adsabs/AstroCLIMB huggingface dataset:
Paper: The Eclipsing Binaries via Artificial Intelligence. II. Need for Speed in PHOEBE Forward Models
Authors: Marcin Wrona, Andrej Prša
Figure caption: (Top) Corner plot with posterior distributions after sampling the solutions found by all five ANNs during the cross-validation test (colors match the ones used for learning curves in Figure 9) and using PHOEBE (black color). Here we present results for synthetic light curve #4. True values are marked with magenta dashed lines on histograms and magenta dots on 2D plots. Above the histograms, we provide the average standard deviation of distributions for a given parameter ﹩{ \mathcal X }﹩ over all five ANNs ﹩\left({\langle \sigma \rangle }_{{ \mathcal X }}^{{\rm{ANN}}}\right)﹩ and for the standard deviation of the posterior distribution obtained with PHOEBE ﹩\left({\sigma }_{{ \mathcal X }}^{{\rm{PHOEBE}}}\right)﹩. (Bottom) Histograms of posterior distributions of the fitting parameters for the remaining three synthetic light curves. Line colors are the same as those used in corner plot.
Paper: The Eclipsing Binaries via Artificial Intelligence. II. Need for Speed in PHOEBE Forward Models
Authors: Marcin Wrona, Andrej Prša
Figure caption: 2D histograms showing the differences between the true parameter values and the highest log-probability estimates. The 2D histograms represent the average parameter shifts and scatter obtained using all five ANNs trained during the cross-validation test. The top row displays the orbital parameters e, ω, and i, plotted against their respective absolute or relative differences. The second and third rows present the main set of parameters ﹩e\sin \omega ﹩, ﹩e\cos \omega ﹩, ﹩\cos i﹩, (R1 + R2)/a, R2/R1, and T2/T1. The color scale represents the logarithm of the number of occurrences in each bin (logN).
Paper: The Eclipsing Binaries via Artificial Intelligence. II. Need for Speed in PHOEBE Forward Models
Authors: Marcin Wrona, Andrej Prša
Figure caption: Phase-folded light curves of one of the synthetic objects plotted alongside their respective PHOEBAI-fitted models. The left-hand plot shows the light curve with 100 data points, while the right-hand plot shows the same curve with 10,000 data points. Despite the drastic difference in sample size, the obtained parameters are consistent with the true values, which are ﹩e\sin \omega =-0.1726﹩; ﹩e\cos \omega =0.2489﹩; ﹩\cos i=0.0871﹩; (R1 + R2)/a = 0.2967; R2/R1 = 0.7485; and T2/T1 = 0.8195.
Paper: The Eclipsing Binaries via Artificial Intelligence. II. Need for Speed in PHOEBE Forward Models
Authors: Marcin Wrona, Andrej Prša
Figure caption: (Top) Learning curves for an ANN trained on five different subsets of the training set as part of a cross-validation test. In each variant, the training set consisted of 1 million entries, and the validation set of 250,000. In each variant, the validation set consisted of different objects, and the training set of the remaining part. (Bottom) Differences between the various learning curves become noticeable only when zooming in on the last 1000 steps of the training session.
Paper: The Eclipsing Binaries via Artificial Intelligence. II. Need for Speed in PHOEBE Forward Models
Authors: Marcin Wrona, Andrej Prša
Figure caption: Architecture of the best-performing deep ANN based on cross-validation tests, using 1,000,000 objects for the training set and 250,000 for the validation set. This ANN contains six HLs, each with 512 nodes. The AF for the first HL is elu, while sigmoid is used for the remaining HLs. The AF for the OL is linear. The optimization algorithm used is Adam.
Paper: The Eclipsing Binaries via Artificial Intelligence. II. Need for Speed in PHOEBE Forward Models
Authors: Marcin Wrona, Andrej Prša
Figure caption: Synthetic light curves illustrating the effect of varying dilution fractions (﹩{ \mathcal D }﹩) on light-curve amplitudes.
Paper: The Eclipsing Binaries via Artificial Intelligence. II. Need for Speed in PHOEBE Forward Models
Authors: Marcin Wrona, Andrej Prša
Figure caption: Phase-folded synthetic light curves that were used to evaluate the stability and performance of ANNs during cross validation.
