adsabs/AstroCLIMB huggingface dataset:
Paper: A Reanalysis of Public Galactic Bulge Gravitational Microlensing Events from OGLE-III and -IV
Authors: Nathan Golovich, William Dawson, Fran Bartolić, Casey Y. Lam, Jessica R. Lu, Michael S. Medford, Michael D. Schneider, George Chapline, Edward F. Schlafly, Alex Drlica-Wagner, Kerianne Pruett
Figure caption: Posterior corner plot for OGLE-III BLG 156.7.141434 (see Figure 5) covering the microlensing model parameters with and without the GP component of the model.
Paper: A Reanalysis of Public Galactic Bulge Gravitational Microlensing Events from OGLE-III and -IV
Authors: Nathan Golovich, William Dawson, Fran Bartolić, Casey Y. Lam, Jessica R. Lu, Michael S. Medford, Michael D. Schneider, George Chapline, Edward F. Schlafly, Alex Drlica-Wagner, Kerianne Pruett
Figure caption: Posterior corner plot for OGLE-III BLG 102.7.44461. The strongest correlations are between microlensing parameters. There is a slight correlation between t E and the Matérn-3/2 kernel parameters; however, in general, the GP hyperparameters are much more well-behaved than the microlensing parameters.
Paper: A Reanalysis of Public Galactic Bulge Gravitational Microlensing Events from OGLE-III and -IV
Authors: Nathan Golovich, William Dawson, Fran Bartolić, Casey Y. Lam, Jessica R. Lu, Michael S. Medford, Michael D. Schneider, George Chapline, Edward F. Schlafly, Alex Drlica-Wagner, Kerianne Pruett
Figure caption: Example OGLE-III light curve (OGLE BLG 122.6.83113) with results from our MCMC analysis. This figure is analogous to Figure 5 but with a much simpler GP component and a clear parallax signal. Despite most of the parallax signal lying in the seasonal gaps in observability, the parallax model is still a better fit (PSPL+parallax+GP to PSPL+GP likelihood-ratio = 4.8).
Paper: A Reanalysis of Public Galactic Bulge Gravitational Microlensing Events from OGLE-III and -IV
Authors: Nathan Golovich, William Dawson, Fran Bartolić, Casey Y. Lam, Jessica R. Lu, Michael S. Medford, Michael D. Schneider, George Chapline, Edward F. Schlafly, Alex Drlica-Wagner, Kerianne Pruett
Figure caption: Example OGLE-IV light curve (OGLE-IV BLG 514.15.53029) with results from our MCMC analysis. This figure is analogous to Figure 5 except with OGLE-IV data from Mróz et al. (2019). It shows the results for the OGLE-IV event with the largest Einstein crossing time. This event has the most dramatic apparent parallax signal; however, the vast majority is fit within the observing gaps (PSPL+parallax+GP to PSPL+GP likelihood-ratio = 3.0).
Paper: A Reanalysis of Public Galactic Bulge Gravitational Microlensing Events from OGLE-III and -IV
Authors: Nathan Golovich, William Dawson, Fran Bartolić, Casey Y. Lam, Jessica R. Lu, Michael S. Medford, Michael D. Schneider, George Chapline, Edward F. Schlafly, Alex Drlica-Wagner, Kerianne Pruett
Figure caption: Example OGLE-III light curve (OGLE-III BLG 122.1.184151) with results from our MCMC analysis. This figure is analogous to Figure 5 and shows the results for the OGLE-III event with the largest Einstein crossing time. There is strong parallax (PSPL+parallax+GP to PSPL+GP likelihood-ratio = 8.8), but again much is apparently missing as it occurs in a gap in the data.
Paper: Recovering the CMB Signal with Machine Learning
Authors: Guo-Jian Wang, Hong-Liang Shi, Ye-Peng Yan, Jun-Qing Xia, Yan-Yun Zhao, Si-Yu Li, Jun-Feng Li
Figure caption: Power spectrum of the recovered CMB map (upper panel, using a bin size of 1), and the difference between this spectrum and that of the simulated CMB map (lower panel, using a bin size of 30).
Paper: Recovering the CMB Signal with Machine Learning
Authors: Guo-Jian Wang, Hong-Liang Shi, Ye-Peng Yan, Jun-Qing Xia, Yan-Yun Zhao, Si-Yu Li, Jun-Feng Li
Figure caption: Recovered CMB temperature map using the neural network. Upper panel: the simulated pure CMB map. Middle panel: the CMB map recovered by the neural network. Lower panel: the residual map of the recovered CMB map.
