adsabs/AstroCLIMB huggingface dataset:
Paper: Deep Learning of Dark Energy Spectroscopic Instrument Mock Spectra to Find Damped Lyα Systems
Authors: Ben Wang, Jiaqi Zou, Zheng Cai, J. Xavier Prochaska, Zechang Sun, Jiani Ding, Andreu Font-Ribera, Alma Gonzalez, Hiram K. Herrera-Alcantar, Vid Irsic, Xiaojing Lin, David Brooks, Soléne Chabanier, Roger de Belsunce, Nathalie Palanque-Delabrouille, Gregory Tarle, Zhimin Zhou
Figure caption: Validation samples: red lines are the DLAs detected by our CNN model, and blues lines are the DLAs in mock spectra. (a),(b) TP case: our model can detect DLAs with different column density levels. Even sub-DLAs can be characterized. (c) FP case: the DLA finder identifies a DLA in this window, but there is no such DLA in the mock catalog. In the lower panel of (c) is the same spectra as in the upper panel but with a higher S/N. It is clear that there is no DLA if we check the high S/N spectra. However, this is very difficult to identify even for the human being in the upper panel of panel (c) because of the low S/N. (d) FN sample: the DLA finder misses a sub-DLA in this wavelength range. In the lower panel of (d) is the same spectra as in the upper panel but with higher S/N. This shows a missing sub-DLA with low S/N(<2). The flux range for the lower and upper panels of (c) and (d) is quite different because of the random array as the noise is inserted to get a lower S/N.
Paper: Deep Learning of Dark Energy Spectroscopic Instrument Mock Spectra to Find Damped Lyα Systems
Authors: Ben Wang, Jiaqi Zou, Zheng Cai, J. Xavier Prochaska, Zechang Sun, Jiani Ding, Andreu Font-Ribera, Alma Gonzalez, Hiram K. Herrera-Alcantar, Vid Irsic, Xiaojing Lin, David Brooks, Soléne Chabanier, Roger de Belsunce, Nathalie Palanque-Delabrouille, Gregory Tarle, Zhimin Zhou
Figure caption: The standard CNN architecture we used, casting DLAs as a 1D image problem. There are three convolutional layers, three pooling layers, one fully connected layer, and three subfully connected layers. We have reset two parameters variable. One is n, it stands for how many pixels one window contains; it could be 400 or 600. The other one is m, and it stands for the dimensions of the input data; the value of it is 1 or 4 (median smoothing for low S/N spectra). The three subfully connected layers are correspond to three labels: classification, offset, and column density.
Paper: Deep Learning of Dark Energy Spectroscopic Instrument Mock Spectra to Find Damped Lyα Systems
Authors: Ben Wang, Jiaqi Zou, Zheng Cai, J. Xavier Prochaska, Zechang Sun, Jiani Ding, Andreu Font-Ribera, Alma Gonzalez, Hiram K. Herrera-Alcantar, Vid Irsic, Xiaojing Lin, David Brooks, Soléne Chabanier, Roger de Belsunce, Nathalie Palanque-Delabrouille, Gregory Tarle, Zhimin Zhou
Figure caption: Completeness results for different S/N levels and column densities using the desi-Y1 mock spectra. This is the result choosing the critical value of C min as 0.5. Some bins on the top have a value of 1; this is because there are few DLAs with logN H I  above 22 from the Y1 mock. For example, there are just two DLAs in the bin with S/N 4 to 5 and logN H I  above 22. The DLA finder detects them both, so the purity goes to 1. There are just five DLAs in the bin with S/N 3 to 4 and logN H I  above 22. The DLA finder detects four of them and misses one DLA, so the purity goes down to 0.8. That is why the completeness for HCD DLAs changes a lot in different bins.