Paper: The Stellar Abundances and Galactic Evolution Survey (SAGES). II. Machine Learning–based Stellar Parameters for 21 Million Stars from the First Data Release
Authors: Hongrui Gu, Zhou Fan, Gang Zhao, Huang Yang, Timothy C. Beers, Wei Wang, Jie Zheng, Jingkun Zhao, Chun Li, Yuqin Chen, Haibo Yuan, Haining Li, Kefeng Tan, Yihan Song, Ali Luo, Nan Song, Yujuan Liu
Figure caption: Left panel: The out-of-bag (OOB) scores for different combinations of hyperparameters are shown, with different colors representing different max_features values, and the x-axis representing different n_estimators values. The y-axis represents 1 −  OOB score, so a smaller value on the y-axis indicates better performance. For the selected n_estimators = 300, the accuracy is only 0.1% lower than when n_estimators = 750, but the computational speed is 2.5 times faster. Right panel: For n_estimators = 300 and max_features = “None,” the 1 − OOB score varies with the max_depth parameter. A smaller value on the y-axis indicates better performance. By testing each max_depth three times and averaging the results to obtain the mean curve (represented by the black dashed line), it can be seen that the optimal performance is achieved when max_depth = 17.
Paper: The Stellar Abundances and Galactic Evolution Survey (SAGES). II. Machine Learning–based Stellar Parameters for 21 Million Stars from the First Data Release
Authors: Hongrui Gu, Zhou Fan, Gang Zhao, Huang Yang, Timothy C. Beers, Wei Wang, Jie Zheng, Jingkun Zhao, Chun Li, Yuqin Chen, Haibo Yuan, Haining Li, Kefeng Tan, Yihan Song, Ali Luo, Nan Song, Yujuan Liu
Figure caption: Comparisons between the spectroscopic and photometric stellar parameters for the test set from Data Set 1, for five models (dwarf [Fe/H], giant [Fe/H], dwarf Teff, giant Teff, log g). The lower portion of each panel shows the number-density distribution of the difference between two spectroscopic and photometric stellar parameters. The mean and standard deviation are obtained from fitting the histograms, and their values are shown in the legends.
Paper: The Stellar Abundances and Galactic Evolution Survey (SAGES). II. Machine Learning–based Stellar Parameters for 21 Million Stars from the First Data Release
Authors: Hongrui Gu, Zhou Fan, Gang Zhao, Huang Yang, Timothy C. Beers, Wei Wang, Jie Zheng, Jingkun Zhao, Chun Li, Yuqin Chen, Haibo Yuan, Haining Li, Kefeng Tan, Yihan Song, Ali Luo, Nan Song, Yujuan Liu
Figure caption: Left two panels: number-density distribution of the training samples for Data Set 1 in the color–absolute magnitude diagram, and the histogram of [Fe/H] from different spectral survey catalogs. Right two panels: same as the left two panels but from the test set.
Paper: The Stellar Abundances and Galactic Evolution Survey (SAGES). II. Machine Learning–based Stellar Parameters for 21 Million Stars from the First Data Release
Authors: Hongrui Gu, Zhou Fan, Gang Zhao, Huang Yang, Timothy C. Beers, Wei Wang, Jie Zheng, Jingkun Zhao, Chun Li, Yuqin Chen, Haibo Yuan, Haining Li, Kefeng Tan, Yihan Song, Ali Luo, Nan Song, Yujuan Liu
Figure caption: Distribution of the data used in this work in the Gaia color–absolute magnitude diagram. The colors distinguish four different types of stars. The data used in this work are the dwarf (yellow) and giant (red) stars.
Paper: The Stellar Abundances and Galactic Evolution Survey (SAGES). II. Machine Learning–based Stellar Parameters for 21 Million Stars from the First Data Release
Authors: Hongrui Gu, Zhou Fan, Gang Zhao, Huang Yang, Timothy C. Beers, Wei Wang, Jie Zheng, Jingkun Zhao, Chun Li, Yuqin Chen, Haibo Yuan, Haining Li, Kefeng Tan, Yihan Song, Ali Luo, Nan Song, Yujuan Liu
Figure caption: Similar to Figure 2, but using data for Data Set 2.
Paper: The Stellar Abundances and Galactic Evolution Survey (SAGES). II. Machine Learning–based Stellar Parameters for 21 Million Stars from the First Data Release
Authors: Hongrui Gu, Zhou Fan, Gang Zhao, Huang Yang, Timothy C. Beers, Wei Wang, Jie Zheng, Jingkun Zhao, Chun Li, Yuqin Chen, Haibo Yuan, Haining Li, Kefeng Tan, Yihan Song, Ali Luo, Nan Song, Yujuan Liu
Figure caption: Similar to Figure 6, but using the test set from Data Set 2.
