeq 5.7$
LAEs). Fortunately, this can be corrected statistically provided that the low-
redshift interloper fraction is known e.g. from spectroscopic follow-up of a
subset of the population.

We developed a method for measuring the angular LAE-Ly$\alpha$ forest cross-
correlation and the Ly$\alpha$ forest auto-correlation functions. Our method
incorporates the individual posteriors from the Bayesian SED fitting framework
to measure the angular correlation functions consistent with the propagated
error including photometric noise and SED uncertainties. Low-redshift
interlopers in our foreground ($z\simeq 4.9$) and background ($z\simeq 5.7$)
LAE samples are again a main systematic uncertainty which can also be
corrected statistically using a partial spectroscopic follow-up of the parent
LAE samples. Applying the technique to the present data, we did not detect any
angular LAE-Ly$\alpha$ forest cross-correlation and auto-correlation of the
Ly$\alpha$ forest at $z\simeq 4.9$. Our result is consistent with no
Ly$\alpha$ forest fluctuations around LAEs and should be below $58\,\%$ at
$10\,h^{-1}\rm cMpc$ compared to the global mean transmission at $z\simeq
4.9$. We will discuss the physical implications of this limit in a companion
paper (Kakiichi et al in prep).

Finally, we presented a reconstructed 2D tomographic map of the IGM at
$z\simeq 4.9$, co-spatial with the distribution of foreground LAEs in the same
cosmic volume in the COSMOS field. The map embraces a field $140\,h^{-1}\rm
cMpc$ in diameter with a transverse spatial resolution of $\simeq
11\,h^{-1}\rm cMpc$. While the current map is still dominated by photometric
noise, it represents the most detailed tomographic map close to the end of
cosmic reionization. The ability of photometric IGM tomography to map both the
large-scale structures of galaxies and the IGM across a large region of the
sky is extremely appealing, allowing us to apply the technique for many
science cases including constraints on LyC escape fractions, the nature of
ionizing sources through the UV background and thermal fluctuations of the
IGM, and the searches for ionized bubbles, protoclusters, and quasar light-
echoes.

Photometric IGM tomography can be embedded in traditional NB imaging and wide-
field spectrscopic surveys and is applicable at all redshifts from $z\sim 2$
to 6 where NB filters are available, thus making it possible to examine the
co-evolution of galaxies and the cosmic web during the first few billion years
of cosmic history. We argue that the technique can be improved through
extremely-deep NB imaging and large spectroscopic follow-up campaigns.
Although this would require a large, but nonetheless practical, investment of
telescope time with existing 8-10 m telescopes, we make the case that such an
investment is worthwhile. Combining multi-wavelength dataset including the
UltraVISTA JHK and Spitzer/IRAC imaging and soon available JWST/NIRCam imaging
in the COSMOS field will enable us to better control the systematic
uncertainty arising from the intrinsic SEDs of background galaxies. Future
Subaru/PFS surveys will greatly increase the number of spectroscopically-
confirmed background galaxies. Furthermore, a large-scale JWST spectroscopic
survey tiling the COSMOS field would push the redshift frontier closer to the
reionization epoch. The wide-field capability of the photometric IGM
tomography is highly complementary to surveys based on using extremely-deep
spectra with ELT/MOSAICS and TMT/WFOS as their small fields of view require
interesting target regions to be pre-selected, e.g. from photometric IGM
tomographic maps. As such, photometric IGM tomography has great potential to
uncover the physics of galaxy-cosmic web connection in the early Universe in
the coming decade.

## Acknowledgements

KK thanks Harley Katz, Fred Davies, and K-G Lee for discussions and
constructive comments. This project has received funding from the European
Research Council (ERC) under the European Union’s Horizon 2020 research and
innovation programme (grant agreement No 885301). RSE acknowledges financial
support from ERC Advanced Grant FP7/669253. RAM acknowledges support from the
ERC Advanced Grant 740246 (Cosmic Gas). This work is supported by the World
Premier International Research Center Initiative (WPI Initiative), MEXT,
Japan, as well as KAKENHI Grant-in-Aid for Scientific Research (A) (20H00180
and 21H04467) through the Japan Society for the Promotion of Science (JSPS).
This work was supported by the joint research program of the Institute for
Cosmic Ray Research (ICRR), University of Tokyo.

This paper is based on data collected at the Subaru Telescope and retrieved
from the HSC data archive system, which is operated by the Subaru Telescope
and Astronomy Data Center (ADC) at NAOJ. Data analysis was in part carried out
with the cooperation of Center for Computational Astrophysics (CfCA), NAOJ. We
are honored and grateful for the opportunity of observing the Universe from
Maunakea, which has the cultural, historical and natural significance in
Hawaii. The Hyper Suprime-Cam (HSC) collaboration includes the astronomical
communities of Japan and Taiwan, and Princeton University. The HSC
instrumentation and software were developed by the National Astronomical
Observatory of Japan (NAOJ), the Kavli Institute for the Physics and
Mathematics of the Universe (Kavli IPMU), the University of Tokyo, the High
Energy Accelerator Research Organization (KEK), the Academia Sinica Institute
for Astronomy and Astrophysics in Taiwan (ASIAA), and Princeton University.
Funding was contributed by the FIRST program from the Japanese Cabinet Office,
the Ministry of Education, Culture, Sports, Science and Technology (MEXT), the
Japan Society for the Promotion of Science (JSPS), Japan Science and
Technology Agency (JST), the Toray Science Foundation, NAOJ, Kavli IPMU, KEK,
ASIAA, and Princeton University. This paper makes use of software developed
for Vera C. Rubin Observatory. We thank the Rubin Observatory for making their
code available as free software at http://pipelines.lsst.io/.

## Data Availability

All the original HSC data and spectroscopic catalogues including DEIMOS10k are
available online through the HSC-SSP website and NASA/IPAC IRSA (see the links
listed in this paper). The SILVERRUSH catalogue is available at
http://cos.icrr.u-tokyo.ac.jp/rush.html. The table of the measured Ly$\alpha$
forest transmission along our background galaxies is available through online
supplementary material.

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##  Appendix A Derivation of the estimators

For clarity, we explicitly show the derivation of the estimator and the
variance. The mean Ly$\alpha$ forest transmission is given by
$\langle\overline{T}_{\rm IGM}\rangle=\int\overline{T}_{\rm
IGM}P(\overline{T}_{\rm IGM}|\\{f_{{\rm NB},i}^{\rm obs},\bm{f}_{{\rm
BB},i}^{\rm obs}\\}_{i=1,\dots,N_{\rm bg}})d\overline{T}_{\rm IGM}$. By
substituting Equation 13, we have

$\displaystyle\langle\bar{T}_{\rm IGM}\rangle$
$\displaystyle=\int\left(\frac{1}{N_{\rm bg}}\sum_{i=1}^{N_{\rm bg}}T_{{\rm
IGM},i}\right)P(T_{{\rm IGM},i}|f_{{\rm NB},i}^{\rm obs},\bm{f}_{{\rm
BB},i}^{\rm obs})\prod_{i=1}^{N_{\rm bg}}dT_{{\rm IGM},i},$
$\displaystyle=\frac{1}{N_{\rm bg}}\sum_{i=1}^{N_{\rm bg}}\int T_{{\rm
IGM},i}P(T_{{\rm IGM},i}|f_{{\rm NB},i}^{\rm obs},\bm{f}_{{\rm BB},i}^{\rm
obs})dT_{{\rm IGM},i},$ $\displaystyle=\frac{1}{N_{\rm bg}}\sum_{i=1}^{N_{\rm
bg}}\langle T_{{\rm IGM},i}\rangle.$

The variance of the mean Ly$\alpha$ forest transmission is given by
$\sigma^{2}_{\overline{T}_{\rm IGM}}=\int(\overline{T}_{\rm
IGM}-\langle\overline{T}_{\rm IGM}\rangle)^{2}P(\overline{T}_{\rm
IGM}|\\{f_{{\rm NB},i}^{\rm obs},\bm{f}_{{\rm BB},i}^{\rm
obs}\\}_{i=1,\dots,N_{\rm bg}})d\overline{T}_{\rm IGM}$. By substituting
Equation 13 and the above result, we have

$\displaystyle\sigma^{2}_{\overline{T}_{\rm IGM}}=$
$\displaystyle\frac{1}{N^{2}_{\rm bg}}\int\left(\sum_{i=1}^{N_{\rm bg}}T_{{\rm
IGM},i}-\langle T_{{\rm IGM},i}\rangle\right)^{2}P(T_{{\rm IGM},i}|f_{{\rm
NB},i}^{\rm obs},\bm{f}_{{\rm BB},i}^{\rm obs})\prod_{i=1}^{N_{\rm
bg}}dT_{{\rm IGM},i}.$

Since all the individual measurements are uncorrelated, the cross-terms are
zeros, i.e.