Paper: Deep Learning of Dark Energy Spectroscopic Instrument Mock Spectra to Find Damped Lyα Systems
Authors: Ben Wang, Jiaqi Zou, Zheng Cai, J. Xavier Prochaska, Zechang Sun, Jiani Ding, Andreu Font-Ribera, Alma Gonzalez, Hiram K. Herrera-Alcantar, Vid Irsic, Xiaojing Lin, David Brooks, Soléne Chabanier, Roger de Belsunce, Nathalie Palanque-Delabrouille, Gregory Tarle, Zhimin Zhou
Figure caption: Four outputs for every window: “pred”, “offset”, “coldensity”, and “conf”. The red line is “pred”; the value for 120 pixels near the center of a DLA is 1 and for other pixels is 0. The green line is the “offset”; it shows the distance between every pixel and the center pixel of a DLA. The value of the offset is close to 0 for the pixels without a DLA. The blue line is the column density. The last gray line is the confidence level. This is an example with high confidence level; the confidence value for pixels with a DLA is above 0.9.
Paper: Deep Learning of Dark Energy Spectroscopic Instrument Mock Spectra to Find Damped Lyα Systems
Authors: Ben Wang, Jiaqi Zou, Zheng Cai, J. Xavier Prochaska, Zechang Sun, Jiani Ding, Andreu Font-Ribera, Alma Gonzalez, Hiram K. Herrera-Alcantar, Vid Irsic, Xiaojing Lin, David Brooks, Soléne Chabanier, Roger de Belsunce, Nathalie Palanque-Delabrouille, Gregory Tarle, Zhimin Zhou
Figure caption:  N H I  distribution of mock spectra and training sets. There are more DLAs with low-column density in the mock data, which follows the empirically measured distribution. The distribution of the column density in the training data is uniformed to reduce the bias of the model to lower N H I  values.
Paper: Deep Learning of Dark Energy Spectroscopic Instrument Mock Spectra to Find Damped Lyα Systems
Authors: Ben Wang, Jiaqi Zou, Zheng Cai, J. Xavier Prochaska, Zechang Sun, Jiani Ding, Andreu Font-Ribera, Alma Gonzalez, Hiram K. Herrera-Alcantar, Vid Irsic, Xiaojing Lin, David Brooks, Soléne Chabanier, Roger de Belsunce, Nathalie Palanque-Delabrouille, Gregory Tarle, Zhimin Zhou
Figure caption: Smoothing result for the spectra. The blue dashed line stands for the center of DLAs. The red dashed line is the error for the spectra. The upper panel is the original flux. The lower three panels show the flux after median smoothing for 3, 7, and 15 pixels.
Paper: Deep Learning of Dark Energy Spectroscopic Instrument Mock Spectra to Find Damped Lyα Systems
Authors: Ben Wang, Jiaqi Zou, Zheng Cai, J. Xavier Prochaska, Zechang Sun, Jiani Ding, Andreu Font-Ribera, Alma Gonzalez, Hiram K. Herrera-Alcantar, Vid Irsic, Xiaojing Lin, David Brooks, Soléne Chabanier, Roger de Belsunce, Nathalie Palanque-Delabrouille, Gregory Tarle, Zhimin Zhou
Figure caption: The column density estimation for the DLAs matched between the CNN model and the true value in mock spectra with S/N > 3 and log(N H I ) > 20.0.
Paper: Deep Learning of Dark Energy Spectroscopic Instrument Mock Spectra to Find Damped Lyα Systems
Authors: Ben Wang, Jiaqi Zou, Zheng Cai, J. Xavier Prochaska, Zechang Sun, Jiani Ding, Andreu Font-Ribera, Alma Gonzalez, Hiram K. Herrera-Alcantar, Vid Irsic, Xiaojing Lin, David Brooks, Soléne Chabanier, Roger de Belsunce, Nathalie Palanque-Delabrouille, Gregory Tarle, Zhimin Zhou
Figure caption: We have applied our CNN model on the desiY1-0.14 mock spectra. These mock spectra contain both DLAs and BALs. The purity and completeness are shown in (a) and (b). The minimum S/N for this mock is 1.0, and thus the left panel (S/N < 0) is blank. The completeness is still above 96% for S/N > 3 spectra. The purity drops about 10%–20% in different bins. Nevertheless, the DESI has a formal BAL catalog, which will get rid of more than 98.6% BALs from the catalog. Then, we can run the DLA finder on the BAL-removed spectra. Therefore, we think that the purity result shown in Figure 8 is still valid.