Paper: The Stellar Abundances and Galactic Evolution Survey (SAGES). II. Machine Learning–based Stellar Parameters for 21 Million Stars from the First Data Release
Authors: Hongrui Gu, Zhou Fan, Gang Zhao, Huang Yang, Timothy C. Beers, Wei Wang, Jie Zheng, Jingkun Zhao, Chun Li, Yuqin Chen, Haibo Yuan, Haining Li, Kefeng Tan, Yihan Song, Ali Luo, Nan Song, Yujuan Liu
Figure caption: Flow chart of this work.
Paper: Spectuner: A Framework for Automated Line Identification of Interstellar Molecules
Authors: Yisheng Qiu, Tianwei Zhang, Thomas Möller, Xue-Jian Jiang, Zihao Song, Huaxi Chen, Donghui Quan
Figure caption: Upper panels: comparison of the parameters estimated by X. Liu et al. (2022), labeled “test,” with our best-fitting parameters, labeled “pred.” When the original study suggests multiple components, we compare the components with the highest integrated intensity. The red and blue dots represent the results based on the peak-matching and χ2 loss functions, respectively. Each data point compares the parameters of a molecule at one state. X. Liu et al. (2022) adopted fixed values for source size and excitation temperature, and fit the column density, velocity width, and velocity offset. Lower panels: comparison of the parameters derived using the peak-matching and χ2 loss functions.
Paper: Spectuner: A Framework for Automated Line Identification of Interstellar Molecules
Authors: Yisheng Qiu, Tianwei Zhang, Thomas Möller, Xue-Jian Jiang, Zihao Song, Huaxi Chen, Donghui Quan
Figure caption: Line identification of acetone (CH3COCH3) in Orion KL-Tianma. The black solid lines show the observed spectrum, and the black dashed lines indicate the CH3COCH3 spectral lines identified by X. Liu et al. (2022). The gray horizontal lines represent the threshold used by our algorithm to identify a peak. The red lines show the best-fitting spectrum using the peak-matching loss. While the best-fitting spectrum predicts most peaks at the identified lines by X. Liu et al. (2022), some peaks are too faint to be identified by our peak finder. This explains the low recall of CH3COCH3, as shown in Figure 13.
Paper: Spectuner: A Framework for Automated Line Identification of Interstellar Molecules
Authors: Yisheng Qiu, Tianwei Zhang, Thomas Möller, Xue-Jian Jiang, Zihao Song, Huaxi Chen, Donghui Quan
Figure caption: Incorrect line assignment of HNCO in Orion KL-GBT by our algorithm. The black vertical dashed lines indicate the two HNCO lines identified by D. T. Frayer et al. (2015). Our algorithm incorrectly assigns them to HN13CO, as indicated by the red lines, which show the best-fitting spectrum of HN13CO using the peak-matching loss function.
Paper: Spectuner: A Framework for Automated Line Identification of Interstellar Molecules
Authors: Yisheng Qiu, Tianwei Zhang, Thomas Möller, Xue-Jian Jiang, Zihao Song, Huaxi Chen, Donghui Quan
Figure caption: Spectral lines of methanol in W51-Mopra. The black solid lines show the observed spectrum from Y. Watanabe et al. (2017), and the black vertical lines are spectral lines of methanol identified by Y. Watanabe et al. (2017). The red and blue lines show the fitting results using one and two physical components, respectively. The recall of each fitting result is indicated in the legend. These plots imply that the spectrum of methanol can be better modeled using two physical components.
Paper: Spectuner: A Framework for Automated Line Identification of Interstellar Molecules
Authors: Yisheng Qiu, Tianwei Zhang, Thomas Möller, Xue-Jian Jiang, Zihao Song, Huaxi Chen, Donghui Quan
Figure caption: Identified lines of H2CS in Orion KL-GBT. The red lines show our best-fitting spectrum of H2CS using the peak-matching loss function. The result implies a different velocity component of H2CS in addition to the one identified by D. T. Frayer et al. (2015).
