adsabs/AstroCLIMB huggingface dataset:
Paper: Ultradiffuse Galaxies (UDGs) with Hyper Suprime-Cam. I. Revised Catalog of Coma Cluster UDGs 
Authors: Jose Miguel G. Bautista, Jin Koda, Masafumi Yagi, Yutaka Komiyama, Hitomi Yamanoi
Figure caption: Histograms of GALFIT-measured r-band (a) μ 0, (b) r eff, (c) n, and (d) q. All UDGs (r eff ≥ 1.0) are shown in blue, while UDGs with r eff ≥ 1.5 are overplotted in red.
Paper: Ultradiffuse Galaxies (UDGs) with Hyper Suprime-Cam. I. Revised Catalog of Coma Cluster UDGs 
Authors: Jose Miguel G. Bautista, Jin Koda, Masafumi Yagi, Yutaka Komiyama, Hitomi Yamanoi
Figure caption: Comparisons of the objects matched with Zaritsky et al. (2019; 47, in red) and unmatched (79, blue) UDGs that satisfy the Zaritsky et al. (2019) selection criteria: (a) μ 0, g, (b) 〈μ g〉eff, and (c) 〈μ r〉eff. The unmatched UDGs are generally fainter in all of the surface brightnesses.
Paper: Ultradiffuse Galaxies (UDGs) with Hyper Suprime-Cam. I. Revised Catalog of Coma Cluster UDGs 
Authors: Jose Miguel G. Bautista, Jin Koda, Masafumi Yagi, Yutaka Komiyama, Hitomi Yamanoi
Figure caption: Example UDGs which were classified as nucleated in Yagi et al. (2016) and nonnucleated in this work: (a) raw and (b) residual after subtracting the model from GALFIT.
Paper: Ultradiffuse Galaxies (UDGs) with Hyper Suprime-Cam. I. Revised Catalog of Coma Cluster UDGs 
Authors: Jose Miguel G. Bautista, Jin Koda, Masafumi Yagi, Yutaka Komiyama, Hitomi Yamanoi
Figure caption: Nucleation fraction f n as a function of (a) M r and (b) projected radius with respect to the Coma Cluster center. The latter is restricted to m r ≤ 20 (M r ≤ −15.05) to avoid incompleteness from the limit in m psf. The error bars assume Poissonian noise.
Paper: Ultradiffuse Galaxies (UDGs) with Hyper Suprime-Cam. I. Revised Catalog of Coma Cluster UDGs 
Authors: Jose Miguel G. Bautista, Jin Koda, Masafumi Yagi, Yutaka Komiyama, Hitomi Yamanoi
Figure caption: Angular histogram of all UDGs in the catalog (blue) and UDGs with r eff, r ≥ 1.5 (red) in 10° bins.
Paper: Ultradiffuse Galaxies (UDGs) with Hyper Suprime-Cam. I. Revised Catalog of Coma Cluster UDGs 
Authors: Jose Miguel G. Bautista, Jin Koda, Masafumi Yagi, Yutaka Komiyama, Hitomi Yamanoi
Figure caption: Surface density of all UDG candidates in the catalog (blue) and UDG candidates excluding sub-UDGs (red) in 0.25 Mpc projected radius bins. As a reference, the black dashed line shows the theoretical density if the UDGs were distributed as a uniform sphere.
Paper: Ultradiffuse Galaxies (UDGs) with Hyper Suprime-Cam. I. Revised Catalog of Coma Cluster UDGs 
Authors: Jose Miguel G. Bautista, Jin Koda, Masafumi Yagi, Yutaka Komiyama, Hitomi Yamanoi
Figure caption: Kernel density estimation of UDG positions, with a Gaussian kernel. Dashed magenta lines mark increments of 1 Mpc in projected cluster radius.
Paper: An Unbiased Method of Measuring the Ratio of Two Data Sets
Authors: Zeyang Sun, Pengjie Zhang, Fuyu Dong, Ji Yao, Huanyuan Shan, Eric Jullo, Jean-Paul Kneib, Boyan Yin
Figure caption: Measurements of galaxy–shear cross-correlation (﹩{W}^{g{\gamma }_{t}}﹩) and galaxy–CMB lensing cross-correlation (﹩{w}^{g{\kappa }_{\mathrm{CMB}}}﹩) for various lens–source pairs, together with the best-fitting theory curves. The gray areas (﹩\theta \lt 10^{\prime} ﹩) correspond to the scales that are not used in the lensing-ratio measurement.