Paper: Deep Learning of Dark Energy Spectroscopic Instrument Mock Spectra to Find Damped Lyα Systems
Authors: Ben Wang, Jiaqi Zou, Zheng Cai, J. Xavier Prochaska, Zechang Sun, Jiani Ding, Andreu Font-Ribera, Alma Gonzalez, Hiram K. Herrera-Alcantar, Vid Irsic, Xiaojing Lin, David Brooks, Soléne Chabanier, Roger de Belsunce, Nathalie Palanque-Delabrouille, Gregory Tarle, Zhimin Zhou
Figure caption: GP completeness results for different S/N levels and column densities using the desi-Y1 mock spectra. Here we only select absorbers with N H I  > 20.0 to provide a simple comparison because the current GP model is well developed only using N H I  > 20.0 absorbers.
Paper: Deep Learning of Dark Energy Spectroscopic Instrument Mock Spectra to Find Damped Lyα Systems
Authors: Ben Wang, Jiaqi Zou, Zheng Cai, J. Xavier Prochaska, Zechang Sun, Jiani Ding, Andreu Font-Ribera, Alma Gonzalez, Hiram K. Herrera-Alcantar, Vid Irsic, Xiaojing Lin, David Brooks, Soléne Chabanier, Roger de Belsunce, Nathalie Palanque-Delabrouille, Gregory Tarle, Zhimin Zhou
Figure caption: BAO-fitting result: (a) QSO–Lya cross correlation and (b) Lya–Lya autocorrelation. The red line is the best-fit model by masking the DLAs in the mock catalog. The black dashed line is the best-fit model by masking DLAs detected by our CNN model. The blue dashed line is the best-fit model if we do not mask any DLA. The yellow dashed line is the best-fit model by masking DLAs detected by the GP. The gray points are the data points from the analyze by masking the DLAs in the mock spectra.
Paper: Deep Learning of Dark Energy Spectroscopic Instrument Mock Spectra to Find Damped Lyα Systems
Authors: Ben Wang, Jiaqi Zou, Zheng Cai, J. Xavier Prochaska, Zechang Sun, Jiani Ding, Andreu Font-Ribera, Alma Gonzalez, Hiram K. Herrera-Alcantar, Vid Irsic, Xiaojing Lin, David Brooks, Soléne Chabanier, Roger de Belsunce, Nathalie Palanque-Delabrouille, Gregory Tarle, Zhimin Zhou
Figure caption: GP purity results for different S/N levels and column densities using the desi-Y1 mock spectra. Here we only select absorbers with N H I  > 20.0 to provide a simple comparison because the current GP model is well developed only using N H I  > 20.0 absorbers.
Paper: Deep Learning of Dark Energy Spectroscopic Instrument Mock Spectra to Find Damped Lyα Systems
Authors: Ben Wang, Jiaqi Zou, Zheng Cai, J. Xavier Prochaska, Zechang Sun, Jiani Ding, Andreu Font-Ribera, Alma Gonzalez, Hiram K. Herrera-Alcantar, Vid Irsic, Xiaojing Lin, David Brooks, Soléne Chabanier, Roger de Belsunce, Nathalie Palanque-Delabrouille, Gregory Tarle, Zhimin Zhou
Figure caption: The value and error bar for three different fittings: masking DLAs in the mock catalog, masking DLAs detected by the CNN, and masking DLAs detected by the GP. It is clear that the result from masking the GP DLA catalog has larger difference from that of the mock DLA catalog. The gray line is the theoretical value for these two parameters.
