adsabs/AstroCLIMB huggingface dataset:
Paper: ACCESS, LRG-BEASTS, and MOPSS: Featureless Optical Transmission Spectra of WASP-25b and WASP-124b
Authors: Chima D. McGruder, Mercedes López-Morales, James Kirk, Benjamin V. Rackham, Erin May, Eva-Maria Ahrer, George W. King, Munazza K. Alam, Natalie H. Allen, Kevin Ortiz Ceballos, Néstor Espinoza, Tyler Gardner, Andrés Jordán, Kelly Meyer, John D. Monnier, David J. Osip, Peter J. Wheatley
Figure caption: Same as Figure 6 but for transit UT180620.
Paper: Three-dimensional MHD Simulations of the Magnetic Pileup at Mars
Authors: M. Wang, Z. J. Guan, L. Xie, J. Y. Lu, X. Xu, Y. Wei, Z. Zhou, L. Chai, J. Wang, Q. Chang, H. X. Zhang, B. H. Qu, H. Y. Sui, J. Q. Zhang, F. H. Qiao, L. Li
Figure caption: The solar wind dynamic pressure effect on the magnetic pileup condition at Mars represented by the difference in magnetic field intensity, dB  t , on the X–Y (upper panels) and X–Z (bottom panels) planes in the MSO coordinate system for the Parker-spiral IMF condition. Left panels (a) and (d) show dB  t  under the same P  d  (1 nPa) between n = 1 cm−3, V = −776 km s−1 and n = 5 cm−3, V = −347 km s−1, middle panels (b) and (e) display dB  t  between P  d  = 5 nPa (n = 5 cm−3, V = −776 km s−1) and P  d  = 1 nPa (n = 5 cm−3, V = −347 km s−1), and right panels (c) and (f) are dB  t  between P  d  = 5 nPa (n = 5 cm−3, V = −776 km s−1) and P  d  = 1 nPa (n = 1 cm−3, V = −776 km s−1). The black dashed lines represent the locations of the magnetic pileup boundary and the bow shock for Vignes et al. (2000) models.
Paper: Three-dimensional MHD Simulations of the Magnetic Pileup at Mars
Authors: M. Wang, Z. J. Guan, L. Xie, J. Y. Lu, X. Xu, Y. Wei, Z. Zhou, L. Chai, J. Wang, Q. Chang, H. X. Zhang, B. H. Qu, H. Y. Sui, J. Q. Zhang, F. H. Qiao, L. Li
Figure caption: Relation between the magnetic field lines and the magnetic pileup condition on the southern hemisphere of X–Z plane in the MSO coordinates for P  d  = 1.06 nPa (n = 4.0 cm−3, V  X  = −400.0 km s−1). Panel (a) displays dB  t  under the steady state between the IMF conditions of zero intensity (IMF = 0) and B  Z  = 3 nT (B  X  = B  Y  = 0  nT) (same as panel (g) of Figure 2), and panel (b) shows the corresponding condition under B  Z  = −3 nT (B  X  = B  Y  = 0  nT) (same as panel (h) of Figure 2). The solid black lines are the magnetic field lines, and the white dashed lines represent the locations of the magnetic pileup boundary and the bow shock for Vignes et al. (2000) models.
Paper: Three-dimensional MHD Simulations of the Magnetic Pileup at Mars
Authors: M. Wang, Z. J. Guan, L. Xie, J. Y. Lu, X. Xu, Y. Wei, Z. Zhou, L. Chai, J. Wang, Q. Chang, H. X. Zhang, B. H. Qu, H. Y. Sui, J. Q. Zhang, F. H. Qiao, L. Li
Figure caption: Topology of the difference in magnetic field intensity, the total current density, and the X component of the magnetic field on the Y–Z plane in the MSO coordinates at X = −1.3 R  M  under the solar wind condition of P  d  = 5 nPa (n = 5 cm−3, V = −776 km s−1) and the Parker-spiral IMF. Panel (a) displays the difference in magnetic field intensity, dB  t , between P  d  = 5 nPa (n = 5 cm−3, V = −776 km s−1) and P  d  = 1 nPa (n = 5 cm−3, V = −347 km s−1) (same as panels (b) and (e) of Figure 3). Panel (b) shows the total current density, J  t , and the black lines represent the streamlines of the Y and Z components of the current density, J  Y  and J  Z . Panel (c) displays the X component of the magnetic field, B  X .