Paper: Spectuner: A Framework for Automated Line Identification of Interstellar Molecules
Authors: Yisheng Qiu, Tianwei Zhang, Thomas Möller, Xue-Jian Jiang, Zihao Song, Huaxi Chen, Donghui Quan
Figure caption: Recall and precision of species identified in Orion KL-Tianma. The red and blue bars represent the results using the peak-matching and χ2 loss functions, respectively. Definitions of recall and precision are provided in Section 4.1.
Paper: Spectuner: A Framework for Automated Line Identification of Interstellar Molecules
Authors: Yisheng Qiu, Tianwei Zhang, Thomas Möller, Xue-Jian Jiang, Zihao Song, Huaxi Chen, Donghui Quan
Figure caption: Best-fitting results for Orion KL-GBT. Black lines represent the observed spectrum. Left and right panels show the results using the peak-matching and χ2 loss functions, respectively. The peak-matching loss function is described in Section 2.4.
Paper: Spectuner: A Framework for Automated Line Identification of Interstellar Molecules
Authors: Yisheng Qiu, Tianwei Zhang, Thomas Möller, Xue-Jian Jiang, Zihao Song, Huaxi Chen, Donghui Quan
Figure caption: Recall and precision of species identified in Orion KL-GBT. The red and blue bars represent the results using the peak-matching and χ2 loss functions, respectively. Definitions of recall and precision are provided in Section 4.1.
Paper: Spectuner: A Framework for Automated Line Identification of Interstellar Molecules
Authors: Yisheng Qiu, Tianwei Zhang, Thomas Möller, Xue-Jian Jiang, Zihao Song, Huaxi Chen, Donghui Quan
Figure caption: Best-fitting results for Orion KL-Tianma. Black lines represent the observed spectrum. Left and right panels show the results using the peak-matching and χ2 loss functions, respectively. The line survey data are introduced in Section 3.3. The peak-matching loss function is described in Section 2.4.
Paper: AstroSat UVIT Survey of M31: New Compact Source Catalog
Authors: Denis Leahy, Sujith Ranasinghe, Saptadwipa Mitra, Kripansh Rawal
Figure caption: The DSS Poss2 blue filter image of M31 (gray-scale image). R.A. and decl. are J2000 coordinates. The white ellipse shows the D25 ellipse for M31 (A. Gil de Paz et al. 2007). The locations of the fields for the UVIT survey of M31 are shown by the circles. F8, F21, and F23 to F25 are new fields marked with thicker black circles. Fields F20 and F22 (gray circles) were not observed because of stars in those fields above the brightness limit for instrument safety.
Paper: AstroSat UVIT Survey of M31: New Compact Source Catalog
Authors: Denis Leahy, Sujith Ranasinghe, Saptadwipa Mitra, Kripansh Rawal
Figure caption: A small region (2﹩\mathop{.}\limits^{{\rm{^{\prime} }}}﹩2 wide by 1﹩\mathop{.}\limits^{{\rm{^{\prime} }}}﹩5 high) in the field F1 showing the F148W image and the sources detected using CCDLAB. Sources with eccentricity e > 0.8 are circled with cyan, sources with FWHMY > 5 are circled with magenta, sources with FWHMX > 5 are circled with blue, sources with 0.75 < e < 0.8 are circled with red, and the remainder are circled with green. The sources kept in the final selection have e < 0.8, FWHMX < 5, and FWHMY < 5 and are marked by yellow squares.
Paper: AstroSat UVIT Survey of M31: New Compact Source Catalog
Authors: Denis Leahy, Sujith Ranasinghe, Saptadwipa Mitra, Kripansh Rawal
Figure caption: Top panel: map of UVIT F148W sources (red crosses) from the M31 survey. Bottom panel: map of UVIT F154W sources (red crosses) overlaid on the F148W sources (gray crosses). The sources shown have a minimum signal-to-noise (S/N) of 5.
Paper: AstroSat UVIT Survey of M31: New Compact Source Catalog
Authors: Denis Leahy, Sujith Ranasinghe, Saptadwipa Mitra, Kripansh Rawal
Figure caption: Top panel: map of UVIT F169M sources (red crosses) overlaid on the F148W sources (gray crosses). Bottom panel: map of UVIT F172M sources (red crosses) overlaid on the F148W sources (gray crosses). The sources shown have a minimum S/N of 5.