Paper: Recovering the CMB Signal with Machine Learning
Authors: Guo-Jian Wang, Hong-Liang Shi, Ye-Peng Yan, Jun-Qing Xia, Yan-Yun Zhao, Si-Yu Li, Jun-Feng Li
Figure caption: The general structure of the network model used for the component separation of a CMB map. The network contains symmetric convolutional layers (“conv”) and deconvolution layers (“deconv”). Skip connections (the dashed lines) are connected symmetrically from each convolutional layer to its corresponding mirrored deconvolutional layer.
Paper: Recovering the CMB Signal with Machine Learning
Authors: Guo-Jian Wang, Hong-Liang Shi, Ye-Peng Yan, Jun-Qing Xia, Yan-Yun Zhao, Si-Yu Li, Jun-Feng Li
Figure caption: Deviations of the recovered power spectra compared with the cosmic variance.
Paper: Recovering the CMB Signal with Machine Learning
Authors: Guo-Jian Wang, Hong-Liang Shi, Ye-Peng Yan, Jun-Qing Xia, Yan-Yun Zhao, Si-Yu Li, Jun-Feng Li
Figure caption: Five small patches with 3 × 3 deg2, selected from Figure 2. These patches are selected from the north pole to the south pole, with different latitudes.
Paper: Recovering the CMB Signal with Machine Learning
Authors: Guo-Jian Wang, Hong-Liang Shi, Ye-Peng Yan, Jun-Qing Xia, Yan-Yun Zhao, Si-Yu Li, Jun-Feng Li
Figure caption: Left panel: rms for 18 zonal bands in the residual map, each being 10° wide in Galactic latitudes. Right panel: rms of the contaminated CMB maps at 100 GHz and 143 GHz.
Paper: Recovering the CMB Signal with Machine Learning
Authors: Guo-Jian Wang, Hong-Liang Shi, Ye-Peng Yan, Jun-Qing Xia, Yan-Yun Zhao, Si-Yu Li, Jun-Feng Li
Figure caption: The same as Figure 5, but without instrument noise and instrument beam effects being included in the training data and the testing data.
Paper: Recovering the CMB Signal with Machine Learning
Authors: Guo-Jian Wang, Hong-Liang Shi, Ye-Peng Yan, Jun-Qing Xia, Yan-Yun Zhao, Si-Yu Li, Jun-Feng Li
Figure caption: The two blocks (the areas inside the brown lines) of the full-sky contaminated CMB map used to train the neural network.
Paper: Recovering the CMB Signal with Machine Learning
Authors: Guo-Jian Wang, Hong-Liang Shi, Ye-Peng Yan, Jun-Qing Xia, Yan-Yun Zhao, Si-Yu Li, Jun-Feng Li
Figure caption: Power spectra of the recovered CMB map (upper panels, using a bin size of 1) and the difference between these spectra and that of the simulated CMB maps (lower panels, using a bin size of 30) when using 1, 2, 3, and 4 frequency bands, respectively.
Paper: Recovering the CMB Signal with Machine Learning
Authors: Guo-Jian Wang, Hong-Liang Shi, Ye-Peng Yan, Jun-Qing Xia, Yan-Yun Zhao, Si-Yu Li, Jun-Feng Li
Figure caption: The same as Figure 7, but now the Planck mask is used when calculating C  ℓ .
Paper: Recovering the CMB Signal with Machine Learning
Authors: Guo-Jian Wang, Hong-Liang Shi, Ye-Peng Yan, Jun-Qing Xia, Yan-Yun Zhao, Si-Yu Li, Jun-Feng Li
Figure caption: Power spectra of a simulated CMB map, considering Planck beam effects, and power spectra of instrument noise at nine observational frequency bands.
Paper: Recovering the CMB Signal with Machine Learning
Authors: Guo-Jian Wang, Hong-Liang Shi, Ye-Peng Yan, Jun-Qing Xia, Yan-Yun Zhao, Si-Yu Li, Jun-Feng Li
Figure caption: Power spectrum of the recovered Planck CMB map (upper panel, using a bin size of 1), and the difference between this spectrum and that of the Planck Commander foreground-cleaned CMB map (lower panel, using a bin size of 30).