Paper: On the Origins of Extreme Velocity Stars as Revealed by Large-scale Galactic Surveys
Authors: Qing-Zheng Li, Yang Huang, Xiao-Bo Dong, Hua-Wei Zhang, Timothy C. Beers, Zhen Yuan
Figure caption: Left-hand panel: Similar to Figure 10, but for all extreme velocity stars (blue stars) with V GSR ≥ 0.8V esc, HVS candidates (red stars), and field stars of the MW (gray pluses). The contours show the number density for the halo HiVels with V GSR < 0.8V esc. Right panel: The normalized distributions of [α/Fe] for field halo stars (gray), HVS candidates (red) and extreme velocity stars (blue) with [Fe/H] between −3.3 and −1.0.
Paper: On the Origins of Extreme Velocity Stars as Revealed by Large-scale Galactic Surveys
Authors: Qing-Zheng Li, Yang Huang, Xiao-Bo Dong, Hua-Wei Zhang, Timothy C. Beers, Zhen Yuan
Figure caption: A 3D representation of the backward-integrated orbits for the 15 extreme velocity stars and the Sgr dSph. The present positions of the stars, as well as the Sgr dSph, are marked as black dots. The green dot represents the latest pericenter of the Sgr dSph. The directions of the backward-integrated orbits are denoted by the arrows. The positions of the Sun and the Galactic Center are represented by blue and red dots, respectively. The Solar circle (R = 8.34 kpc) and the edge of the MW disk (R = 25 kpc) are denoted by the inner and outer dotted lines, respectively. The two red lines denote the backward-integrated orbits of two HVS candidates, SG-HVS43 and SG-HVS47; the blue line indicates the backward-integrated orbit of SG-HiVel6195 (also called J1443+1453), discovered by Huang et al. (2021), which was the first HVS candidate originating from the Sgr dSph.
Paper: On the Origins of Extreme Velocity Stars as Revealed by Large-scale Galactic Surveys
Authors: Qing-Zheng Li, Yang Huang, Xiao-Bo Dong, Hua-Wei Zhang, Timothy C. Beers, Zhen Yuan
Figure caption: Left-hand panel: The distribution of backward time for the 15 extreme velocity stars that have close encounters with the Sgr dSph within its 2r  h . The red-solid line indicates the backward time of the Sgr dSph from the present position to its latest pericenter (39.7 Myr), and the red-dashed lines mark its 1σ uncertainty (calculated using the Monte Carlo simulation similar to that described in Section 5.2). Middle panel: The distribution of the closest distances between the 15 stars and the Sgr dSph. The red-solid line denotes two times r  h  of the Sgr dSph. Right-hand panel: The distribution of the ejection velocities of the 15 stars, assuming their Sgr dSph origin.
Paper: On the Origins of Extreme Velocity Stars as Revealed by Large-scale Galactic Surveys
Authors: Qing-Zheng Li, Yang Huang, Xiao-Bo Dong, Hua-Wei Zhang, Timothy C. Beers, Zhen Yuan
Figure caption: Distributions of the extreme velocity star (blue stars) and HVS candidate (red stars) originating from the Sgr dSph galaxy, Sgr stream member stars (green triangles), Sgr dSph-associated globular clusters (magenta squares), and field stars of the MW (gray-plus symbols) in the [α/Fe]–[Fe/H] plane. The four Sgr dSph-associated globular clusters are M 54, Terzan 7, Terzan 8, and Arp 2, respectively. Their elemental-abundance ratios are measured from high-resolution spectroscopy (Sbordone et al. 2005; Carretta et al. 2010, 2014; Mottini et al. 2008).
Paper: On the Origins of Extreme Velocity Stars as Revealed by Large-scale Galactic Surveys
Authors: Qing-Zheng Li, Yang Huang, Xiao-Bo Dong, Hua-Wei Zhang, Timothy C. Beers, Zhen Yuan
Figure caption: Left-hand panel: A 3D representation of the backward-integrated orbits of eight HiVel stars passing within 1 kpc near the GC. The positions of the Sun and the GC are represented by the blue and red dots, respectively. The Solar circle (R = 8.34 kpc) is represented by the dashed circle. Right-hand panel: 90% confidence region of eight HiVel stars in the Galactic plane (X–Y).