$\displaystyle\iint dT_{{\rm IGM},i}dT_{{\rm IGM},j}P(T_{{\rm IGM},i}|f_{{\rm
NB},i}^{\rm obs},\bm{f}_{{\rm BB},i}^{\rm obs})P(T_{{\rm IGM},j}|f_{{\rm
NB},j}^{\rm obs},\bm{f}_{{\rm BB},j}^{\rm obs})$
$\displaystyle\times\left(T_{{\rm IGM},i}-\langle T_{{\rm
IGM},i}\rangle\right)\left(T_{{\rm IGM},j}-\langle T_{{\rm
IGM},j}\rangle\right)=0.$

Thus, we obtain

$\displaystyle\sigma^{2}_{\overline{T}_{\rm IGM}}=$
$\displaystyle\frac{1}{N_{\rm bg}^{2}}\sum_{i=1}^{N_{\rm bg}}\int\left(T_{{\rm
IGM},i}-\langle T_{{\rm IGM},i}\rangle\right)^{2}P(T_{{\rm IGM},i}|f_{{\rm
NB},i}^{\rm obs},\bm{f}_{{\rm BB},i}^{\rm obs})dT_{{\rm IGM},i}.$

The calculation for the angular averaged Ly$\alpha$ forest transmission around
galaxies is identical to that for the estimator and the variance for the mean
Ly$\alpha$ forest transmission for each angular bin. Thus, we obtain the
estimator,

$\displaystyle\langle\overline{T}_{\rm IGM}(\theta)\rangle=$
$\displaystyle\frac{1}{N_{\rm pair}(\theta)}\sum_{j=1}^{N_{\rm
fg}}\sum_{i=1}^{N_{\rm bg}}\mathcal{I}(|\theta-\theta_{ij}|)$
$\displaystyle~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}\times\int T_{{\rm
IGM},i}P(T_{{\rm IGM},i}|f_{{\rm NB},i}^{\rm obs},\bm{f}_{{\rm BB},i}^{\rm
obs})dT_{{\rm IGM},i},$

and the variance,

$\displaystyle{\rm Var}[\overline{T}_{\rm IGM}(\theta)]=\frac{1}{N_{\rm
pair}(\theta)^{2}}\sum_{j=1}^{N_{\rm fg}}\sum_{i=1}^{N_{\rm
bg}}\mathcal{I}(|\theta-\theta_{ij}|)$
$\displaystyle~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}\times\int(T_{{\rm
IGM},i}-\langle T_{{\rm IGM},i}\rangle)^{2}P(T_{{\rm IGM},i}|f_{{\rm
NB},i}^{\rm obs},\bm{f}_{{\rm BB},i}^{\rm obs})dT_{{\rm IGM},i}.$

##  Appendix B Interloper contamination

The contamination by low-redshift interlopers in the foreground and background
LAE samples dilutes the angular mean Ly$\alpha$ forest transmission around the
foreground LAEs. When we measure the observed angular mean Ly$\alpha$ forest
transmission around LAEs using the background LAE sample, one can be
decomposed the estimator into four different contributions: (1) true
foreground LAE - true background LAE pairs (fg-bg pairs), (2) foreground
interloper - true background LAE pairs (fg.int-bg pairs), (3) true foreground
LAE - background interloper pairs (fg-bg.int pairs), and (4) foreground
interloper - background interloper pairs (fg.int-bg.int pairs),

$\displaystyle\overline{T}_{\rm IGM}(\theta)=\frac{1}{N_{\rm
pair}(\theta)}\times$ $\displaystyle~{}~{}\left(\mathop{\sum^{N^{\rm
true}_{\rm fg}}\sum^{N^{\rm true}_{\rm bg}}}_{\begin{subarray}{c}\rm fg-
bg\,pairs\end{subarray}}T_{{\rm IGM},i}^{\rm
true}\mathcal{I}(|\theta-\theta_{ij}|)+\\!\\!\mathop{\sum^{N^{\rm int}_{\rm
fg}}\sum^{N^{\rm true}_{\rm bg}}}_{\begin{subarray}{c}\rm fg.int-
bg\,pairs\end{subarray}}T_{{\rm IGM},i}^{\rm
true}\mathcal{I}(|\theta-\theta_{ij}|)\right.$
$\displaystyle\left.+\\!\\!\mathop{\sum^{N^{\rm true}_{\rm fg}}\sum^{N^{\rm
int}_{\rm bg}}}_{\begin{subarray}{c}\rm fg-
bg.int\,pairs\end{subarray}}\\!\\!T_{{\rm IGM},i}^{\rm
bg.int}\mathcal{I}(|\theta-\theta_{ij}|)+\\!\\!\\!\\!\mathop{\sum^{N^{\rm
int}_{\rm fg}}\sum^{N^{\rm int}_{\rm bg}}}_{\begin{subarray}{c}\rm fg.int-
bg.int\,pairs\end{subarray}}\\!\\!\\!\\!T_{{\rm IGM},i}^{\rm
bg.int}\mathcal{I}(|\theta-\theta_{ij}|)\right),$

where $N_{\rm pairs}(\theta)$ is the total number of observed pairs per
angular bin $N_{\rm pairs}(\theta)$, $N_{\rm fg}^{\rm true}$ and $N_{\rm
bg}^{\rm true}$ are the number of true foreground and background LAEs, $N_{\rm
fg}^{\rm int}$ and $N_{\rm bg}^{\rm int}$ are the number of foreground and
background interlopers, and $T_{{\rm IGM},i}^{\rm true}$ and $T_{{\rm
IGM},i}^{\rm bg.int}$ are the measured Ly$\alpha$ forest transmission along
true background LAEs and background LAE interlopers. The expectation value of
the angular averaged Ly$\alpha$ forest transmission around LAEs is then given
by

$\displaystyle\langle\overline{T}_{\rm IGM}(\theta)\rangle$
$\displaystyle=\frac{N_{\rm pair}^{\rm fg-bg\,pairs}(\theta)}{N_{\rm
pair}(\theta)}\langle\overline{T}_{\rm IGM}^{\rm true}(\theta)\rangle$
$\displaystyle+\frac{N_{\rm pair}^{\rm fg.int-bg\,pairs}(\theta)}{N_{\rm
pair}(\theta)}\langle\overline{T}_{\rm IGM}^{\rm true}(\theta)\rangle_{\rm
fg.int-bg\,pairs}$ $\displaystyle+\frac{N_{\rm pair}^{\rm fg-
bg.int\,pairs}(\theta)}{N_{\rm pair}(\theta)}\langle\overline{T}_{\rm
IGM}^{\rm bg.int}(\theta)\rangle_{\rm fg-bg,int\,pairs}$
$\displaystyle+\frac{N_{\rm pair}^{\rm fg.int-bg.int\,pairs}(\theta)}{N_{\rm
pair}(\theta)}\langle\overline{T}_{\rm IGM}^{\rm bg.int}(\theta)\rangle_{\rm
fg.int-bg.int\,pairs},$

where $N_{\rm pairs}^{\rm fg-bg\,pairs}(\theta)$, $N_{\rm pairs}^{\rm fg.int-
bg\,pairs}(\theta)$, $N^{\rm fg-bg.int\,pairs}_{\rm pairs}(\theta)$, and
$N^{\rm fg.int-bg.int\,pairs}_{\rm pairs}(\theta)$ are the number of
foreground LAE - background LAE, foreground LAE interloper - background LAE,
foreground LAE - background LAE interloper, and foreground LAE interloper -
background LAE interloper pairs per bin.