Paper: Recovering the CMB Signal with Machine Learning
Authors: Guo-Jian Wang, Hong-Liang Shi, Ye-Peng Yan, Jun-Qing Xia, Yan-Yun Zhao, Si-Yu Li, Jun-Feng Li
Figure caption: Power spectra of the recovered CMB maps (upper panels, using a bin size of 1), and the difference between these spectra and those of the simulated CMB maps (lower panels, using a bin size of 30), when using two frequency bands. The left panel is for a 50% noise level and the right panel is for a 30% noise level.
Paper: Recovering the CMB Signal with Machine Learning
Authors: Guo-Jian Wang, Hong-Liang Shi, Ye-Peng Yan, Jun-Qing Xia, Yan-Yun Zhao, Si-Yu Li, Jun-Feng Li
Figure caption: Planck CMB temperature map recovered by the neural network. Upper panel: the Planck Commander foreground-cleaned CMB map. Middle panel: the CMB map recovered by the neural network. Lower panel: the residual map of the recovered CMB map.
Paper: Recovering the CMB Signal with Machine Learning
Authors: Guo-Jian Wang, Hong-Liang Shi, Ye-Peng Yan, Jun-Qing Xia, Yan-Yun Zhao, Si-Yu Li, Jun-Feng Li
Figure caption: The same as Figure 14, but now the Planck mask is applied to the training set.
Paper: Recovering the CMB Signal with Machine Learning
Authors: Guo-Jian Wang, Hong-Liang Shi, Ye-Peng Yan, Jun-Qing Xia, Yan-Yun Zhao, Si-Yu Li, Jun-Feng Li
Figure caption: Five small patches with 3 × 3 deg2, selected from Figure 12. These patches are selected from the north pole to the south pole, with different latitudes.
Paper: Recovering the CMB Signal with Machine Learning
Authors: Guo-Jian Wang, Hong-Liang Shi, Ye-Peng Yan, Jun-Qing Xia, Yan-Yun Zhao, Si-Yu Li, Jun-Feng Li
Figure caption: Recovered CMB temperature map and power spectrum when using the first block of Figure 11 to train the network. Upper left panel: the simulated pure CMB map. Upper right panel: the CMB map recovered by the neural network. Lower left panel: the residual map of the recovered CMB map. Lower right panel: the power spectrum of the recovered CMB map (with a bin size of 5), and the difference between this spectrum and that of the simulated CMB map (with a bin size of 30).
Paper: Recovering the CMB Signal with Machine Learning
Authors: Guo-Jian Wang, Hong-Liang Shi, Ye-Peng Yan, Jun-Qing Xia, Yan-Yun Zhao, Si-Yu Li, Jun-Feng Li
Figure caption: Power spectra of thermal dust maps for cases 1, 3, 4, and 5, respectively. The black lines refer to the power spectrum of the dust template, while the others are the power spectra of ten sets of simulated thermal dust maps.
Paper: Recovering the CMB Signal with Machine Learning
Authors: Guo-Jian Wang, Hong-Liang Shi, Ye-Peng Yan, Jun-Qing Xia, Yan-Yun Zhao, Si-Yu Li, Jun-Feng Li
Figure caption: Power spectra of the recovered CMB maps, and the difference between these spectra and those of the simulated CMB maps, for cases 2, 3, 4, and 5, respectively.
Paper: Recovering the CMB Signal with Machine Learning
Authors: Guo-Jian Wang, Hong-Liang Shi, Ye-Peng Yan, Jun-Qing Xia, Yan-Yun Zhao, Si-Yu Li, Jun-Feng Li
Figure caption: Three small patches with 3 × 3 deg2, selected from Figure 22. These patches are selected with different latitudes.
Paper: Recovering the CMB Signal with Machine Learning
Authors: Guo-Jian Wang, Hong-Liang Shi, Ye-Peng Yan, Jun-Qing Xia, Yan-Yun Zhao, Si-Yu Li, Jun-Feng Li
Figure caption: EE and BB power spectra calculated from the recovered CMB Q/U maps in Figure 20 (left panels, using a bin size of 5), and the difference between these spectra and those of the simulated maps (right panels, using a bin size of 30).
Paper: Recovering the CMB Signal with Machine Learning
Authors: Guo-Jian Wang, Hong-Liang Shi, Ye-Peng Yan, Jun-Qing Xia, Yan-Yun Zhao, Si-Yu Li, Jun-Feng Li
Figure caption: Three small patches with 3 × 3 deg2, selected from Figure 24. These patches are selected with different latitudes.