Paper: An Unbiased Method of Measuring the Ratio of Two Data Sets
Authors: Zeyang Sun, Pengjie Zhang, Fuyu Dong, Ji Yao, Huanyuan Shan, Eric Jullo, Jean-Paul Kneib, Boyan Yin
Figure caption: Measured posterior probability density function P(R) on the lensing ratios, using our ratio measurement method. P(R) is nearly Gaussian. We also show the best-fit value and the associated 1σ errors. We take lenses as DESI galaxies at three redshift bins denoted by z  L . One of the sources is DECaLS cosmic shear at three redshift bins denoted by z  γ  and the other is Planck cosmic microwave background lensing.
Paper: An Unbiased Method of Measuring the Ratio of Two Data Sets
Authors: Zeyang Sun, Pengjie Zhang, Fuyu Dong, Ji Yao, Huanyuan Shan, Eric Jullo, Jean-Paul Kneib, Boyan Yin
Figure caption: Impact of curvature Ω k  on the lensing ratios R. The negative value of Ω k  has a significantly greater impact on the lensing ratios than the positive value.
Paper: An Unbiased Method of Measuring the Ratio of Two Data Sets
Authors: Zeyang Sun, Pengjie Zhang, Fuyu Dong, Ji Yao, Huanyuan Shan, Eric Jullo, Jean-Paul Kneib, Boyan Yin
Figure caption: Exact lensing ratio (﹩{C}_{{\ell }}^{g{\kappa }_{\mathrm{CMB}}}/{C}_{{\ell }}^{g{\kappa }_{\gamma }}﹩) and its modeling (Equation (B10)) with different values of β). Equation (B10) with β = 1.0 provides an excellent approximation of the lensing ratio.
Paper: An Unbiased Method of Measuring the Ratio of Two Data Sets
Authors: Zeyang Sun, Pengjie Zhang, Fuyu Dong, Ji Yao, Huanyuan Shan, Eric Jullo, Jean-Paul Kneib, Boyan Yin
Figure caption: Consistency test 2. We compare the measured R by our method with R fit from model fitting.
Paper: An Unbiased Method of Measuring the Ratio of Two Data Sets
Authors: Zeyang Sun, Pengjie Zhang, Fuyu Dong, Ji Yao, Huanyuan Shan, Eric Jullo, Jean-Paul Kneib, Boyan Yin
Figure caption: Consistency test 1. We find no statistically significant dependence of R on the chosen scale cut ﹩{\theta }_{\min }﹩ (﹩{\theta }_{\max }﹩).
Paper: An Unbiased Method of Measuring the Ratio of Two Data Sets
Authors: Zeyang Sun, Pengjie Zhang, Fuyu Dong, Ji Yao, Huanyuan Shan, Eric Jullo, Jean-Paul Kneib, Boyan Yin
Figure caption: Consistency test 3. The measured R has negligible dependence on the adopted theoretical template, which depends on cosmology.
Paper: An Unbiased Method of Measuring the Ratio of Two Data Sets
Authors: Zeyang Sun, Pengjie Zhang, Fuyu Dong, Ji Yao, Huanyuan Shan, Eric Jullo, Jean-Paul Kneib, Boyan Yin
Figure caption: Cosmological constraint under the assumption of a model with a w = −1 cosmological constant with free curvature. The Ω m  − Ω k  constraint uses the six measured lensing ratios (green), CMB (gray), CMB + SN (red), and CMB + BAO (blue).
Paper: An Unbiased Method of Measuring the Ratio of Two Data Sets
Authors: Zeyang Sun, Pengjie Zhang, Fuyu Dong, Ji Yao, Huanyuan Shan, Eric Jullo, Jean-Paul Kneib, Boyan Yin
Figure caption: Impact of lens (shear source) redshift bias on the lensing ratios.