The total number of observed pairs depends on the angular correlation function
of the observed populations $\omega_{\rm fg-bg}^{\rm obs}(\theta)$,

$N_{\rm pairs}(\theta)=N_{\rm fg}^{\rm obs}N_{\rm bg}^{\rm
obs}\left[1+\omega_{\rm fg-bg}^{\rm
obs}(\theta)\right]\frac{2\pi\theta\Delta\theta}{\Omega_{\rm survey}}$ (47)

where $\Omega_{\rm survey}$ is the survey area and $\Delta\theta$ is the
angular bin size. Similarly, the number of true foreground LAE - true
background LAE pairs etc is given by

$\displaystyle N_{\rm pairs}^{\rm fg-bg\,pairs}(\theta)=N_{\rm fg}^{\rm
true}N_{\rm bg}^{\rm true}\left[1+\omega_{\rm fg-
bg}(\theta)\right]\frac{2\pi\theta\Delta\theta}{\Omega_{\rm survey}},$
$\displaystyle N_{\rm pairs}^{\rm fg.int-bg\,pairs}(\theta)=N_{\rm fg}^{\rm
int}N_{\rm bg}^{\rm true}\left[1+\omega_{\rm fg.int-
bg}(\theta)\right]\frac{2\pi\theta\Delta\theta}{\Omega_{\rm survey}},$
$\displaystyle N_{\rm pairs}^{\rm fg-bg.int\,pairs}(\theta)=N_{\rm fg}^{\rm
true}N_{\rm bg}^{\rm int}\left[1+\omega_{\rm fg-
bg.int}(\theta)\right]\frac{2\pi\theta\Delta\theta}{\Omega_{\rm survey}},$
$\displaystyle N_{\rm pairs}^{\rm fg.int-bg.int\,pairs}(\theta)=N_{\rm
fg}^{\rm int}N_{\rm bg}^{\rm int}\left[1+\omega_{\rm fg.int-
bg.int}(\theta)\right]\frac{2\pi\theta\Delta\theta}{\Omega_{\rm survey}}.$

Defining the interloper fractions in the observed foreground and background
LAE samples to be $f_{\rm fg.int}=N_{\rm fg}^{\rm int}/N^{\rm obs}_{\rm fg}$
and $f_{\rm bg.int}=N_{\rm bg}^{\rm int}/N^{\rm obs}_{\rm bg}$, the observed
angular cross-correlation function from the foreground and background LAE
samples has contributions from true and interloper pairs,

$\displaystyle\omega_{\rm fg-bg}^{\rm obs}(\theta)$ $\displaystyle=(1-f_{\rm
fg.int})(1-f_{\rm bg.int})\omega_{\rm fg-bg}(\theta)$ $\displaystyle+f_{\rm
fg.int}(1-f_{\rm bg.int})\omega_{\rm fg.int-bg}(\theta)$ $\displaystyle+f_{\rm
bg.int}(1-f_{\rm fg.int})\omega_{\rm fg-bg.int}(\theta)$ $\displaystyle+f_{\rm
bg.int}f_{\rm fg.int}\omega_{\rm fg.int-bg.int}(\theta).$

Since both interlopers and true LAEs in the two different NB-selected samples
are located at different redshifts, all these angular correlation functions
should be zeros. The objects in foreground and background LAE samples are not
spatially correlated to each other. Thus,

$\displaystyle\langle\overline{T}_{\rm IGM}^{\rm true}(\theta)\rangle_{\rm
fg.int-bg\,pairs}\approx\langle\overline{T}_{\rm IGM}^{\rm true}\rangle,$
$\displaystyle\langle\overline{T}_{\rm IGM}^{\rm bg.int}(\theta)\rangle_{\rm
fg-bg,int\,pairs}\approx\langle\overline{T}_{\rm IGM}^{\rm bg.int}\rangle,$
$\displaystyle\langle\overline{T}_{\rm IGM}^{\rm bg.int}(\theta)\rangle_{\rm
fg.int-bg.int\,pairs}\approx\langle\overline{T}_{\rm IGM}^{\rm
bg.int}\rangle.$

Therefore, we find that the effect of the interloper contamination on the
observed angular-averaged Ly$\alpha$ forest transmission profile is expressed
as

$\displaystyle\langle\overline{T}_{\rm IGM}(\theta)\rangle$
$\displaystyle=(1-f_{\rm fg.int})(1-f_{\rm bg.int})\langle\overline{T}_{\rm
IGM}^{\rm true}(\theta)\rangle$ $\displaystyle+f_{\rm fg.int}(1-f_{\rm
bg.int})\langle\overline{T}_{\rm IGM}^{\rm true}\rangle$ $\displaystyle+f_{\rm
bg.int}(1-f_{\rm fg.int})\langle\overline{T}_{\rm IGM}^{\rm bg.int}\rangle$
$\displaystyle+f_{\rm fg.int}f_{\rm bg.int}\langle\overline{T}_{\rm IGM}^{\rm
bg.int}\rangle.$

As the observed LAE-Ly$\alpha$ forest cross-correlation is defined with
respect to the observed mean Ly$\alpha$ forest transmission which is also
affected by the interlopers in the background LAE sample, i.e.
$\langle\overline{T}_{\rm IGM}^{\rm obs}\rangle=(1-f_{\rm
bg.int})\langle\overline{T}_{\rm IGM}\rangle^{\rm true}+f_{\rm
bg.int}\langle\overline{T}_{\rm IGM}\rangle^{\rm bg.int}$, we find

$\displaystyle\omega^{\rm obs}_{\rm g\alpha}(\theta)$
$\displaystyle=\frac{\langle\overline{T}^{\rm obs}_{\rm
IGM}(\theta)\rangle}{\langle\overline{T}^{\rm obs}_{\rm IGM}\rangle}-1$
$\displaystyle=\frac{(1-f_{\rm fg.int})(1-f_{\rm
bg.int})\langle\overline{T}_{\rm IGM}^{\rm true}\rangle}{(1-f_{\rm
bg.int})\langle\overline{T}_{\rm IGM}^{\rm true}\rangle+f_{\rm
bg.int}\langle\overline{T}^{\rm bg.int}_{\rm IGM}\rangle}\omega^{\rm
true}_{\rm g\alpha}(\theta),$

where $\omega_{\rm g\alpha}^{\rm true}(\theta)=\langle\overline{T}_{\rm
IGM}^{\rm true}(\theta)\rangle/\langle\overline{T}_{\rm IGM}^{\rm
true}\rangle-1$.