Paper: On the Origins of Extreme Velocity Stars as Revealed by Large-scale Galactic Surveys
Authors: Qing-Zheng Li, Yang Huang, Xiao-Bo Dong, Hua-Wei Zhang, Timothy C. Beers, Zhen Yuan
Figure caption: Distribution of the 6434 halo HiVel stars (gray dots) with V GSR < 0.8V esc in the E total–L Z diagram. We identify and denote some HiVels that belong to certain known Galactic subsystems (see text).
Paper: Imaging the Milky Way with Millihertz Gravitational Waves
Authors: Kaitlyn Szekerczes, Scott Noble, Cecilia Chirenti, James Ira Thorpe
Figure caption: Number density of gravitationally-radiating UCBs in the LISA band in a simulated Milky Way population. The instrumental (dashed) and combined instrumental and confusion limits (solid) for a year of observation with the LISA and AMIGO missions are shown for comparison.
Paper: Imaging the Milky Way with Millihertz Gravitational Waves
Authors: Kaitlyn Szekerczes, Scott Noble, Cecilia Chirenti, James Ira Thorpe
Figure caption: Estimated angular resolution from the coherent diffraction limit for simulated detections with LISA (left, 17,111 systems) and AMIGO (right, 23,245 systems).
Paper: Directly Deriving Parameters from SDSS Photometric Images
Authors: Fan Wu, Yude Bu, Mengmeng Zhang, Zhenping Yi, Meng Liu, Xiaoming Kong
Figure caption: Uncertainty in rotating. We rotate the images by 30° in each step for a full circle to examine the effect of rotation on the model estimation and uncertainty. In the figure, the green (left) and red (right) curves represent the uncertainties and predicted values, respectively, as the outputs of the model, while blue points represent the labels. The red shaded area denotes the 95% confidence interval of the predictions; that is, the range from the 2.5% percentile to the 97.5% percentile based on multiple sampling results. The figure shows that the model results are affected by the rotation of the images. However, when the predicted values deviate significantly from the labels, our model tends to give a wider confidence interval with a higher uncertainty, and the transformation trend of the entire uncertainty curve is almost consistent with the deviation trend of the prediction, i.e., our model can account for this interference in a reasonable way.
Paper: Directly Deriving Parameters from SDSS Photometric Images
Authors: Fan Wu, Yude Bu, Mengmeng Zhang, Zhenping Yi, Meng Liu, Xiaoming Kong
Figure caption: Learning curves. This figure shows the performance of each model component during the training process, as reflected in the training set and validation set. (a)–(c) depict the change in loss during the training of the backbones of each model, where the loss function employed is the smooth L1 loss. (d)–(g) detail the loss alteration during the training of different model head parts, where the loss function, as indicated in Equation (19), is used. (h) shows the changing trend of the accuracy (ACC) regarding the head of the ﹩\mathrm{log}g﹩ classification during the training process. All training processes adopted data augmentation techniques (random rotation and flipping of images) and early stopping techniques to mitigate overfitting.
Paper: Directly Deriving Parameters from SDSS Photometric Images
Authors: Fan Wu, Yude Bu, Mengmeng Zhang, Zhenping Yi, Meng Liu, Xiaoming Kong
Figure caption: Model uncertainty. (a) Effective temperature T eff, (b) surface gravity ﹩\mathrm{log}g﹩, and (c) metallicity [Fe/H]. The uncertainty increases when the model predicts values outside the training data range. This trend is also observed when we estimate ﹩\mathrm{log}g﹩ separately for different categories of stars.
Paper: Directly Deriving Parameters from SDSS Photometric Images
Authors: Fan Wu, Yude Bu, Mengmeng Zhang, Zhenping Yi, Meng Liu, Xiaoming Kong
Figure caption: Add noise. Gaussian noise is added to the gri band, where epsilon indicates the relative magnitude of the noise.
Paper: Directly Deriving Parameters from SDSS Photometric Images
Authors: Fan Wu, Yude Bu, Mengmeng Zhang, Zhenping Yi, Meng Liu, Xiaoming Kong
Figure caption: Attention distribution of ConViT over different regions. The warmer the color, the more attention the model pays. The model focuses more on the star’s surroundings when estimating T eff and ﹩\mathrm{log}g﹩, but more on the star’s core when estimating [Fe/H].