Paper: Recovering the CMB Signal with Machine Learning
Authors: Guo-Jian Wang, Hong-Liang Shi, Ye-Peng Yan, Jun-Qing Xia, Yan-Yun Zhao, Si-Yu Li, Jun-Feng Li
Figure caption: The same as Figure 22, but now using the second block of Figure 11 to train the network.
Paper: Recovering the CMB Signal with Machine Learning
Authors: Guo-Jian Wang, Hong-Liang Shi, Ye-Peng Yan, Jun-Qing Xia, Yan-Yun Zhao, Si-Yu Li, Jun-Feng Li
Figure caption: The same as the Figure 22, but now the Planck mask is adopted in the training data.
Paper: Recovering the CMB Signal with Machine Learning
Authors: Guo-Jian Wang, Hong-Liang Shi, Ye-Peng Yan, Jun-Qing Xia, Yan-Yun Zhao, Si-Yu Li, Jun-Feng Li
Figure caption: Recovered CMB Q (upper panels) and U (lower panels) maps for the CMB-S4 experiment.
Paper: Validations and Corrections of the SFD and Planck Reddening Maps Based on LAMOST and Gaia Data
Authors: Yang Sun, Haibo Yuan, Bingqiu Chen
Figure caption: Spatial distribution of the pixels eliminated in the four reddening maps in Galactic coordinates. Gray, orange, pink, and light-blue dots represent pixels removed in four, three, two, and one maps, respectively. The black circles represent pixels where dust clouds similar to those in Figure 7 are found.
Paper: Validations and Corrections of the SFD and Planck Reddening Maps Based on LAMOST and Gaia Data
Authors: Yang Sun, Haibo Yuan, Bingqiu Chen
Figure caption: Three examples of sight lines that show large Stdfit values (dots above the black dashed lines in Figure 6). Dust clouds far above the Galactic disk (Z ∼ 500 pc) are found as jumps of k in the panels.
Paper: Validations and Corrections of the SFD and Planck Reddening Maps Based on LAMOST and Gaia Data
Authors: Yang Sun, Haibo Yuan, Bingqiu Chen
Figure caption: All-sky distributions of ﹩E{\left(B-V\right)}_{\mathrm{LAMOST}}-E{\left(B-V\right)}_{\mathrm{map}}﹩ (left) and k (right). The panels top to bottom show the SFD, Planck2014-R, Planck2014-Tau, and Planck2019–Tau maps, respectively.(The data used to create this figure are available.)
Paper: Validations and Corrections of the SFD and Planck Reddening Maps Based on LAMOST and Gaia Data
Authors: Yang Sun, Haibo Yuan, Bingqiu Chen
Figure caption: Same as Figure 1 but for the G BP − G color. Note that for the SFD, Planck2014-Tau, and Planck2019–Tau maps, the lower the stellar temperatures, the lower the reddening values, and the larger the reddening coefficients.
Paper: Validations and Corrections of the SFD and Planck Reddening Maps Based on LAMOST and Gaia Data
Authors: Yang Sun, Haibo Yuan, Bingqiu Chen
Figure caption: Left: spatial distribution of the number of stars in each pixel for the SFD map. Note that due to independent 3σ clipping for each extinction map, the numbers of stars in each pixel for the four extinction maps may be slightly different. Right: histogram distribution of the number of remaining sources in each pixel after 3σ clipping. The vertical black line indicates N/pix = 10.
Paper: Validations and Corrections of the SFD and Planck Reddening Maps Based on LAMOST and Gaia Data
Authors: Yang Sun, Haibo Yuan, Bingqiu Chen
Figure caption: Spatial distributions of correction factor errors k err in Galactic coordinates for different reddening maps.(The data used to create this figure are available.)
Paper: Scalar Quadratic Maximum-likelihood Estimators for the CMB Cross-power Spectrum
Authors: Jiming Chen, Shamik Ghosh, Wen Zhao
Figure caption: Plot of the results for the TB and EB power-spectrum estimates for the realistic space-based CMB experiment; the upper and lower panels are TB and EB, respectively. The observed sky is simulated at NSIDE = 512 with ﹩{{\ell }}_{\max }﹩ = 1024, and the noise level is set to 3μK-arcmin. The input power spectra are shown with the black curve. The classic QML method results are shown with an orange line, and the QML-SZ method results with a green line. These results are computed at NSIDE = 16. We also show PCL estimator results, obtained with NaMaster, with δ  c  = 6° C2 apodization, with blue line. The gray region denotes the analytical approximation of the error bounds. The data points are the mean of 1000 estimates, and the error bars are given by the standard deviation of the estimates.