## Online supplementary material: Table of the estimated Ly$\alpha$ forest
transmission

ID | Object name | RA | DEC | $T_{\rm IGM}$ | $M_{\mbox{\tiny UV}}$ | $\beta$ | Note
---|---|---|---|---|---|---|---
1 | DEIMOS_2018_L222036 | 10h02m20.94s | +01d37m06.78s | $+0.049_{-0.194}^{+0.196}$ | $-20.65_{-0.13}^{+0.13}$ | $-1.56_{-1.67}^{+1.65}$ |
2 | DEIMOS_2018_L416105 | 10h02m45.66s | +01d55m35.91s | $+0.531_{-0.176}^{+0.178}$ | $-21.18_{-0.12}^{+0.12}$ | $-1.31_{-1.07}^{+1.07}$ |
3 | DEIMOS_2018_L244697 | 10h01m59.64s | +01d39m16.81s | $+0.045_{-0.064}^{+0.064}$ | $-21.45_{-0.02}^{+0.02}$ | $-1.69_{-0.70}^{+0.70}$ |
4 | DEIMOS_2018_L254463 | 10h01m58.94s | +01d40m10.67s | $+0.098_{-0.049}^{+0.049}$ | $-21.62_{-0.02}^{+0.02}$ | $-2.39_{-0.62}^{+0.63}$ |
5 | DEIMOS_2018_L263567 | 10h01m35.65s | +01d41m08.01s | $+0.070_{-0.141}^{+0.139}$ | $-20.44_{-0.18}^{+0.18}$ | $-2.81_{-1.49}^{+1.47}$ |
6 | DEIMOS_2018_L298678 | 10h01m26.89s | +01d44m30.15s | $+0.168_{-0.269}^{+0.277}$ | $-20.61_{-0.19}^{+0.19}$ | $-0.40_{-1.62}^{+1.61}$ |
7 | DEIMOS_2018_L351640 | 10h01m29.08s | +01d49m29.63s | $-0.016_{-0.161}^{+0.160}$ | $-21.09_{-0.13}^{+0.13}$ | $-0.45_{-1.12}^{+1.14}$ |
8 | DEIMOS_2018_L516019 | 10h01m25.05s | +02d05m08.24s | $+0.286_{-0.069}^{+0.068}$ | $-21.47_{-0.03}^{+0.03}$ | $-1.84_{-0.57}^{+0.56}$ |
9 | DEIMOS_2018_L180331 | 10h00m59.30s | +01d33m19.51s | $+0.143_{-0.057}^{+0.057}$ | $-21.95_{-0.10}^{+0.10}$ | $-2.25_{-0.85}^{+0.85}$ |
10 | DEIMOS_2018_L194207 | 10h00m46.05s | +01d34m35.69s | $+0.395_{-0.157}^{+0.158}$ | $-21.36_{-0.16}^{+0.17}$ | $-1.93_{-1.44}^{+1.41}$ |
11 | DEIMOS_2018_C532566 | 10h01m07.35s | +01d52m22.72s | $-0.077_{-0.456}^{+0.450}$ | $-19.62_{-0.29}^{+0.28}$ | $-1.33_{-2.25}^{+2.20}$ |
12 | DEIMOS_2018_L424749 | 10h00m43.13s | +01d56m26.91s | $+0.383_{-0.262}^{+0.264}$ | $-20.87_{-0.07}^{+0.07}$ | $+0.97_{-0.80}^{+0.78}$ |
13 | DEIMOS_2018_L549131 | 10h00m42.94s | +02d08m12.24s | $+0.097_{-0.045}^{+0.044}$ | $-21.74_{-0.04}^{+0.04}$ | $-2.73_{-0.46}^{+0.45}$ |
14 | DEIMOS_2018_L627591 | 10h01m09.72s | +02d15m13.46s | $+0.340_{-0.244}^{+0.255}$ | $-20.73_{-0.21}^{+0.21}$ | $-0.48_{-1.79}^{+1.72}$ |
15 | DEIMOS_2018_L773957 | 10h01m10.06s | +02d28m28.98s | $+0.345_{-0.092}^{+0.091}$ | $-21.50_{-0.10}^{+0.10}$ | $-2.44_{-0.89}^{+0.89}$ |
16 | DEIMOS_2018_L769394 | 10h01m02.15s | +02d28m04.09s | $+0.314_{-0.099}^{+0.097}$ | $-21.29_{-0.06}^{+0.06}$ | $-2.34_{-1.01}^{+1.00}$ |
17 | DEIMOS_2018_L196471 | 10h00m21.10s | +01d34m46.68s | $+0.099_{-0.056}^{+0.056}$ | $-21.39_{-0.03}^{+0.03}$ | $-2.74_{-0.66}^{+0.65}$ |
18 | DEIMOS_2018_L328419 | 09h59m55.00s | +01d47m20.69s | $-0.141_{-0.162}^{+0.160}$ | $-20.75_{-0.16}^{+0.16}$ | $-1.38_{-1.34}^{+1.33}$ |
19 | DEIMOS_2018_L441897 | 10h00m17.67s | +01d58m07.07s | $+0.206_{-0.078}^{+0.078}$ | $-21.12_{-0.03}^{+0.03}$ | $-2.39_{-0.85}^{+0.85}$ |
20 | DEIMOS_2018_L443917 | 10h00m19.33s | +01d58m19.33s | $+0.266_{-0.157}^{+0.157}$ | $-20.55_{-0.05}^{+0.05}$ | $-1.61_{-1.37}^{+1.34}$ |
21 | DEIMOS_2018_L494763 | 10h00m05.11s | +02d03m12.17s | $+0.033_{-0.082}^{+0.083}$ | $-21.19_{-0.04}^{+0.04}$ | $-1.59_{-0.83}^{+0.84}$ |
22 | DEIMOS_2018_L536534 | 09h59m53.24s | +02d07m05.43s | $+0.139_{-0.048}^{+0.048}$ | $-22.05_{-0.05}^{+0.05}$ | $-1.41_{-0.45}^{+0.45}$ |
23 | DEIMOS_2018_L586681 | 10h00m08.78s | +02d11m36.41s | $+0.268_{-0.119}^{+0.120}$ | $-21.70_{-0.18}^{+0.18}$ | $-1.32_{-1.49}^{+1.48}$ |
24 | DEIMOS_2018_L629750 | 10h00m34.32s | +02d15m24.52s | $+0.109_{-0.093}^{+0.095}$ | $-21.08_{-0.11}^{+0.11}$ | $-1.68_{-1.82}^{+1.84}$ |
25 | DEIMOS_2018_L740404 | 10h00m02.31s | +02d25m24.02s | $+0.286_{-0.062}^{+0.062}$ | $-21.67_{-0.02}^{+0.02}$ | $-1.72_{-0.58}^{+0.58}$ |
26 | DEIMOS_2018_L848185 | 10h00m21.50s | +02d35m11.05s | $+0.072_{-0.028}^{+0.028}$ | $-22.28_{-0.03}^{+0.03}$ | $-2.06_{-0.43}^{+0.44}$ |
27 | DEIMOS_2018_L873321 | 10h00m04.06s | +02d37m35.77s | $+0.224_{-0.047}^{+0.047}$ | $-21.89_{-0.03}^{+0.03}$ | $-2.41_{-0.60}^{+0.61}$ |
28 | DEIMOS_2018_L869970 | 10h00m24.98s | +02d37m18.23s | $+0.263_{-0.139}^{+0.140}$ | $-21.07_{-0.06}^{+0.06}$ | $-0.72_{-1.22}^{+1.23}$ |
29 | DEIMOS_2018_L910650 | 10h00m22.50s | +02d41m03.28s | $+0.012_{-0.083}^{+0.082}$ | $-20.84_{-0.15}^{+0.15}$ | $-3.01_{-1.31}^{+1.29}$ |
30 | DEIMOS_2018_L166586 | 09h59m46.71s | +01d32m08.47s | $+0.143_{-0.095}^{+0.093}$ | $-20.90_{-0.15}^{+0.15}$ | $-3.30_{-1.20}^{+1.20}$ |
31 | DEIMOS_2018_L372292 | 09h59m39.16s | +01d51m28.19s | $+0.175_{-0.101}^{+0.101}$ | $-21.21_{-0.04}^{+0.04}$ | $-0.97_{-0.83}^{+0.83}$ |
32 | DEIMOS_2018_L503575 | 09h58m53.26s | +02d04m01.25s | $+0.468_{-0.249}^{+0.253}$ | $-20.48_{-0.20}^{+0.20}$ | $-1.81_{-1.81}^{+1.77}$ |
33 | DEIMOS_2018_L519281 | 09h59m00.89s | +02d05m27.63s | $+0.172_{-0.056}^{+0.056}$ | $-21.65_{-0.07}^{+0.07}$ | $-2.46_{-0.68}^{+0.68}$ |
34 | DEIMOS_2018_L539609 | 09h59m07.26s | +02d07m21.30s | $+0.203_{-0.038}^{+0.038}$ | $-22.13_{-0.02}^{+0.02}$ | $-1.88_{-0.39}^{+0.39}$ |
35 | DEIMOS_2018_L552206 | 09h58m26.78s | +02d08m27.20s | $+0.188_{-0.100}^{+0.100}$ | $-21.67_{-0.09}^{+0.09}$ | $-0.73_{-1.32}^{+1.35}$ |
36 | DEIMOS_2018_L558430 | 09h59m02.11s | +02d09m02.49s | $+0.079_{-0.039}^{+0.039}$ | $-21.92_{-0.05}^{+0.05}$ | $-2.57_{-0.98}^{+0.94}$ |