Paper: PCAT-DE: Reconstructing Pointlike and Diffuse Signals in Astronomical Images Using Spatial and Spectral Information
Authors: Richard M. Feder, Victoria Butler, Tansu Daylan, Stephen K. N. Portillo, Jack Sayers, Benjamin J. Vaughan, Catalina V. Zamora, Michael Zemcov
Figure caption: Comparison of recovered catalog ensembles for a fixed 250 μm SPIRE CIB observation in the cirrus-free and 1×-Planck cases, with and without the use of Fourier components. In each panel, the red symbols indicate the true positions of the input catalog down to S 250 = 3 mJy, while the blue symbols show 200 stacked samples that are uniformly distributed across the last 1000 posterior samples. “Fuzzier” regions reflect the posterior uncertainty recovered in sources, typically on the faint end. The left panel shows PCAT run on CIB and instrument noise realizations, with no Fourier component model. The middle and right panels show results based on the same CIB realization, but with additional synthetic cirrus dust drawn at the 1×-Planck cirrus level. While PCAT infers several spurious model sources in the absence of a diffuse signal model (middle), the inclusion of the Fourier component model leads to a recovered catalog ensemble nearly identical to the cirrus-free case (right).
Paper: PCAT-DE: Reconstructing Pointlike and Diffuse Signals in Astronomical Images Using Spatial and Spectral Information
Authors: Richard M. Feder, Victoria Butler, Tansu Daylan, Stephen K. N. Portillo, Jack Sayers, Benjamin J. Vaughan, Catalina V. Zamora, Michael Zemcov
Figure caption: Expected improvement in log-likelihood from adding a point source to the model relative to the sparse limit, ﹩\langle {\rm{\Delta }}\mathrm{log}{ \mathcal L }{\rangle }_{n\to 0}﹩, as a function of source density. Three parameters are added to the model per source in the single-band case, while each additional band included in a joint fit increases the number of parameters per source by one. The solid and dashed curves are for results with and without Fourier components, respectively.
Paper: PCAT-DE: Reconstructing Pointlike and Diffuse Signals in Astronomical Images Using Spatial and Spectral Information
Authors: Richard M. Feder, Victoria Butler, Tansu Daylan, Stephen K. N. Portillo, Jack Sayers, Benjamin J. Vaughan, Catalina V. Zamora, Michael Zemcov
Figure caption: Input and recovered flux distributions for 20 mock CIB realizations with increasing cirrus contamination (∼0.3 deg2 in total area). The recovered flux distributions are shifted slightly along the x-axis to highlight the dependence on diffuse contamination for each bin. The error bars indicate the scatter across the ﹩10^{\prime} \times 10^{\prime} ﹩ CIB realizations. The same set of catalogs is used as input for each cirrus level in order to reduce additional uncertainties due to sample variance.
Paper: PCAT-DE: Reconstructing Pointlike and Diffuse Signals in Astronomical Images Using Spatial and Spectral Information
Authors: Richard M. Feder, Victoria Butler, Tansu Daylan, Stephen K. N. Portillo, Jack Sayers, Benjamin J. Vaughan, Catalina V. Zamora, Michael Zemcov
Figure caption: Comparison of input and recovered CIB (solid) and cirrus (dashed) power spectra for four cirrus realizations of increasing power. The recovered cirrus is represented by the Fourier component model image, while the CIB comes from the point-source model. The per-pixel instrument noise is 10−3 Jy beam−1, corresponding to a power spectrum amplitude of 10−8 Jy2 beam−2 arcmin2.
Paper: PCAT-DE: Reconstructing Pointlike and Diffuse Signals in Astronomical Images Using Spatial and Spectral Information
Authors: Richard M. Feder, Victoria Butler, Tansu Daylan, Stephen K. N. Portillo, Jack Sayers, Benjamin J. Vaughan, Catalina V. Zamora, Michael Zemcov
Figure caption:                          ﹩10^{\prime} \times 10^{\prime} ﹩ cirrus-free mock SPIRE observations toward galaxy cluster RX J1347.5-1145. The columns show (going from left to right) the input SZ effect signal, observed maps, median CIB model, and difference between the observed maps and CIB model. By fitting both SZ and CIB simultaneously, we can account for the presence of submillimeter point sources without overfitting the underlying SZ signal. The maps in the rightmost column are shown for visualization purposes only.
Paper: PCAT-DE: Reconstructing Pointlike and Diffuse Signals in Astronomical Images Using Spatial and Spectral Information
Authors: Richard M. Feder, Victoria Butler, Tansu Daylan, Stephen K. N. Portillo, Jack Sayers, Benjamin J. Vaughan, Catalina V. Zamora, Michael Zemcov
Figure caption: Recovered posteriors on ﹩{A}_{b}^{\mathrm{SZ}}﹩ surface brightness parameters for clusters of varying redshift/angular extent. The cluster realizations corresponding to these results are cirrus-free, and the input SZ surface values for each cluster (marked by the red stars) are determined from a combined analysis of Chandra and Bolocam data. 1σ and 2σ density contours are plotted for each cluster.