Paper: Deep Learning of Dark Energy Spectroscopic Instrument Mock Spectra to Find Damped Lyα Systems
Authors: Ben Wang, Jiaqi Zou, Zheng Cai, J. Xavier Prochaska, Zechang Sun, Jiani Ding, Andreu Font-Ribera, Alma Gonzalez, Hiram K. Herrera-Alcantar, Vid Irsic, Xiaojing Lin, David Brooks, Soléne Chabanier, Roger de Belsunce, Nathalie Palanque-Delabrouille, Gregory Tarle, Zhimin Zhou
Figure caption: The CDDF from both the CNN DLA catalog and GP DLA catalog in comparison to the real mock DLA catalog with z < 3.8. The error bars in the y-axis represent the 68% confidence limits.
Paper: Deep Learning of Dark Energy Spectroscopic Instrument Mock Spectra to Find Damped Lyα Systems
Authors: Ben Wang, Jiaqi Zou, Zheng Cai, J. Xavier Prochaska, Zechang Sun, Jiani Ding, Andreu Font-Ribera, Alma Gonzalez, Hiram K. Herrera-Alcantar, Vid Irsic, Xiaojing Lin, David Brooks, Soléne Chabanier, Roger de Belsunce, Nathalie Palanque-Delabrouille, Gregory Tarle, Zhimin Zhou
Figure caption: The column density estimation for the DLAs matched between the GP model and the true value in mock spectra with S/N > 3 and logN H I  > 20.0
Paper: Deep Learning of Dark Energy Spectroscopic Instrument Mock Spectra to Find Damped Lyα Systems
Authors: Ben Wang, Jiaqi Zou, Zheng Cai, J. Xavier Prochaska, Zechang Sun, Jiani Ding, Andreu Font-Ribera, Alma Gonzalez, Hiram K. Herrera-Alcantar, Vid Irsic, Xiaojing Lin, David Brooks, Soléne Chabanier, Roger de Belsunce, Nathalie Palanque-Delabrouille, Gregory Tarle, Zhimin Zhou
Figure caption: The redshift estimation for the DLAs matched between the GP model and the true value in mock spectra with S/N > 3 and logN H I  > 20.0
Paper: Deep Learning of Dark Energy Spectroscopic Instrument Mock Spectra to Find Damped Lyα Systems
Authors: Ben Wang, Jiaqi Zou, Zheng Cai, J. Xavier Prochaska, Zechang Sun, Jiani Ding, Andreu Font-Ribera, Alma Gonzalez, Hiram K. Herrera-Alcantar, Vid Irsic, Xiaojing Lin, David Brooks, Soléne Chabanier, Roger de Belsunce, Nathalie Palanque-Delabrouille, Gregory Tarle, Zhimin Zhou
Figure caption: Redshift estimation for the DLAs matched between the CNN model and the true value in mock spectra with S/N > 3 and log(N H I ) > 20.0.
Paper: Deep Learning of Dark Energy Spectroscopic Instrument Mock Spectra to Find Damped Lyα Systems
Authors: Ben Wang, Jiaqi Zou, Zheng Cai, J. Xavier Prochaska, Zechang Sun, Jiani Ding, Andreu Font-Ribera, Alma Gonzalez, Hiram K. Herrera-Alcantar, Vid Irsic, Xiaojing Lin, David Brooks, Soléne Chabanier, Roger de Belsunce, Nathalie Palanque-Delabrouille, Gregory Tarle, Zhimin Zhou
Figure caption: We have applied our CNN model on these two spectra mentioned by Ho et al. (2021). It is clear that the DLA and sub-DLA candidates are detected.
Paper: Photometric Recalibration of the SDSS Stripe 82 to a Few Millimagnitude Precision with the Stellar Color Regression Method and Gaia EDR3
Authors: Bowen Huang, Haibo Yuan
Figure caption: Reddening values in BP − u, BP − g, RP − r, RP − i, and RP − z colors plotted against E(BP − RP) for the LAMOST sample with 2.5σ clipping performed. The red and blue lines mark the reddening coefficients at 5000 and 6000 K, respectively. Note that lines are not forced to go through the origin.