Paper: Scalar Quadratic Maximum-likelihood Estimators for the CMB Cross-power Spectrum
Authors: Jiming Chen, Shamik Ghosh, Wen Zhao
Figure caption: Plot of the results of the TB- and EB-mode power-spectrum estimates for a realistic ground-based CMB experiment with homogeneous noise; the upper panel and the lower panel are TB and EB, respectively. The observed sky with a 3 μK-arcmin noise is simulated at NSIDE = 512 with ﹩{{\ell }}_{\max }﹩ = 1024. The input B-mode power spectrum is shown with the dotted black curve. The classic QML method results are computed at NSIDE = 32 with ﹩{{\ell }}_{\max }﹩ = 96 (orange), and the QML-SZ method results are computed at NSIDE = 32 with ﹩{{\ell }}_{\max }﹩ = 96 (green). We also show PCL estimator results, obtained with NaMaster, using δ  c  = 10° for r = 0.05 (blue). The gray region denotes the optimal error bounds. The data points are the mean of 1000 estimates, and the error bar is given by the standard deviation of the estimators.
Paper: Scalar Quadratic Maximum-likelihood Estimators for the CMB Cross-power Spectrum
Authors: Jiming Chen, Shamik Ghosh, Wen Zhao
Figure caption: Plot of the results for TE-mode power-spectrum estimates for the realistic space-based CMB experiment. The observed sky is simulated at NSIDE = 512 with ﹩{{\ell }}_{\max }﹩ = 1024 and the noise level is set to 3μ K-arcmin. The input TE-mode power spectrum is shown with the black curve. The classic QML method results are shown by the orange line and the QML-SZ method results with the green line. These results are computed at NSIDE = 16. We also show the PCL estimator results, obtained with NaMaster, with δ  c  = 6° C2 apodization, with the blue line. The gray region denotes the analytical approximation of the error bounds. The data points are the mean of 1000 estimates, and the error bars are given by the standard deviation of the estimates.
Paper: Scalar Quadratic Maximum-likelihood Estimators for the CMB Cross-power Spectrum
Authors: Jiming Chen, Shamik Ghosh, Wen Zhao
Figure caption: Binary masks showing the observed sky patch for a space-based experiment (top) and for the ground-based experiment (bottom) as considered in this work. The red area is the observed area, and the gray area is the masked area. The plots are in Galactic coordinate systems. The sky fractions are 78.8% and 15.1%, respectively.
Paper: Scalar Quadratic Maximum-likelihood Estimators for the CMB Cross-power Spectrum
Authors: Jiming Chen, Shamik Ghosh, Wen Zhao
Figure caption: Normalized covariance matrices ﹩{{ \mathcal C }}_{{\ell },{\ell }^{\prime} }=\mathrm{cov}({\hat{C}}_{{\ell }},{\hat{C}}_{{\ell }^{\prime} })/\sqrt{\mathrm{var}({\hat{C}}_{{\ell }})\mathrm{var}({\hat{C}}_{{\ell }^{\prime} })}﹩ of the two QML methods for the space-based experiment with homogeneous noise. The matrices are obtained from estimates of 1000 simulations for the classic QML estimator (upper panel) and for the QML-SZ estimator (lower panel). The left diagrams show the covariance matrices of the TB mode, and the right diagrams show equivalent plots of EB mode.
Paper: Scalar Quadratic Maximum-likelihood Estimators for the CMB Cross-power Spectrum
Authors: Jiming Chen, Shamik Ghosh, Wen Zhao
Figure caption: Normalized covariance matrices ﹩{{ \mathcal C }}_{{\ell },{\ell }^{\prime} }=\mathrm{cov}({\hat{C}}_{{\ell }},{\hat{C}}_{{\ell }^{\prime} })/\sqrt{\mathrm{var}({\hat{C}}_{{\ell }})\mathrm{var}({\hat{C}}_{{\ell }^{\prime} })}﹩ of the two QML methods for the space-based experiment with inhomogeneous noise. The matrices are obtained from estimates of 1000 simulations for the classic QML estimator (upper panel) and for the QML-SZ estimator (lower panel). The left diagrams show the covariance matrices of the TB mode; the right diagrams show equivalent plots of the EB mode.