Paper: ALMA High-frequency Long-baseline Campaign in 2019: Band 9 and 10 In-band and Band-to-band Observations Using ALMA’s Longest Baselines
Authors: Luke T. Maud, Yoshiharu Asaki, Hiroshi Nagai, Takafumi Tsukui, Akihiko Hirota, Edward B. Fomalont, William R. F. Dent, Satoko Takahashi, Neil Phillips
Figure caption: Phase rms and phase offset as a function of baseline length for all antennas with the reference antenna for a selection of in-band (left) and B2B (right) observations extracted from scan-based solutions (note that all baselines follow the same trend but are not shown for clarity). The top of each plot shows the phase rms while the lower part shows the phase offset. The red points indicate the data while the gray bars show the median values. In the lower sections, the dotted gray lines are the standard deviation of the offset averages accounting for all antennas. The dashed black lines are the expected values, for phase rms from Table 1; for the offset it is zero degrees. The top panels are for EBs X70fe and Xda5c, and depict an ideal scenario, as the phase calibrator and target are identical. The middle and bottom panels show representative band 9 and band 10 cases, respectively. Overall the in-band uses a more distant calibrator that does not reduce the phase rms down to the expected level. For the B2B observations using closer calibrators, the phase rms is reduced by over a factor of 2 compared to the in-band data.
Paper: ALMA High-frequency Long-baseline Campaign in 2019: Band 9 and 10 In-band and Band-to-band Observations Using ALMA’s Longest Baselines
Authors: Luke T. Maud, Yoshiharu Asaki, Hiroshi Nagai, Takafumi Tsukui, Akihiko Hirota, Edward B. Fomalont, William R. F. Dent, Satoko Takahashi, Neil Phillips
Figure caption: Target and check source image coherence as a function of separation angle to the phase calibrator. As in Figure 2, the circle and square symbols represent in-band and B2B modes, while the colors yellow and green represent band 9 and band 10 frequencies. The smaller symbols are for the check sources, the larger for the targets. The lower limits are the coherence values calculated when scaling up the self-calibrated image peak fluxes to account for any flux loss due to phase rms (see Section 3.4). The dashed black lines are the linear fits to all data points. The degradation of coherence follows the same slopes within the fit uncertainty, although the intercept for the coherence is 0.8 at 0.°0 separation angle. The black solid line show values from corruption models that add antenna position uncertainties, yellow from a variable phase screen, and green using both antenna positions uncertainties and the screen model (the dashed and solid lines represent parallel and perpendicular phase-referencing direction offsets with respect to the phase screen motion—see Section 4).
Paper: ALMA High-frequency Long-baseline Campaign in 2019: Band 9 and 10 In-band and Band-to-band Observations Using ALMA’s Longest Baselines
Authors: Luke T. Maud, Yoshiharu Asaki, Hiroshi Nagai, Takafumi Tsukui, Akihiko Hirota, Edward B. Fomalont, William R. F. Dent, Satoko Takahashi, Neil Phillips
Figure caption: Comparisons of expected coherence as calculated from the phase rms plotted against the target source image coherence (left) and check source image coherence (right). Note that the phase rms used to calculate the expected coherence is measured from the bandpass source over the timescale equivalent to the phase-referencing cycle time, and hence acts as a proxy for phase residuals that would remain in the target after phase referencing. The circle and square symbols represent in-band and B2B modes, while the colors yellow and green represent band 9 and band 10 frequencies. In the left panel, the three larger symbols are for the observations where the phase calibrator and target are identical (i.e., 0° separation angle) whereas the outlined green circle and square indicate the band 10 observations that used the same setup with a target-to-phase-calibrator separation of 3.°7. The dashed line is of equal coherence, and the dotted lines are ± 5% and ± 10%. From the stable phase conditions (Section 3.1.1) we expected at least >0.8 coherence for all images. Considering the left target source panel, only the three observations where the target and phase calibrator are identical match the expected coherence. All remaining observations have a finite target-to-phase-calibrator separation, and all have lower-than-expected coherence.
Paper: ALMA High-frequency Long-baseline Campaign in 2019: Band 9 and 10 In-band and Band-to-band Observations Using ALMA’s Longest Baselines
Authors: Luke T. Maud, Yoshiharu Asaki, Hiroshi Nagai, Takafumi Tsukui, Akihiko Hirota, Edward B. Fomalont, William R. F. Dent, Satoko Takahashi, Neil Phillips
Figure caption: Plots of phase rms and phase offset as a function of baseline length to the reference antenna for the target source as corrupted by antenna position uncertainties (top), frozen-flow screen phase referencing (middle), antenna position uncertainties, and screen phase referencing (bottom). In the top panel, the phase rms is not plotted as this is exactly 0°, and antenna positions uncertainties only corrupt the phase offset. The lines are as per Figure 5, and in each panel, the upper and lower sections show the phase rms and phase offset. The screen model alone (middle) dominates in corrupting the phase rms, which is ∼50° after phase referencing despite the phase rms conditions of the model being set at 30° as measured over 60 s for a single source. There is little change in the phase offsets. The bottom plot combines both antenna positions and the screen model corruptions.