Paper: PCAT-DE: Reconstructing Pointlike and Diffuse Signals in Astronomical Images Using Spatial and Spectral Information
Authors: Richard M. Feder, Victoria Butler, Tansu Daylan, Stephen K. N. Portillo, Jack Sayers, Benjamin J. Vaughan, Catalina V. Zamora, Michael Zemcov
Figure caption: Completeness (top) and false discovery rate (bottom) of PCAT-detected sources for mock CIB data with σ inst = 1 mJy beam−1 (solid) and σ inst = 6 mJy beam−1 (dashed). Each line shows the mean and scatter from 20 mock CIB realizations.
Paper: PCAT-DE: Reconstructing Pointlike and Diffuse Signals in Astronomical Images Using Spatial and Spectral Information
Authors: Richard M. Feder, Victoria Butler, Tansu Daylan, Stephen K. N. Portillo, Jack Sayers, Benjamin J. Vaughan, Catalina V. Zamora, Michael Zemcov
Figure caption: Completeness (triangles) and false discovery rates (stars) as a function of flux density. These are evaluated for a range of Fourier component models indicated by the different colors. The top panel shows the 1×-Planck results, while the bottom shows the same for the 8×-Planck case.
Paper: The Mass–Temperature Relation for B and Early A Stars Based on International Ultraviolet Explorer Spectra of Detached Eclipsing Binaries
Authors: Nancy Remage Evans, Mckenzie G. Ferrari, Joanna Kuraszkiewicz, Steven Silverberg, Joy Nichols, Guillermo Torres, Makenzi Fischbach
Figure caption: The log-temperature–log-luminosity relation for main-sequence stars showing no evolution or chemical peculiarities (see text). Luminosity is in solar units; the temperature is determined from the IUE spectral fits.
Paper: The Mass–Temperature Relation for B and Early A Stars Based on International Ultraviolet Explorer Spectra of Detached Eclipsing Binaries
Authors: Nancy Remage Evans, Mckenzie G. Ferrari, Joanna Kuraszkiewicz, Steven Silverberg, Joy Nichols, Guillermo Torres, Makenzi Fischbach
Figure caption: The mass–temperature relation. Main-sequence stars: black points; dashed line: fit to points; solid line: Harmanec relation.
Paper: An Optical Distortion Solution for the Keck I OSIRIS Imager
Authors: Matthew S. R. Freeman, Jessica R. Lu, Jim Lyke, Abhimat Gautam, Renate Kupke, Andrea Ghez, Shoko Sakai, Jay Anderson, Andrea Bellini
Figure caption: The difference in the x and y locations of stars between NIRC2 and OSIRIS Galactic center observations (Section 4.1), repeated with the OSIRIS distortion correction applied (blue) and without the distortion correction (orange). The top two panels show the differences dx and dy in milliarcseconds. The bottom two panels show the differences scaled by their errors σ  x  and σ  y . A 1σ Gaussian is plotted for comparison.
Paper: An Optical Distortion Solution for the Keck I OSIRIS Imager
Authors: Matthew S. R. Freeman, Jessica R. Lu, Jim Lyke, Abhimat Gautam, Renate Kupke, Andrea Ghez, Shoko Sakai, Jay Anderson, Andrea Bellini
Figure caption: Left: the Zemax optical model of the OSIRIS distortion based on the nominal optical design specifications. The Zemax model was converted to pixel coordinates using the OSIRIS pixel size of 18 μm. Center and right: the difference between the Zemax model and the 5th-order Legendre polynomial solutions we calculate, for 2020 and 2021 (from Figure 7). The colors correspond to the angle of the vector.
Paper: An Optical Distortion Solution for the Keck I OSIRIS Imager
Authors: Matthew S. R. Freeman, Jessica R. Lu, Jim Lyke, Abhimat Gautam, Renate Kupke, Andrea Ghez, Shoko Sakai, Jay Anderson, Andrea Bellini
Figure caption: Legendre polynomial uncertainties calculated using bootstrapping. The color shows the standard deviation of 5th-order Legendre polynomials fit to 100 samplings of the data, for the 2020 (top) and 2021 (bottom) solutions in x (left) and y (right).