Paper: Improved Background Model for the Large Area X-Ray Proportional Counter (LAXPC) Instrument on board AstroSat
Authors: H. M. Antia, P. C. Agrawal, Tilak Katoch, R. K. Manchanda, Kallol Mukerjee, Parag Shah
Figure caption: The maximum and minimum altitude during each orbit, and the maximum latitude during each orbit, is shown in the left panel as a function of days since the launch of AstroSat. The arrows at the top mark the times of AstroSat orbit maneuvers. The right panel shows the same quantities over a limited time to show the variations on a scale of 100 days.
Paper: Improved Background Model for the Large Area X-Ray Proportional Counter (LAXPC) Instrument on board AstroSat
Authors: H. M. Antia, P. C. Agrawal, Tilak Katoch, R. K. Manchanda, Kallol Mukerjee, Parag Shah
Figure caption: The light curves for a few selected background observations in LAXPC20 with a time bin of 32 s are shown as a function of mod (t, 84,495 s). Since the background observations cover a period longer than 84,495 s, there is a time period where multiple values are available.
Paper: Improved Background Model for the Large Area X-Ray Proportional Counter (LAXPC) Instrument on board AstroSat
Authors: H. M. Antia, P. C. Agrawal, Tilak Katoch, R. K. Manchanda, Kallol Mukerjee, Parag Shah
Figure caption: The long-term variation in the background count rate after correcting for gain shift and quasi-diurnal variations in LAXPC10 (left panel) and LAXPC20 (right panel).
Paper: Improved Background Model for the Large Area X-Ray Proportional Counter (LAXPC) Instrument on board AstroSat
Authors: H. M. Antia, P. C. Agrawal, Tilak Katoch, R. K. Manchanda, Kallol Mukerjee, Parag Shah
Figure caption: The least-square fit to background light curves, showing the reduced χ 2 as a function of the quasi-diurnal period in the bottom panel and the resulting profile as a function of phase in the top panel.
Paper: Improved Background Model for the Large Area X-Ray Proportional Counter (LAXPC) Instrument on board AstroSat
Authors: H. M. Antia, P. C. Agrawal, Tilak Katoch, R. K. Manchanda, Kallol Mukerjee, Parag Shah
Figure caption: The residuals in the fit to the background of LAXPC20 for the two background observations obtained using the improved background model. The left panel shows the residuals in the light curve with a time bin of 32 s. The red points mark the time interval that is now removed from the GTI. The right panel shows the residuals in the energy spectrum.
Paper: Improved Background Model for the Large Area X-Ray Proportional Counter (LAXPC) Instrument on board AstroSat
Authors: H. M. Antia, P. C. Agrawal, Tilak Katoch, R. K. Manchanda, Kallol Mukerjee, Parag Shah
Figure caption: The position of the 30 keV peak in the calibration source as a function of time since 2017 March 20, just after the last adjustment of HV. The solid line shows a straight line fit to the data.
Paper: Validations and Corrections of the SFD and Planck Reddening Maps Based on LAMOST and Gaia Data
Authors: Yang Sun, Haibo Yuan, Bingqiu Chen
Figure caption: Reddening coefficients of G BP − G RP color as a function of reddening for different reddening maps. In each panel, the dots and lines are measured and fitted values, respectively. Different colors denote different temperature ranges of stars used. For the SFD, Planck2014-Tau, and Planck2019–Tau maps, only dots of E(B − V) < 0.7 mag are used. For the Planck-R map, only dots of E(B − V) < 0.5 mag are used. Note that for the SFD, Planck2014-Tau, and Planck2019–Tau maps, the higher the stellar temperatures, the lower the reddening values, and the larger the reddening coefficients. While for the Planck-R map, the reddening coefficients are larger for higher reddening values when E(B − V) < 0.45 mag.
Paper: Validations and Corrections of the SFD and Planck Reddening Maps Based on LAMOST and Gaia Data
Authors: Yang Sun, Haibo Yuan, Bingqiu Chen
Figure caption: LAMOST reddening against those from different reddening maps for six selected sight lines of E(B − V) about 0.02 (top two rows), 0.1 (middle two rows), and 0.4 mag (bottom two rows), respectively. Columns from left to right are the SFD, Planck2014-R, Planck2014-Tau, and Planck2019–Tau maps, respectively. For each panel, the blue line represents the final k and the red dashed line denotes line of equality. The spatial position, estimated k value, and standard deviation are also labeled. The first, third, and fifth rows have typical standard deviations, while the second, fourth, and sixth rows show examples of abnormal standard deviations.