Paper: AstroSat UVIT Survey of M31: New Compact Source Catalog
Authors: Denis Leahy, Sujith Ranasinghe, Saptadwipa Mitra, Kripansh Rawal
Figure caption: Top panel: map of UVIT N219M sources (red crosses) overlaid on the F148W sources (gray crosses). Bottom panel: map of UVIT N279N sources (red crosses) overlaid on the F148W sources (gray crosses). The sources shown have a minimum S/N of 5.
Paper: AstroSat UVIT Survey of M31: New Compact Source Catalog
Authors: Denis Leahy, Sujith Ranasinghe, Saptadwipa Mitra, Kripansh Rawal
Figure caption: The distribution of source fluxes for each of the filters: F148W, F154W, F169M, F172M, N219M, and N279N.
Paper: AstroSat UVIT Survey of M31: New Compact Source Catalog
Authors: Denis Leahy, Sujith Ranasinghe, Saptadwipa Mitra, Kripansh Rawal
Figure caption: Flux comparison for dual sources: those detected twice in the same filter in the overlap region of different fields (shown in Figure 1).
Paper: The S-PLUS Ultra-short Survey: Photometric Recalibration with the Best Star Database
Authors: Xiaolu Li, Kai Xiao, Yang Huang, Haibo Yuan, Yanke Tang, Timothy C. Beers, Bowen Huang, Mingyang Ma, Pedro K. Humire, Alvaro Alvarez-Candal, Federico Sestito, Ning Gai, Yongna Mao, Hongrui Gu, Zhenzhao Tao, Lin Yang, Shuai Xu, Rong Hu
Figure caption: An example showing the CCD spatial distribution of the magnitude difference between Aper_3 and Pstotal for all observed stars within nine tiles. The panels from top to bottom show the results of the u, J0378, J0395, J0410, J0430, g, J0515, r, J0660, i, J0861, and z bands, with band labels on the right and a color bar on top. The selected nine tiles (SHORTS-STRIPE82_0087, SHORTS-STRIPE82_0088, SHORTS-STRIPE82_0090, SHORTS-STRIPE82_0092, SHORTS-STRIPE82_0094, SHORTS-STRIPE82_0098, SHORTS-STRIPE82_0100, SHORTS-STRIPE82_0104, and SHORTS-STRIPE82_0109) are ordered from left to right, with the tile number marked between the first-row panels and the color bar. The median value of the magnitude difference between Aper_3 and Pstotal is indicated in each panel. Note that the color bar ranges from −1 × 10−5 to 1 × 10−5.
Paper: The S-PLUS Ultra-short Survey: Photometric Recalibration with the Best Star Database
Authors: Xiaolu Li, Kai Xiao, Yang Huang, Haibo Yuan, Yanke Tang, Timothy C. Beers, Bowen Huang, Mingyang Ma, Pedro K. Humire, Alvaro Alvarez-Candal, Federico Sestito, Ning Gai, Yongna Mao, Hongrui Gu, Zhenzhao Tao, Lin Yang, Shuai Xu, Rong Hu
Figure caption: Polynomial fits of the intrinsic colors, with respect to Teff and [Fe/H], for the selected stars. For each panel, the colors of the points represent the logarithm of the number density, calculated using Gaussian kernel density estimation. The fit results after 3σ clipping are shown in each panel, with the red and blue curves representing results for [Fe/H] = 0 and [Fe/H] = −1, respectively. The fitting residuals are labeled in red. The stellar color is predicted by GBP/RP − m, where m denotes the USS magnitude before recalibration (the top 12 panels), the USS magnitude after recalibration (the middle 12 panels), or the USS XPSP magnitude (the bottom 12 panels).
Paper: The S-PLUS Ultra-short Survey: Photometric Recalibration with the Best Star Database
Authors: Xiaolu Li, Kai Xiao, Yang Huang, Haibo Yuan, Yanke Tang, Timothy C. Beers, Bowen Huang, Mingyang Ma, Pedro K. Humire, Alvaro Alvarez-Candal, Federico Sestito, Ning Gai, Yongna Mao, Hongrui Gu, Zhenzhao Tao, Lin Yang, Shuai Xu, Rong Hu
Figure caption: Same as Figure 9, but for the crossmatching radius with Gaia. To clearly display the spatial distribution of the astrometric offsets between the USS and Gaia, we calculated the median value of the astrometric offset for each tile. We then subtracted this median value from all offsets of stars within the tile, and the resulting astrometric offsets are shown in each panel. The tile_ID and the median value of the astrometric offset are labeled in the bottom-right and the top-left corner of each panel, respectively.