Paper: ALMA High-frequency Long-baseline Campaign in 2019: Band 9 and 10 In-band and Band-to-band Observations Using ALMA’s Longest Baselines
Authors: Luke T. Maud, Yoshiharu Asaki, Hiroshi Nagai, Takafumi Tsukui, Akihiko Hirota, Edward B. Fomalont, William R. F. Dent, Satoko Takahashi, Neil Phillips
Figure caption: Illustrative phase screen used for the phase-referencing model. The screen is sampled at the location of the antennas within the ALMA array configuration and the screen moved with time. To replicate phase referencing, the screen phases are associated with the target or phase calibrator, respectively, while also introducing an offset into the position sampled on the screen to account for any geometric difference between the target and phase calibrator. The resolution of each pixel we use relates to 10 m × 10 m, and for illustration a target-to-phase-calibrator separation angle of 4° would correspond to a shift of 70 m (7 pixels) for a screen at height 1000 m. The scale of the phase values is arbitrary, as these are later set dependently on the baseline-based phase rms built from a time sequence of continually sampled antenna-based phases while propagating the screen over the antenna coordinates.
Paper: ALMA High-frequency Long-baseline Campaign in 2019: Band 9 and 10 In-band and Band-to-band Observations Using ALMA’s Longest Baselines
Authors: Luke T. Maud, Yoshiharu Asaki, Hiroshi Nagai, Takafumi Tsukui, Akihiko Hirota, Edward B. Fomalont, William R. F. Dent, Satoko Takahashi, Neil Phillips
Figure caption: Comparisons of the in-band and B2B mode target image coherence that observe the same source. The circle and square symbols represent paired and unpaired observations, in that paired observation are taken on the same night in each mode, while unpaired match observations are taken on different nights. The colors yellow and green represent band 9 and band 10 frequencies. The two larger symbols highlight the observation pairs where the phase calibrator and target were the same source (i.e., 0° separation angle) whereas the outlined green circle indicates the paired band 10 observations that used the same target and phase calibrator with a 3.°7 separation angle. The dashed line is of equal coherence, while the dotted lines are ± 5% and ± 10%. Except those using the same calibrators, the B2B used closer calibrators compared to the in-band mode and typically achieved a higher coherence.
Paper: ALMA High-frequency Long-baseline Campaign in 2019: Band 9 and 10 In-band and Band-to-band Observations Using ALMA’s Longest Baselines
Authors: Luke T. Maud, Yoshiharu Asaki, Hiroshi Nagai, Takafumi Tsukui, Akihiko Hirota, Edward B. Fomalont, William R. F. Dent, Satoko Takahashi, Neil Phillips
Figure caption: Spatial structure function (SSF) style plot indicating the phase rms as a function of baseline length as measured using the phase-time series from the bandpass source. The gray points show the phase rms calculated from the entire timescale of the bandpass scan (305 s) while the red points indicate the value measured over a timescale equivalent to the phase-referencing cycle time of 51 s for the data set X105d2. The solid black bar indicates the median value of the red points using only baselines longer than the 80th percentile. The green, blue, and yellow bars are lines of phase rms at 30°, 50°, and 70°. Assuming the target phase is corrected down to the level of the black bar, we expect a target image coherence >0.87.
Paper: ALMA High-frequency Long-baseline Campaign in 2019: Band 9 and 10 In-band and Band-to-band Observations Using ALMA’s Longest Baselines
Authors: Luke T. Maud, Yoshiharu Asaki, Hiroshi Nagai, Takafumi Tsukui, Akihiko Hirota, Edward B. Fomalont, William R. F. Dent, Satoko Takahashi, Neil Phillips
Figure caption: Target images for a selection of in-band (left) and B2B (right) observations. The top panels show the band 9 EBs X70fe and Xda5c where the target and phase calibrator are identical (i.e., 0.°0) and make up an ideal scenario; the middle row shows band 9 EBs Xb0b8 and Xc5f1 using 3.°8 and 0.°8 separated calibrators; the bottom plots show the band 10 EBs X105d2 and X7fe3 using 3.°7 and 0.°7 separated calibrators. Each pair of plots uses the same color scaling. Contours are drawn at −5σ (black dotted), 5σ, 15σ, 25σ, and 50σ (color), where σ are listed in Table 5. Note that, except for X70fe and Xda5c, the right panel B2B observations have higher image coherence and more pointlike image structure; strong central emission is colored black. The beams are shown at the bottom left of each panel.