ID | Object name | RA (J2000) | DEC (J2000) | $T_{\rm IGM}$ | $M_{\mbox{\tiny UV}}$ | $\beta$ | Note
---|---|---|---|---|---|---|---
1 | SILVERRUSH_2021_15477 | 09h57m44.50s | +02d16m39.00s | $+3.004_{-1.648}^{+1.693}$ | $-19.47_{-0.32}^{+0.32}$ | $-1.33_{-2.32}^{+2.27}$ |  $94\%$ outlier
2 | SILVERRUSH_2021_14862 | 09h57m47.81s | +02d16m18.53s | $+1.431_{-0.798}^{+0.826}$ | $-19.83_{-0.27}^{+0.27}$ | $-0.87_{-2.16}^{+2.04}$ |  $63\%$ outlier
3 | SILVERRUSH_2021_5731 | 09h57m48.23s | +02d33m56.51s | $+0.604_{-0.381}^{+0.401}$ | $-20.57_{-0.26}^{+0.27}$ | $-1.15_{-2.17}^{+2.12}$ |
4 | SILVERRUSH_2021_18336 | 09h58m11.04s | +02d45m50.21s | $+0.621_{-0.455}^{+0.481}$ | $-20.33_{-0.28}^{+0.28}$ | $-1.47_{-2.22}^{+2.21}$ |
5 | SILVERRUSH_2021_10859 | 09h58m12.36s | +02d03m09.22s | $-0.006_{-0.368}^{+0.372}$ | $-20.31_{-0.21}^{+0.22}$ | $-0.52_{-1.82}^{+1.77}$ |
6 | SILVERRUSH_2021_18574 | 09h58m27.78s | +02d39m56.75s | $+1.367_{-0.691}^{+0.700}$ | $-20.13_{-0.23}^{+0.24}$ | $-0.24_{-1.80}^{+1.68}$ |  $66\%$ outlier
7 | SILVERRUSH_2021_6822 | 09h58m31.95s | +01d49m20.00s | $+0.054_{-0.610}^{+0.619}$ | $-19.96_{-0.23}^{+0.23}$ | $+0.22_{-1.51}^{+1.41}$ |
8 | SILVERRUSH_2021_11514 | 09h58m33.35s | +02d36m54.65s | $+0.650_{-0.589}^{+0.623}$ | $-20.03_{-0.23}^{+0.23}$ | $+0.05_{-1.61}^{+1.52}$ |
9 | SILVERRUSH_2021_30501 | 09h58m35.63s | +01d42m12.81s | $-0.459_{-0.423}^{+0.403}$ | $-20.25_{-0.22}^{+0.22}$ | $-0.86_{-1.98}^{+1.90}$ |
10 | SILVERRUSH_2021_11470 | 09h58m36.02s | +02d36m53.34s | $+0.851_{-0.568}^{+0.589}$ | $-19.88_{-0.27}^{+0.28}$ | $-0.98_{-2.15}^{+2.05}$ |
11 | SILVERRUSH_2021_16049 | 09h58m39.00s | +02d40m29.31s | $+0.212_{-0.723}^{+0.731}$ | $-19.64_{-0.29}^{+0.29}$ | $-0.20_{-1.89}^{+1.71}$ |
12 | SILVERRUSH_2021_14954 | 09h58m48.41s | +02d16m30.33s | $+0.655_{-0.359}^{+0.366}$ | $-20.88_{-0.21}^{+0.21}$ | $-0.60_{-1.92}^{+1.85}$ |
13 | SILVERRUSH_2021_29922 | 09h58m50.72s | +01d41m48.91s | $+0.320_{-0.345}^{+0.351}$ | $-19.99_{-0.24}^{+0.23}$ | $-1.90_{-2.07}^{+2.14}$ |
14 | SILVERRUSH_2021_10114 | 09h58m53.35s | +02d02m32.42s | $+0.970_{-0.307}^{+0.311}$ | $-21.20_{-0.10}^{+0.11}$ | $+0.80_{-0.87}^{+0.87}$ |
15 | SILVERRUSH_2021_12010 | 09h59m03.53s | +02d48m54.26s | $+0.478_{-0.424}^{+0.445}$ | $-20.14_{-0.29}^{+0.28}$ | $-1.26_{-2.25}^{+2.20}$ |
16 | SILVERRUSH_2021_16825 | 09h59m07.45s | +02d50m32.69s | $+0.168_{-0.095}^{+0.096}$ | $-21.38_{-0.15}^{+0.15}$ | $-2.33_{-1.28}^{+1.25}$ |
17 | SILVERRUSH_2021_9749 | 09h59m12.55s | +02d13m30.14s | $+0.221_{-0.149}^{+0.150}$ | $-20.57_{-0.15}^{+0.15}$ | $-2.23_{-1.33}^{+1.29}$ |
18 | SILVERRUSH_2021_13613 | 09h59m13.96s | +02d38m09.57s | $+0.357_{-0.375}^{+0.395}$ | $-20.44_{-0.19}^{+0.19}$ | $+0.08_{-1.52}^{+1.45}$ |
19 | SILVERRUSH_2021_9791 | 09h59m16.76s | +02d47m11.84s | $+0.128_{-0.321}^{+0.316}$ | $-19.84_{-0.26}^{+0.25}$ | $-2.29_{-1.97}^{+2.05}$ |
20 | SILVERRUSH_2021_7633 | 09h59m21.03s | +01d49m50.58s | $-0.507_{-0.455}^{+0.439}$ | $-19.72_{-0.27}^{+0.27}$ | $-1.68_{-2.22}^{+2.22}$ |
21 | SILVERRUSH_2021_2289 | 09h59m24.91s | +02d31m40.21s | $+0.301_{-0.120}^{+0.116}$ | $-20.52_{-0.14}^{+0.13}$ | $-3.62_{-1.03}^{+1.07}$ |
22 | SILVERRUSH_2021_20291 | 09h59m31.84s | +01d45m46.94s | $+1.024_{-0.607}^{+0.646}$ | $-19.86_{-0.27}^{+0.27}$ | $-1.30_{-2.22}^{+2.16}$ |
23 | SILVERRUSH_2021_7234 | 09h59m32.63s | +01d38m28.70s | $-0.136_{-0.602}^{+0.590}$ | $-19.83_{-0.27}^{+0.27}$ | $-0.22_{-1.88}^{+1.72}$ |
24 | SILVERRUSH_2021_4409 | 09h59m38.56s | +01d36m52.89s | $+0.896_{-0.327}^{+0.327}$ | $-20.87_{-0.17}^{+0.17}$ | $-1.51_{-1.45}^{+1.42}$ |
25 | SILVERRUSH_2021_7785 | 09h59m39.98s | +02d12m22.68s | $+1.660_{-1.013}^{+1.050}$ | $-19.24_{-0.29}^{+0.29}$ | $-1.52_{-2.25}^{+2.23}$ | 65% outlier
26 | SILVERRUSH_2021_26324 | 09h59m40.74s | +01d28m57.84s | $+0.460_{-0.183}^{+0.183}$ | $-21.13_{-0.15}^{+0.16}$ | $-2.28_{-1.42}^{+1.38}$ |
27 | SILVERRUSH_2021_29198 | 09h59m40.78s | +01d30m26.06s | $+0.121_{-0.418}^{+0.428}$ | $-20.33_{-0.24}^{+0.25}$ | $-0.31_{-1.85}^{+1.74}$ |
28 | SILVERRUSH_2021_8381 | 09h59m44.34s | +01d39m08.55s | $+1.111_{-0.762}^{+0.790}$ | $-19.84_{-0.26}^{+0.26}$ | $-0.05_{-1.78}^{+1.61}$ |