Paper: The S-PLUS Ultra-short Survey: Photometric Recalibration with the Best Star Database
Authors: Xiaolu Li, Kai Xiao, Yang Huang, Haibo Yuan, Yanke Tang, Timothy C. Beers, Bowen Huang, Mingyang Ma, Pedro K. Humire, Alvaro Alvarez-Candal, Federico Sestito, Ning Gai, Yongna Mao, Hongrui Gu, Zhenzhao Tao, Lin Yang, Shuai Xu, Rong Hu
Figure caption: The left panel shows the spatial variations of the medium-scale flat field after 20 × 20 binning for all bands. The right panel is similar to the left panel, but displays the difference between the results of each band and those of the r band. The bands and color bars are shown in the bottom-left corner of each subpanel and at the top of each panel, respectively. Nine adjacent points from the annular and central regions for each band are selected and marked with red plus signs in each panel. The difference between the median values of the two regions, in units of millimagnitude, is calculated and indicated in the bottom-right corner of each subpanel.
Paper: The S-PLUS Ultra-short Survey: Photometric Recalibration with the Best Star Database
Authors: Xiaolu Li, Kai Xiao, Yang Huang, Haibo Yuan, Yanke Tang, Timothy C. Beers, Bowen Huang, Mingyang Ma, Pedro K. Humire, Alvaro Alvarez-Candal, Federico Sestito, Ning Gai, Yongna Mao, Hongrui Gu, Zhenzhao Tao, Lin Yang, Shuai Xu, Rong Hu
Figure caption: Same as Figure 6, but for the correlation of the 400 values for each of the two bands. For each panel, the correlation coefficients are marked in black in the bottom-right corner. The linear fitting lines are shown as red lines, and the slopes of the lines are marked in red in the top-left corners. The black dashed lines denote y = x in each panel. A histogram of the distribution of the correlation coefficients is shown in the top-right corner.
Paper: The S-PLUS Ultra-short Survey: Photometric Recalibration with the Best Star Database
Authors: Xiaolu Li, Kai Xiao, Yang Huang, Haibo Yuan, Yanke Tang, Timothy C. Beers, Bowen Huang, Mingyang Ma, Pedro K. Humire, Alvaro Alvarez-Candal, Federico Sestito, Ning Gai, Yongna Mao, Hongrui Gu, Zhenzhao Tao, Lin Yang, Shuai Xu, Rong Hu
Figure caption: An example showing the difference between the SDSS and USS r-band magnitudes before (the top-left subpanel) and after recalibration (the bottom-left subpanel), and between the PS1 and USS r-band magnitudes before (the top-right subpanel) and after recalibration (the bottom-right subpanel). For each subpanel, the red line represents the result of linear fitting; the slope (red) and the standard deviation of the residuals (black) are indicated. A color bar coding the USS r-band magnitudes is shown on the right.
Paper: The S-PLUS Ultra-short Survey: Photometric Recalibration with the Best Star Database
Authors: Xiaolu Li, Kai Xiao, Yang Huang, Haibo Yuan, Yanke Tang, Timothy C. Beers, Bowen Huang, Mingyang Ma, Pedro K. Humire, Alvaro Alvarez-Candal, Federico Sestito, Ning Gai, Yongna Mao, Hongrui Gu, Zhenzhao Tao, Lin Yang, Shuai Xu, Rong Hu
Figure caption: Normalized transmission functions, as a function of wavelength, for the r and i bands. The labels are marked in each panel. The green curves denote the USS system transmission curves. For the top panel, the pink dashed curves denote the PS1 photometric transmission curves. For the bottom panel, the pink dashed curves denote the SDSS system transmission curves.
Paper: The S-PLUS Ultra-short Survey: Photometric Recalibration with the Best Star Database
Authors: Xiaolu Li, Kai Xiao, Yang Huang, Haibo Yuan, Yanke Tang, Timothy C. Beers, Bowen Huang, Mingyang Ma, Pedro K. Humire, Alvaro Alvarez-Candal, Federico Sestito, Ning Gai, Yongna Mao, Hongrui Gu, Zhenzhao Tao, Lin Yang, Shuai Xu, Rong Hu
Figure caption: Correlation plots of the normalized FWHM for each of the two bands for SHORTS-STRIPE82_0090. The gray dashed lines denote y = x in each panel.