Paper: The LSST Cadence Impact on Non-time-critical Eclipsing Binary Science
Authors: Andrej Prša, Peter Yoachim, Lynne Jones
Figure caption: The number of visits (in log scale), cumulative for all passbands over 10 yr, computed for the opsim baseline v2.1. Top: the number of visits per EB. Middle: the number of visits while the system is in eclipse. Bottom: the fraction of all visits while the system is in eclipse (i.e., middle panel divided by top panel). The bottom panel is particularly telling because it peaks at low fractions, i.e., the region of the parameter space where EB detection rates are strongly suppressed.
Paper: The LSST Cadence Impact on Non-time-critical Eclipsing Binary Science
Authors: Andrej Prša, Peter Yoachim, Lynne Jones
Figure caption: The fraction of visits in each of the ugrizy passbands with respect to all visits over the 10 yr survey time span. The ensemble of peaks around 20%–25% indicates that the simulated color availability will be more than sufficient to “absolutize” photometry and yield absolute temperatures of both components. The distributions have been computed for the baseline opsim run v2.1.
Paper: The LSST Cadence Impact on Non-time-critical Eclipsing Binary Science
Authors: Andrej Prša, Peter Yoachim, Lynne Jones
Figure caption: Comparison between TESS (blue) and LSST (red) light-curve data. The examples are sorted from a low (top) to a high (bottom) number of data points in LSST data, driven predominantly by the observing strategy (WFD vs. DDF). The figure showcases both detection efficiency challenges for sparsely sampled light curves (top) and benchmark-grade modeling possibilities from DDF data (bottom).
Paper: The LSST Cadence Impact on Non-time-critical Eclipsing Binary Science
Authors: Andrej Prša, Peter Yoachim, Lynne Jones
Figure caption: The fraction of systems detected as EBs given the threshold number of data points in eclipse with respect to all data points. Note that the fraction is ∼0.85 even when the threshold is set to a single data point in eclipse (the leftmost part of the curve); that is because LSST will miss every single eclipse for about ∼15% of all TESS EBs. This is the direct consequence of the chosen cadence, orbital period of the EB and, consequently, eclipse widths.
Paper: The LSST Cadence Impact on Non-time-critical Eclipsing Binary Science
Authors: Andrej Prša, Peter Yoachim, Lynne Jones
Figure caption: The effect of the 5% threshold on the number of successful detections. The 5% threshold implies that 5% of all data points in a light curve are within an eclipse. As expected, we see that the distribution of detected EBs is strongly biased against long periods.
Paper: The LSST Cadence Impact on Non-time-critical Eclipsing Binary Science
Authors: Andrej Prša, Peter Yoachim, Lynne Jones
Figure caption: Number of visits in all passbands over the 10 yr survey lifetime in a Mollweide projection. The peaks in the distributions correspond to deep-drilling fields. This map is computed from the v2.1 baseline run.
Paper: Ultradiffuse Galaxies (UDGs) with Hyper Suprime-Cam. I. Revised Catalog of Coma Cluster UDGs 
Authors: Jose Miguel G. Bautista, Jin Koda, Masafumi Yagi, Yutaka Komiyama, Hitomi Yamanoi
Figure caption: Change in χ 2 from the best-fit solution, ﹩{\chi }_{\min }^{2}﹩, for the sample of UDGs in Figure 9. From the 3D parameter space of r eff, r, m r, and n r, we show two projections: (a) r eff, r and m r, and (b) r eff, r and n r. The χ 2 values are optimized in the other parameters for each fixed pair (n r, r eff, r) and (m r, r eff, r). The ﹩{\chi }^{2}-{\chi }_{\min }^{2}=1﹩ contour is shown in yellow, while the error output from GALFIT is shown in red. Blank regions are where no convergent solutions were found. As in Figure 9, columns are arranged according to 〈μ r〉eff: from left to right, 〈μ r〉eff fainter than 24, 25, 26, and 27 mag arcsec−2. The plot ranges span (Δn, Δr eff,r, Δm r) = (0.1, 0.1 kpc, 0.1 mag) for the left two columns, and (0.5, 1.0 kpc, 0.5 mag) for the right two columns.