Paper: Photometric Recalibration of the SDSS Stripe 82 to a Few Millimagnitude Precision with the Stellar Color Regression Method and Gaia EDR3
Authors: Bowen Huang, Haibo Yuan
Figure caption: Precision of intrinsic colors for the LAMOST target sample. From top to bottom are BP − u, BP − g, RP − r, RP − i, and RP − z colors, respectively. The first column shows the residual distributions. The Gaussian fitting results are overplotted in red, with the σ values marked. The second, third, and fourth columns plot residuals against T eff, [Fe/H], and log g, respectively.
Paper: Photometric Recalibration of the SDSS Stripe 82 to a Few Millimagnitude Precision with the Stellar Color Regression Method and Gaia EDR3
Authors: Bowen Huang, Haibo Yuan
Figure caption: Distributions in the R.A.–E(BP − RP) plane for the LAMOST (left) and SDSS (right) target stars. The LAMOST and SDSS reference stars are also plotted as red dots.
Paper: Photometric Recalibration of the SDSS Stripe 82 to a Few Millimagnitude Precision with the Stellar Color Regression Method and Gaia EDR3
Authors: Bowen Huang, Haibo Yuan
Figure caption: Distributions in the planes of T eff –[Fe/H] (top) and T eff –log g (bottom) for the LAMOST (left) and SDSS (right) target stars. The reference stars are plotted as red dots.
Paper: Photometric Recalibration of the SDSS Stripe 82 to a Few Millimagnitude Precision with the Stellar Color Regression Method and Gaia EDR3
Authors: Bowen Huang, Haibo Yuan
Figure caption: Magnitude offsets in the u, g, r, i, and z bands as a function of decl. for the LAMOST and SDSS target samples. The vertical solid lines mark the approximate boundaries between the different CCD columns. The vertical dashed lines mark the approximate boundaries between the two strips of Stripe 82.
Paper: Photometric Recalibration of the SDSS Stripe 82 to a Few Millimagnitude Precision with the Stellar Color Regression Method and Gaia EDR3
Authors: Bowen Huang, Haibo Yuan
Figure caption: Same as Figure 3 but for the SDSS sample.
Paper: Photometric Recalibration of the SDSS Stripe 82 to a Few Millimagnitude Precision with the Stellar Color Regression Method and Gaia EDR3
Authors: Bowen Huang, Haibo Yuan
Figure caption: Magnitude offsets in the u, g, r, i, and z bands as a function of R.A. for the LAMOST and SDSS target samples. Stars in the north and south strips are displayed in red and black, respectively.
Paper: Photometric Recalibration of the SDSS Stripe 82 to a Few Millimagnitude Precision with the Stellar Color Regression Method and Gaia EDR3
Authors: Bowen Huang, Haibo Yuan
Figure caption: Same as Figure 5 but for the SDSS target sample.
Paper: Photometric Recalibration of the SDSS Stripe 82 to a Few Millimagnitude Precision with the Stellar Color Regression Method and Gaia EDR3
Authors: Bowen Huang, Haibo Yuan
Figure caption: Values of ﹩{\delta }_{m}^{\mathrm{ext}}({\rm{R}}.{\rm{A}}.)﹩ and ﹩{\delta }_{m}^{{ff}}(\mathrm{decl}.)﹩ for the u, g, r, i, and z bands. The first and second columns plot ﹩{\delta }_{m}^{{ff}}(\mathrm{decl}.)﹩ as a function of decl. for the south and north strips, respectively. The vertical dashed lines mark the approximate boundaries between the different camera CCD columns. The third and fourth columns plot ﹩{\delta }_{m}^{\mathrm{ext}}({\rm{R}}.{\rm{A}}.)﹩ as a function of R.A. for the south and north strips, respectively. The shaded regions are of ±0.02 mag in the u band and ±0.01 mag in the griz bands.