29 | SILVERRUSH_2021_30551 | 09h59m44.66s | +01d31m16.69s | $-0.218_{-0.364}^{+0.357}$ | $-20.12_{-0.27}^{+0.26}$ | $-1.47_{-2.20}^{+2.18}$ |
30 | SILVERRUSH_2021_30156 | 09h59m44.74s | +01d29m06.98s | $+1.126_{-0.610}^{+0.653}$ | $-20.23_{-0.24}^{+0.24}$ | $-1.68_{-2.14}^{+2.16}$ |
31 | SILVERRUSH_2021_35601 | 09h59m46.72s | +01d32m08.32s | $+0.153_{-0.112}^{+0.109}$ | $-20.87_{-0.16}^{+0.16}$ | $-2.96_{-1.36}^{+1.35}$ |
32 | SILVERRUSH_2021_18016 | 09h59m47.08s | +02d07m04.45s | $+0.460_{-0.415}^{+0.445}$ | $-20.12_{-0.23}^{+0.24}$ | $-0.67_{-1.99}^{+1.89}$ |
33 | SILVERRUSH_2021_5959 | 09h59m47.39s | +02d22m16.98s | $+0.051_{-0.424}^{+0.430}$ | $-19.80_{-0.27}^{+0.28}$ | $-1.12_{-2.19}^{+2.10}$ |
34 | SILVERRUSH_2021_16681 | 09h59m47.55s | +01d44m49.35s | $+1.747_{-0.940}^{+0.937}$ | $-19.81_{-0.27}^{+0.28}$ | $-0.26_{-1.90}^{+1.73}$ | 76% outlier
35 | SILVERRUSH_2021_13964 | 09h59m48.41s | +02d04m38.14s | $-0.323_{-0.446}^{+0.430}$ | $-20.26_{-0.20}^{+0.21}$ | $+0.07_{-1.57}^{+1.48}$ |
36 | SILVERRUSH_2021_8018 | 09h59m52.03s | +01d50m05.75s | $+0.304_{-0.153}^{+0.154}$ | $-21.19_{-0.13}^{+0.13}$ | $-0.83_{-1.09}^{+1.10}$ |
37 | SILVERRUSH_2021_16665 | 09h59m53.25s | +02d07m05.34s | $+0.134_{-0.050}^{+0.049}$ | $-22.05_{-0.05}^{+0.05}$ | $-1.29_{-0.45}^{+0.44}$ |
38 | SILVERRUSH_2021_13162 | 09h59m53.65s | +02d48m59.72s | $+0.010_{-0.353}^{+0.351}$ | $-20.40_{-0.21}^{+0.22}$ | $-0.29_{-1.77}^{+1.70}$ |
39 | SILVERRUSH_2021_3069 | 09h59m55.00s | +01d47m20.65s | $-0.158_{-0.162}^{+0.161}$ | $-20.74_{-0.16}^{+0.17}$ | $-1.41_{-1.36}^{+1.36}$ |
40 | SILVERRUSH_2021_16061 | 09h59m58.23s | +01d56m31.31s | $-0.367_{-0.582}^{+0.551}$ | $-19.67_{-0.27}^{+0.27}$ | $-0.70_{-2.10}^{+1.95}$ |
41 | SILVERRUSH_2021_1188 | 09h59m59.16s | +01d46m12.92s | $+0.187_{-0.412}^{+0.416}$ | $-19.61_{-0.27}^{+0.27}$ | $-1.66_{-2.18}^{+2.21}$ |
42 | SILVERRUSH_2021_25058 | 09h59m59.92s | +01d28m54.45s | $+0.672_{-0.438}^{+0.455}$ | $-20.61_{-0.20}^{+0.20}$ | $+0.06_{-1.54}^{+1.48}$ |
43 | SILVERRUSH_2021_18119 | 10h00m01.62s | +02d06m20.22s | $+1.512_{-0.744}^{+0.744}$ | $-20.00_{-0.23}^{+0.24}$ | $-0.35_{-1.86}^{+1.73}$ | 72% outlier
44 | SILVERRUSH_2021_10528 | 10h00m02.67s | +02d02m55.46s | $+1.482_{-0.746}^{+0.744}$ | $-20.05_{-0.18}^{+0.18}$ | $+0.79_{-1.00}^{+0.98}$ | 72% outlier
45 | SILVERRUSH_2021_2770 | 10h00m03.95s | +01d36m01.86s | $+0.784_{-0.645}^{+0.686}$ | $-19.71_{-0.26}^{+0.27}$ | $-0.75_{-2.12}^{+1.99}$ |
46 | SILVERRUSH_2021_16285 | 10h00m05.06s | +02d07m17.00s | $+0.293_{-0.376}^{+0.370}$ | $-19.50_{-0.26}^{+0.25}$ | $-2.33_{-1.93}^{+2.05}$ |
47 | SILVERRUSH_2021_3270 | 10h00m07.03s | +01d36m19.86s | $-0.387_{-0.597}^{+0.569}$ | $-19.66_{-0.27}^{+0.27}$ | $-0.84_{-2.15}^{+2.04}$ |
48 | SILVERRUSH_2021_17881 | 10h00m07.79s | +02d05m49.55s | $+0.494_{-0.241}^{+0.244}$ | $-21.08_{-0.12}^{+0.12}$ | $+0.40_{-1.01}^{+1.03}$ |
49 | SILVERRUSH_2021_289 | 10h00m12.02s | +01d45m44.58s | $+0.197_{-0.195}^{+0.197}$ | $-20.87_{-0.15}^{+0.15}$ | $-0.49_{-1.23}^{+1.26}$ |
50 | SILVERRUSH_2021_10643 | 10h00m13.42s | +01d40m26.33s | $+0.089_{-0.213}^{+0.215}$ | $-20.88_{-0.14}^{+0.14}$ | $-0.39_{-1.24}^{+1.26}$ |
51 | SILVERRUSH_2021_8145 | 10h00m17.05s | +02d46m11.06s | $+0.207_{-0.349}^{+0.359}$ | $-20.42_{-0.21}^{+0.22}$ | $-0.25_{-1.75}^{+1.67}$ |
52 | SILVERRUSH_2021_7322 | 10h00m19.98s | +02d01m03.21s | $+0.087_{-0.464}^{+0.467}$ | $-19.99_{-0.23}^{+0.24}$ | $-0.30_{-1.86}^{+1.74}$ |
53 | SILVERRUSH_2021_16575 | 10h00m23.22s | +01d56m08.12s | $-0.475_{-0.539}^{+0.528}$ | $-20.26_{-0.15}^{+0.15}$ | $+0.85_{-0.94}^{+0.92}$ |
54 | SILVERRUSH_2021_19364 | 10h00m25.55s | +02d30m40.97s | $+1.120_{-0.673}^{+0.705}$ | $-19.61_{-0.29}^{+0.28}$ | $-1.76_{-2.21}^{+2.29}$ |
55 | SILVERRUSH_2021_20253 | 10h00m29.12s | +02d41m15.66s | $+3.337_{-1.572}^{+1.561}$ | $-19.97_{-0.28}^{+0.28}$ | $-0.67_{-2.08}^{+1.96}$ | 99% outlier
56 | SILVERRUSH_2021_7812 | 10h00m29.58s | +01d50m00.69s | $+0.284_{-0.264}^{+0.252}$ | $-19.81_{-0.23}^{+0.21}$ | $-2.63_{-1.73}^{+1.79}$ |
57 | SILVERRUSH_2021_8206 | 10h00m33.77s | +02d35m03.52s | $-0.226_{-0.328}^{+0.313}$ | $-20.21_{-0.26}^{+0.27}$ | $-0.99_{-2.10}^{+2.03}$ |