Paper: Ultradiffuse Galaxies (UDGs) with Hyper Suprime-Cam. I. Revised Catalog of Coma Cluster UDGs 
Authors: Jose Miguel G. Bautista, Jin Koda, Masafumi Yagi, Yutaka Komiyama, Hitomi Yamanoi
Figure caption: Comparison of our r-band values to the corresponding R-band values in Yagi et al. (2016). UDGs in this work are plotted as blue dots, while the 65 SC-UDGs that were removed by the GALFIT r eff, r selection criterion are shown as orange crosses. For m, the dashed line is Equation (1), assuming the median color g – r = 0.55.
Paper: Ultradiffuse Galaxies (UDGs) with Hyper Suprime-Cam. I. Revised Catalog of Coma Cluster UDGs 
Authors: Jose Miguel G. Bautista, Jin Koda, Masafumi Yagi, Yutaka Komiyama, Hitomi Yamanoi
Figure caption: Comparison of μ 0, g, m g, r eff, g, and q g (or n) between this work and (a)–(d) the 47 UDGs in van Dokkum et al. (2015), (e)–(h) the 98 UDGs in Zaritsky et al. (2019), and (i)–(l) the 86 UDGs in Zaritsky et al. (2022). Panel (l) compares n as it is set free in Zaritsky et al. (2022). Meanwhile, since n is fixed to 1 in van Dokkum et al. (2015) and Zaritsky et al. (2019), panels (d) and (h) compare q instead. The dashed lines have unit slope. Systematic offsets are apparent between the parameters of this work and Zaritsky et al. (2019).
Paper: Ultradiffuse Galaxies (UDGs) with Hyper Suprime-Cam. I. Revised Catalog of Coma Cluster UDGs 
Authors: Jose Miguel G. Bautista, Jin Koda, Masafumi Yagi, Yutaka Komiyama, Hitomi Yamanoi
Figure caption: Nine sample UDGs that are outliers in the correlation plot (Figure 13). (a) r-band HSC images. (b) Residuals from the best-fit r-band values in this work. (c) Residuals from the R-band fit values from Yagi et al. (2016). The m R for the Sérsic model is converted to the equivalent m r using Equation (1). Comparing the two sets of residuals, we find the different fit values produce little difference in the quality of the residuals.
Paper: Ultradiffuse Galaxies (UDGs) with Hyper Suprime-Cam. I. Revised Catalog of Coma Cluster UDGs 
Authors: Jose Miguel G. Bautista, Jin Koda, Masafumi Yagi, Yutaka Komiyama, Hitomi Yamanoi
Figure caption: Example UDGs organized by 〈μ r〉eff. Columns from left to right: UDGs fainter than 24, 25, 26, and 27 mag arcsec−2. DF44 is shown in the top of the 2nd column. Each UDG’s ID number in this catalog is shown in the bottom right of each cutout.
Paper: Ultradiffuse Galaxies (UDGs) with Hyper Suprime-Cam. I. Revised Catalog of Coma Cluster UDGs 
Authors: Jose Miguel G. Bautista, Jin Koda, Masafumi Yagi, Yutaka Komiyama, Hitomi Yamanoi
Figure caption: (a) The spatial distribution of UDGs around the Coma Cluster center (α J2000, δ J2000) = (12:59:42.8, +27:58:14). (b) UDGs from the other catalogs of the same area.
Paper: Ultradiffuse Galaxies (UDGs) with Hyper Suprime-Cam. I. Revised Catalog of Coma Cluster UDGs 
Authors: Jose Miguel G. Bautista, Jin Koda, Masafumi Yagi, Yutaka Komiyama, Hitomi Yamanoi
Figure caption: Comparison of contaminant masks between this work and Yagi et al. (2016) for one outlier UDG (bottom right in Figure 14). From left to right: zoomed-in r-band HSC raw image, contaminant mask generated in this work, and contaminant mask from Yagi et al. (2016).