Paper: Photometric Recalibration of the SDSS Stripe 82 to a Few Millimagnitude Precision with the Stellar Color Regression Method and Gaia EDR3
Authors: Bowen Huang, Haibo Yuan
Figure caption: The differences of ﹩{\delta }_{m}^{{ff}}(\mathrm{decl}.)﹩ between the two strips in the u, g, r, i, and z bands (from top to bottom). The green solid lines mark the original differences. The blue solid lines mark the differences after accounting for the effect of flat-fielding corrections of I07. The vertical dashed lines mark the approximate boundaries between different CCD columns. The red dashed line for each CCD column marks the mean value. The standard deviations of the blue lines with respect to the red lines are labeled.
Paper: Photometric Recalibration of the SDSS Stripe 82 to a Few Millimagnitude Precision with the Stellar Color Regression Method and Gaia EDR3
Authors: Bowen Huang, Haibo Yuan
Figure caption: Magnitude offsets in the u, g, r, i, and z bands as functions of decl. (left) and R.A. (right), after corrections of the ﹩{\delta }_{m}^{\mathrm{ext}}({\rm{R}}.{\rm{A}}.)﹩ and ﹩{\delta }_{m}^{{ff}}(\mathrm{decl}.)﹩. In the left column, the vertical solid lines mark the approximate boundaries between the different CCD columns, and the vertical dashed lines mark the approximate boundaries between the two strips. In the right column, the red and black dots represent stars in the north and south strips, respectively. The green lines represent the median values.
Paper: Photometric Recalibration of the SDSS Stripe 82 to a Few Millimagnitude Precision with the Stellar Color Regression Method and Gaia EDR3
Authors: Bowen Huang, Haibo Yuan
Figure caption: The variations of magnitude offset as a function of magnitude for the six SDSS CCD columns after corrections for ﹩{\delta }_{m}^{\mathrm{ext}}({\rm{R}}.{\rm{A}}.)﹩ and ﹩{\delta }_{c}^{{ff}}(\mathrm{decl}.)﹩. The red solid lines are third-order polynomial fitting results.
Paper: Photometric Recalibration of the SDSS Stripe 82 to a Few Millimagnitude Precision with the Stellar Color Regression Method and Gaia EDR3
Authors: Bowen Huang, Haibo Yuan
Figure caption: Histogram distributions of magnitude offsets (﹩{\delta }_{m}^{\mathrm{ext}}({\rm{R}}.{\rm{A}}.)﹩ + ﹩{\delta }_{m}^{{ff}}(\mathrm{decl}.)﹩) in the u, g, r, i, and z bands. The solid red lines are Gaussian fitting results. The σ values are marked.
Paper: Photometric Recalibration of the SDSS Stripe 82 to a Few Millimagnitude Precision with the Stellar Color Regression Method and Gaia EDR3
Authors: Bowen Huang, Haibo Yuan
Figure caption: Spatial variations of magnitude offsets (﹩{\delta }_{m}^{\mathrm{ext}}({\rm{R}}.{\rm{A}}.)﹩ + ﹩{\delta }_{m}^{{ff}}(\mathrm{decl}.)﹩) in the u, g, r, i, and z bands. Color bars are to the right of each panel.
Paper: Photometric Recalibration of the SDSS Stripe 82 to a Few Millimagnitude Precision with the Stellar Color Regression Method and Gaia EDR3
Authors: Bowen Huang, Haibo Yuan
Figure caption: Histogram distributions of magnitude offsets in the u, g, r, i, and z bands before (left) and after (right) corrections of the ﹩{\delta }_{m}^{\mathrm{ext}}({\rm{R}}.{\rm{A}}.)﹩ and ﹩{\delta }_{m}^{{ff}}(\mathrm{decl}.)﹩. The Gaussian fitting curves are overplotted, with the σ values labeled.
Paper: Photometric Recalibration of the SDSS Stripe 82 to a Few Millimagnitude Precision with the Stellar Color Regression Method and Gaia EDR3
Authors: Bowen Huang, Haibo Yuan
Figure caption: The comparisons of ﹩{\delta }_{m}^{\mathrm{ext}}({\rm{R}}.{\rm{A}}.)﹩ for the south (top) and north (bottom) strips between the LAMOST and SDSS samples. The lines of equality are overplotted in red. Data points that are obtained with fewer than 80 stars are plotted in gray. The dispersion on the top of each panel considers only black points.