58 | SILVERRUSH_2021_1983 | 10h00m42.19s | +02d20m19.55s | $+0.353_{-0.337}^{+0.352}$ | $-20.56_{-0.18}^{+0.18}$ | $+0.27_{-1.39}^{+1.34}$ |
59 | SILVERRUSH_2021_13853 | 10h00m45.73s | +01d52m57.82s | $+0.619_{-0.541}^{+0.549}$ | $-19.51_{-0.30}^{+0.29}$ | $-1.76_{-2.21}^{+2.26}$ |
60 | SILVERRUSH_2021_26210 | 10h00m50.72s | +01d30m15.91s | $+0.682_{-0.477}^{+0.492}$ | $-20.55_{-0.23}^{+0.24}$ | $+0.07_{-1.63}^{+1.51}$ |
61 | SILVERRUSH_2021_30634 | 10h00m54.09s | +01d32m37.10s | $+0.303_{-0.164}^{+0.166}$ | $-20.98_{-0.20}^{+0.20}$ | $-2.28_{-1.69}^{+1.66}$ |
62 | SILVERRUSH_2021_3834 | 10h00m55.52s | +01d36m30.84s | $+0.140_{-0.266}^{+0.271}$ | $-20.09_{-0.23}^{+0.23}$ | $-1.86_{-2.00}^{+2.04}$ |
63 | SILVERRUSH_2021_4313 | 10h00m58.44s | +01d59m14.80s | $+0.681_{-0.407}^{+0.381}$ | $-19.36_{-0.21}^{+0.20}$ | $-3.38_{-1.29}^{+1.39}$ |
64 | SILVERRUSH_2021_17533 | 10h01m00.36s | +02d29m26.27s | $+0.870_{-0.446}^{+0.471}$ | $-20.27_{-0.23}^{+0.22}$ | $-1.89_{-2.01}^{+2.03}$ |
65 | SILVERRUSH_2021_11568 | 10h01m02.96s | +01d51m44.74s | $+0.086_{-0.175}^{+0.174}$ | $-20.40_{-0.21}^{+0.21}$ | $-2.08_{-1.80}^{+1.80}$ |
66 | SILVERRUSH_2021_25665 | 10h01m05.80s | +01d28m09.73s | $+0.177_{-0.107}^{+0.104}$ | $-21.01_{-0.17}^{+0.17}$ | $-2.97_{-1.39}^{+1.37}$ |
67 | SILVERRUSH_2021_12775 | 10h01m07.35s | +01d52m22.69s | $-0.051_{-0.452}^{+0.457}$ | $-19.64_{-0.30}^{+0.29}$ | $-1.34_{-2.26}^{+2.21}$ |
68 | SILVERRUSH_2021_12960 | 10h01m09.72s | +02d15m13.47s | $+0.340_{-0.244}^{+0.258}$ | $-20.73_{-0.21}^{+0.21}$ | $-0.50_{-1.82}^{+1.75}$ |
69 | SILVERRUSH_2021_18708 | 10h01m10.06s | +02d28m29.03s | $+0.337_{-0.091}^{+0.090}$ | $-21.49_{-0.10}^{+0.10}$ | $-2.55_{-0.92}^{+0.91}$ |
70 | SILVERRUSH_2021_20733 | 10h01m11.27s | +02d52m21.34s | $+0.348_{-0.126}^{+0.125}$ | $-21.58_{-0.08}^{+0.08}$ | $-0.46_{-0.70}^{+0.71}$ |
71 | SILVERRUSH_2021_1683 | 10h01m13.38s | +01d57m42.35s | $+0.547_{-0.604}^{+0.637}$ | $-19.75_{-0.24}^{+0.25}$ | $-0.71_{-2.06}^{+1.92}$ |
72 | SILVERRUSH_2021_9090 | 10h01m20.43s | +02d46m55.92s | $+0.295_{-0.377}^{+0.393}$ | $-20.24_{-0.21}^{+0.21}$ | $-0.52_{-1.76}^{+1.72}$ |
73 | SILVERRUSH_2021_19827 | 10h01m22.00s | +02d50m46.17s | $-0.106_{-0.323}^{+0.315}$ | $-20.39_{-0.20}^{+0.20}$ | $-0.55_{-1.71}^{+1.69}$ |
74 | SILVERRUSH_2021_17230 | 10h01m23.84s | +01d56m00.29s | $+0.118_{-0.139}^{+0.139}$ | $-20.62_{-0.18}^{+0.18}$ | $-2.27_{-1.50}^{+1.47}$ |
75 | SILVERRUSH_2021_2146 | 10h01m24.33s | +01d35m30.38s | $+0.336_{-0.529}^{+0.554}$ | $-19.66_{-0.29}^{+0.29}$ | $-1.09_{-2.25}^{+2.16}$ |
76 | SILVERRUSH_2021_17379 | 10h01m26.89s | +01d44m30.14s | $+0.171_{-0.278}^{+0.286}$ | $-20.62_{-0.19}^{+0.20}$ | $-0.43_{-1.63}^{+1.63}$ |
77 | SILVERRUSH_2021_14961 | 10h01m28.45s | +02d05m20.62s | $+0.681_{-0.623}^{+0.651}$ | $-19.49_{-0.29}^{+0.29}$ | $-1.21_{-2.23}^{+2.18}$ |
78 | SILVERRUSH_2021_6339 | 10h01m29.08s | +01d49m29.79s | $+0.002_{-0.161}^{+0.162}$ | $-20.98_{-0.15}^{+0.14}$ | $-0.75_{-1.23}^{+1.25}$ |
79 | SILVERRUSH_2021_1202 | 10h01m31.07s | +02d31m05.81s | $+0.576_{-0.493}^{+0.518}$ | $-19.94_{-0.22}^{+0.22}$ | $-0.85_{-1.96}^{+1.92}$ |
80 | SILVERRUSH_2021_16105 | 10h01m31.12s | +01d43m20.31s | $+2.122_{-0.951}^{+1.010}$ | $-20.10_{-0.25}^{+0.26}$ | $-1.57_{-2.07}^{+2.04}$ | 92% outlier
81 | SILVERRUSH_2021_6187 | 10h01m34.39s | +02d11m19.23s | $+0.710_{-0.360}^{+0.366}$ | $-20.84_{-0.10}^{+0.10}$ | $+1.13_{-0.70}^{+0.68}$ |
82 | SILVERRUSH_2021_13573 | 10h01m34.98s | +02d38m03.77s | $+0.363_{-0.122}^{+0.122}$ | $-20.81_{-0.11}^{+0.12}$ | $-3.16_{-1.02}^{+0.98}$ |
83 | SILVERRUSH_2021_17641 | 10h01m37.91s | +02d06m47.69s | $+0.156_{-0.142}^{+0.138}$ | $-20.28_{-0.16}^{+0.16}$ | $-3.12_{-1.33}^{+1.33}$ |
84 | SILVERRUSH_2021_6512 | 10h01m43.63s | +02d45m26.65s | $+0.382_{-0.207}^{+0.211}$ | $-21.08_{-0.16}^{+0.16}$ | $-0.71_{-1.30}^{+1.32}$ |
85 | SILVERRUSH_2021_2121 | 10h01m45.15s | +01d35m27.82s | $-0.207_{-0.220}^{+0.221}$ | $-20.41_{-0.22}^{+0.23}$ | $-1.72_{-1.85}^{+1.82}$ |
86 | SILVERRUSH_2021_4794 | 10h01m46.65s | +01d48m27.07s | $+0.073_{-0.371}^{+0.382}$ | $-19.71_{-0.27}^{+0.26}$ | $-1.73_{-2.16}^{+2.16}$ |