Paper: An Optical Distortion Solution for the Keck I OSIRIS Imager
Authors: Matthew S. R. Freeman, Jessica R. Lu, Jim Lyke, Abhimat Gautam, Renate Kupke, Andrea Ghez, Shoko Sakai, Jay Anderson, Andrea Bellini
Figure caption: Calculations of the OSIRIS plate scale and the correction to the position angle, based on the four-parameter transformations to the Hubble reference frame.
Paper: An Optical Distortion Solution for the Keck I OSIRIS Imager
Authors: Matthew S. R. Freeman, Jessica R. Lu, Jim Lyke, Abhimat Gautam, Renate Kupke, Andrea Ghez, Shoko Sakai, Jay Anderson, Andrea Bellini
Figure caption: The measurements of the PA correction from Figure 14 as a function of telescope elevation. The data from 2020 August 4 show a correlation with elevation.
Paper: An Optical Distortion Solution for the Keck I OSIRIS Imager
Authors: Matthew S. R. Freeman, Jessica R. Lu, Jim Lyke, Abhimat Gautam, Renate Kupke, Andrea Ghez, Shoko Sakai, Jay Anderson, Andrea Bellini
Figure caption: The results of an F-test (Equation 1) comparing each polynomial model order to the previous polynomial model order, i.e., the point labeled Order 5 is the result of an F-test comparing the 5th-order polynomial model to the 4th-order polynomial model. We find the 5th-order model to have a higher F value, indicating it is a better fit to the data.
Paper: An Optical Distortion Solution for the Keck I OSIRIS Imager
Authors: Matthew S. R. Freeman, Jessica R. Lu, Jim Lyke, Abhimat Gautam, Renate Kupke, Andrea Ghez, Shoko Sakai, Jay Anderson, Andrea Bellini
Figure caption: Residuals between the OSIRIS distortion-corrected observations of M15 and M92, and the reference-frame positions from Section 3.7. The colors correspond to the angle of the vector.
Paper: An Optical Distortion Solution for the Keck I OSIRIS Imager
Authors: Matthew S. R. Freeman, Jessica R. Lu, Jim Lyke, Abhimat Gautam, Renate Kupke, Andrea Ghez, Shoko Sakai, Jay Anderson, Andrea Bellini
Figure caption: Weighted mean of the distortion residuals in Figure 11. Binned into 64 × 64 pixel bins.
Paper: Bayesian Analysis for Remote Biosignature Identification on exoEarths (BARBIE). I. Using Grid-based Nested Sampling in Coronagraphy Observation Simulations for H2O
Authors: Natasha Latouf, Avi M. Mandell, Geronimo L. Villanueva, Michael Dane Moore, Nicholas Susemiehl, Vincent Kofman, Michael D. Himes
Figure caption: Results of the analysis for the fiducial abundance (3 × 10−3) for H2O, as an example case. Each dot represents a bandpass center, with the pink line portraying the median retrieved values and the gray shaded region representing the upper and lower limits for the 68% credible region; the true value is shown with the black dashed line. Increased statistical certainty can be seen where the gray regions narrow. Each point is colored to indicate varying detection strength. Unconstrained regions (﹩\mathrm{ln}B\lt 2.5﹩) are shown in light pink dots, weak detections (﹩2.5\leqslant \mathrm{ln}B\lt 5﹩) are shown in dark pink diamonds, and strong detections (﹩\mathrm{ln}B\gt 5﹩) are shown in purple squares.
Paper: Bayesian Analysis for Remote Biosignature Identification on exoEarths (BARBIE). I. Using Grid-based Nested Sampling in Coronagraphy Observation Simulations for H2O
Authors: Natasha Latouf, Avi M. Mandell, Geronimo L. Villanueva, Michael Dane Moore, Nicholas Susemiehl, Vincent Kofman, Michael D. Himes
Figure caption: Three corner plots, one for each notable H2O feature, with one corner plot at very high S/N to portray the intrinsic parameter degeneracies. Each has the same parameters and an S/N of 12, as shown in Table 1, with the exception of locking Cf and having an S/N of 200 for corner plot (d). In all corner plots, the 68% credible regions are shown as pink shading in the 1D marginalized posterior distributions along the diagonal of the corner plot, and the true values are represented by black lines in the diagonals of the corner plot, and black stars within the 2D plots.