Paper: Photometric Recalibration of the SDSS Stripe 82 to a Few Millimagnitude Precision with the Stellar Color Regression Method and Gaia EDR3
Authors: Bowen Huang, Haibo Yuan
Figure caption: The variations of magnitude offset as a function of color g − i for the six SDSS CCD columns after corrections for ﹩{\delta }_{m}^{\mathrm{ext}}({\rm{R}}.{\rm{A}}.)﹩ and ﹩{\delta }_{c}^{{ff}}(\mathrm{decl}.)﹩.
Paper: Photometric Recalibration of the SDSS Stripe 82 to a Few Millimagnitude Precision with the Stellar Color Regression Method and Gaia EDR3
Authors: Bowen Huang, Haibo Yuan
Figure caption: Values of ﹩{\delta }_{c}^{{ff}}(\mathrm{decl}.)﹩ versus ﹩{\delta }_{g-r}^{{ff}}(\mathrm{decl}.)﹩ for the LAMOST and SDSS samples. The dashed lines mark the expected relations for each color. The dispersions of ﹩{\delta }_{c}^{{ff}}(\mathrm{decl}.)﹩ against the expected relations are marked.
Paper: Photometric Recalibration of the SDSS Stripe 82 to a Few Millimagnitude Precision with the Stellar Color Regression Method and Gaia EDR3
Authors: Bowen Huang, Haibo Yuan
Figure caption: The comparisons of ﹩{\delta }_{m}^{{ff}}(\mathrm{decl}.)﹩ between the LAMOST and SDSS samples. The lines of equality are overplotted in red. Data points that are obtained with fewer than 100 stars are plotted in gray. The dispersion on the top of each panel considers only black points.
Paper: Photometric Recalibration of the SDSS Stripe 82 to a Few Millimagnitude Precision with the Stellar Color Regression Method and Gaia EDR3
Authors: Bowen Huang, Haibo Yuan
Figure caption: Same as Figure 9 but for the v4.2 catalog.
Paper: A Census of Thermally Pulsing AGB Stars in the Andromeda Galaxy and a First Estimate of Their Contribution to the Global Dust Budget
Authors: Steven R. Goldman, Martha L. Boyer, Julianne Dalcanton, Iain McDonald, Léo Girardi, Benjamin F. Williams, Sundar Srinivasan, Karl Gordon
Figure caption: M31 clusters identified by Johnson et al. (2015) where we have also identified x-AGB candidates (red circles). On the left of each panel is an HST F110W image cutout with dimensions of four times the cluster apparent radius (APRAD; dashed blue circle). Additional AGB candidates that we have identified in these clusters are shown with orange circles. The right panel shows the SEDs of the dusty cluster sources (in red on the left) fit with a grid of oxygen-rich radiative transfer models using the Dusty Evolved Star Kit (Desk; Goldman 2020).
Paper: A Census of Thermally Pulsing AGB Stars in the Andromeda Galaxy and a First Estimate of Their Contribution to the Global Dust Budget
Authors: Steven R. Goldman, Martha L. Boyer, Julianne Dalcanton, Iain McDonald, Léo Girardi, Benjamin F. Williams, Sundar Srinivasan, Karl Gordon
Figure caption: Top: a map of the ratio of AGB-to-RGB stars in the PHAT footprint. The red areas represent older regions dominated by RGB stars, where more massive stars have already evolved through the AGB phase. The ratio of AGB-to-RGB stars beyond the 10 kpc ring is noisier due to low source density. We have excluded Bricks 1 and 3 due to confusion, Bricks 22 and 23 due to lower source counts, and the overlapping Brick regions due to miscounted/duplicated RGB stars. Bottom: an example dust map within the PHAT-footprint region created using the dust maps by Utomo et al. (2019), shown here to illustrate the locations of the star-forming rings.