87 | SILVERRUSH_2021_3298 | 10h01m48.99s | +02d43m33.19s | $+0.555_{-0.290}^{+0.298}$ | $-20.84_{-0.18}^{+0.19}$ | $-0.64_{-1.56}^{+1.54}$ |
88 | SILVERRUSH_2021_2229 | 10h01m49.38s | +02d42m57.80s | $+0.129_{-0.168}^{+0.167}$ | $-20.55_{-0.20}^{+0.21}$ | $-2.31_{-1.67}^{+1.63}$ |
89 | SILVERRUSH_2021_12036 | 10h01m58.21s | +02d03m39.83s | $+0.360_{-0.175}^{+0.175}$ | $-20.96_{-0.13}^{+0.13}$ | $-1.10_{-1.15}^{+1.15}$ |
90 | SILVERRUSH_2021_9550 | 10h01m59.31s | +02d47m12.50s | $+0.025_{-0.178}^{+0.179}$ | $-20.94_{-0.17}^{+0.18}$ | $-0.94_{-1.45}^{+1.45}$ |
91 | SILVERRUSH_2021_16977 | 10h01m59.59s | +02d51m17.82s | $+0.197_{-0.180}^{+0.178}$ | $-20.03_{-0.16}^{+0.16}$ | $-3.58_{-1.11}^{+1.16}$ |
92 | SILVERRUSH_2021_12487 | 10h01m59.63s | +02d48m49.38s | $+0.105_{-0.277}^{+0.279}$ | $-20.04_{-0.25}^{+0.24}$ | $-2.15_{-1.94}^{+1.98}$ |
93 | SILVERRUSH_2021_3832 | 10h02m03.15s | +01d36m23.73s | $+0.066_{-0.139}^{+0.138}$ | $-20.63_{-0.19}^{+0.19}$ | $-2.36_{-1.57}^{+1.51}$ |
94 | SILVERRUSH_2021_17329 | 10h02m13.03s | +02d49m22.35s | $+0.041_{-0.437}^{+0.447}$ | $-20.35_{-0.25}^{+0.26}$ | $-0.26_{-1.85}^{+1.72}$ |
95 | SILVERRUSH_2021_13599 | 10h02m13.49s | +02d15m33.77s | $-0.016_{-0.334}^{+0.332}$ | $-19.54_{-0.25}^{+0.24}$ | $-2.55_{-1.83}^{+1.97}$ |
96 | SILVERRUSH_2021_8332 | 10h02m14.21s | +02d12m42.92s | $+0.709_{-0.472}^{+0.494}$ | $-20.02_{-0.24}^{+0.25}$ | $-0.96_{-2.06}^{+1.99}$ |
97 | SILVERRUSH_2021_5999 | 10h02m14.91s | +02d00m15.48s | $+0.584_{-0.422}^{+0.436}$ | $-20.62_{-0.14}^{+0.14}$ | $+0.89_{-0.90}^{+0.88}$ |
98 | SILVERRUSH_2021_17778 | 10h02m28.43s | +02d18m01.68s | $+0.903_{-0.553}^{+0.581}$ | $-19.77_{-0.27}^{+0.27}$ | $-1.62_{-2.17}^{+2.17}$ |
99 | SILVERRUSH_2021_7927 | 10h02m33.27s | +02d23m43.92s | $+0.515_{-0.381}^{+0.408}$ | $-20.26_{-0.23}^{+0.24}$ | $-0.90_{-1.95}^{+1.88}$ |
100 | SILVERRUSH_2021_13416 | 10h02m35.77s | +02d04m32.18s | $-0.224_{-0.570}^{+0.571}$ | $-19.45_{-0.32}^{+0.31}$ | $-1.37_{-2.27}^{+2.24}$ |
101 | SILVERRUSH_2021_16238 | 10h02m39.10s | +01d55m36.29s | $+1.456_{-0.854}^{+0.901}$ | $-19.52_{-0.30}^{+0.30}$ | $-1.43_{-2.22}^{+2.21}$ | 61% outlier
102 | SILVERRUSH_2021_15946 | 10h02m40.16s | +01d55m43.07s | $+0.894_{-0.661}^{+0.703}$ | $-19.71_{-0.29}^{+0.30}$ | $-0.85_{-2.18}^{+2.04}$ |
103 | SILVERRUSH_2021_7711 | 10h02m46.25s | +02d12m20.62s | $+0.155_{-0.150}^{+0.148}$ | $-20.43_{-0.19}^{+0.18}$ | $-2.63_{-1.55}^{+1.53}$ |
104 | SILVERRUSH_2021_9814 | 10h02m50.67s | +02d13m28.89s | $+0.254_{-0.480}^{+0.496}$ | $-19.87_{-0.26}^{+0.26}$ | $-0.67_{-2.06}^{+1.93}$ |
105 | SILVERRUSH_2021_5990 | 10h02m52.37s | +02d22m37.19s | $+0.580_{-0.418}^{+0.435}$ | $-20.08_{-0.25}^{+0.25}$ | $-1.14_{-2.13}^{+2.04}$ |
106 | SILVERRUSH_2021_20536 | 10h02m52.94s | +02d08m04.05s | $+0.262_{-0.206}^{+0.207}$ | $-20.75_{-0.18}^{+0.19}$ | $-1.41_{-1.57}^{+1.54}$ |
107 | SILVERRUSH_2021_12884 | 10h02m57.58s | +02d14m47.58s | $+0.179_{-0.092}^{+0.092}$ | $-21.78_{-0.09}^{+0.09}$ | $-0.79_{-0.74}^{+0.74}$ |
108 | SILVERRUSH_2021_8932 | 10h03m01.98s | +02d12m44.60s | $+0.170_{-0.109}^{+0.107}$ | $-21.03_{-0.16}^{+0.16}$ | $-2.47_{-1.29}^{+1.25}$ |
109 | SILVERRUSH_2021_25697 | 10h03m05.33s | +01d51m41.09s | $+1.699_{-0.833}^{+0.827}$ | $-20.32_{-0.23}^{+0.23}$ | $+0.02_{-1.65}^{+1.54}$ | 78% outlier
110 | SILVERRUSH_2021_22488 | 10h03m08.03s | +02d28m21.83s | $+0.709_{-0.482}^{+0.486}$ | $-19.79_{-0.26}^{+0.25}$ | $-2.19_{-2.03}^{+2.13}$ |
111 | SILVERRUSH_2021_20373 | 10h03m08.72s | +02d38m41.23s | $+0.607_{-0.232}^{+0.236}$ | $-21.45_{-0.14}^{+0.14}$ | $-0.59_{-1.19}^{+1.21}$ |
112 | SILVERRUSH_2021_13232 | 10h03m17.07s | +02d14m59.48s | $+0.315_{-0.157}^{+0.153}$ | $-20.67_{-0.18}^{+0.18}$ | $-2.83_{-1.43}^{+1.42}$ |
113 | SILVERRUSH_2021_18409 | 10h03m26.90s | +02d18m17.53s | $+0.056_{-0.214}^{+0.215}$ | $-20.67_{-0.21}^{+0.22}$ | $-1.19_{-1.79}^{+1.76}$ |
114 | SILVERRUSH_2021_11719 | 10h03m31.66s | +02d25m53.25s | $+0.288_{-0.439}^{+0.454}$ | $-19.94_{-0.26}^{+0.26}$ | $-1.28_{-2.18}^{+2.12}$ |
115 | SILVERRUSH_2021_7140 | 10h03m34.29s | +02d23m30.30s | $-0.497_{-0.249}^{+0.248}$ | $-20.88_{-0.17}^{+0.17}$ | $-1.18_{-1.47}^{+1.47}$ |