adsabs/AstroCLIMB huggingface dataset:
Paper: Metallicity Dependence of Giant Planets around M Dwarfs
Authors: Tianjun Gan, Christopher A. Theissen, Sharon X. Wang, Adam J. Burgasser, Shude Mao
Figure caption: Left panel: the planet mass and scaled semimajor axis distribution of our M dwarf giant-planet sample. Different colors represent planets with scaled semimajor axis belonging to different ranges (see Section 5.2 for details). Right panel: similar to the left but for equilibrium temperature.
Paper: Metallicity Dependence of Giant Planets around M Dwarfs
Authors: Tianjun Gan, Christopher A. Theissen, Sharon X. Wang, Adam J. Burgasser, Shude Mao
Figure caption: Comparisons of [Fe/H] (left) and [M/H] (right) between H-band and K-band measurements. The background gray dots are the field M stars. The red and blue dots are the M dwarfs with hot and warm Jupiters with scaled semimajor axis a/R* ≤ 20 and a/R* > 20, respectively. The systematic errors of 0.09 and 0.08 dex of the H- and K-band calibration have been taken into account. The black dashed line marks the one-to-one function.
Paper: Metallicity Dependence of Giant Planets around M Dwarfs
Authors: Tianjun Gan, Christopher A. Theissen, Sharon X. Wang, Adam J. Burgasser, Shude Mao
Figure caption: Same as Figure 5 but for [M/H]M13,H and [M/H]M13,K. The vertical gray line in each panel marks the median [M/H] of the field M dwarf sample, −0.04 and 0.0 dex. The results are similar to those using [Fe/H].
Paper: Metallicity Dependence of Giant Planets around M Dwarfs
Authors: Tianjun Gan, Christopher A. Theissen, Sharon X. Wang, Adam J. Burgasser, Shude Mao
Figure caption: The iron abundance [Fe/H]M13,K vs. stellar mass of 22 M dwarfs with HJs (red) and WJs (blue) in our planet sample. The background gray dots and black squares represent the field M dwarfs and their binned results (binning size = 0.1M⊙) where the uncertainties are the standard deviations in each stellar mass bin.
Paper: Metallicity Dependence of Giant Planets around M Dwarfs
Authors: Tianjun Gan, Christopher A. Theissen, Sharon X. Wang, Adam J. Burgasser, Shude Mao
Figure caption: Left panel: refined planet mass vs. host metallicity [Fe/H]M13,K. The red and blue dots are giant planets with a/R* ≤ 20 and a/R* > 20. Five multi-giant-planet systems are marked with black boxes. Right panel: similar to the left but for mass ratio.
Paper: Data-space Validation of High-dimensional Models by Comparing Sample Quantiles
Authors: Stephen Thorp, Hiranya V. Peiris, Daniel J. Mortlock, Justin Alsing, Boris Leistedt, Sinan Deger
Figure caption: Two-dimensional illustration of quantile–quantile (Q–Q) and probability–probability (P–P) plots along a principal component axis. The upper row shows a variety of bivariate distributions in coordinate space. Our reference distribution (blue) is a multivariate normal in all three columns. In the first column, this is compared to a shifted (orange) and rotated (green) copy of the same distribution. In the second column the alternatives are a multivariate t-distribution with five dof (orange), and a multivariate Cauchy (green). In the third column, the alternate distributions are both bivariate skew-normals. The middle row shows Q–Q plots comparing the percentiles of the alternate distributions to the percentiles of the normal reference distribution. The comparison is made along the axis defined by the first principal component of the reference distribution (labeled ﹩{\hat{{\boldsymbol{v}}}}_{1}﹩ in the upper panels). The bottom row shows the equivalent P–P plots.
Paper: Data-space Validation of High-dimensional Models by Comparing Sample Quantiles
Authors: Stephen Thorp, Hiranya V. Peiris, Daniel J. Mortlock, Justin Alsing, Boris Leistedt, Sinan Deger
Figure caption: Same as Figure 3, but for a “boomerang”-shaped distribution. We only compare along the first principal axis (﹩{\hat{{\boldsymbol{v}}}}_{1}﹩) here, as the marginals along the second axis are identical by construction.
Paper: Data-space Validation of High-dimensional Models by Comparing Sample Quantiles
Authors: Stephen Thorp, Hiranya V. Peiris, Daniel J. Mortlock, Justin Alsing, Boris Leistedt, Sinan Deger
Figure caption: Schematic of the quantile–quantile and probability–probability plot-based validation scheme laid out in Section 2. The steps highlighted in the boxes of the flowchart correspond to the same steps in the mathematical description in Appendix A.1.
Paper: Data-space Validation of High-dimensional Models by Comparing Sample Quantiles
Authors: Stephen Thorp, Hiranya V. Peiris, Daniel J. Mortlock, Justin Alsing, Boris Leistedt, Sinan Deger
Figure caption: One-dimensional theoretical quantile–quantile (Q–Q) and probability–probability (P–P) plots comparing a normal distribution (blue) to two alternative distributions: a skew-normal (green), and a Student’s t-distribution (orange).
Paper: Data-space Validation of High-dimensional Models by Comparing Sample Quantiles
Authors: Stephen Thorp, Hiranya V. Peiris, Daniel J. Mortlock, Justin Alsing, Boris Leistedt, Sinan Deger
Figure caption: Quantile–quantile (Q–Q; top row) and probability–probability (P–P; second row) plots comparing predictions from the pop-cosmos model (A24) to data from the COSMOS catalog (J. R. Weaver et al. 2022). Comparisons are shown for the first three principal axes of a set of nine broadband colors: u − g, g − r, r − z, z − J, J − K  s , K  s  − irac1, g − z, z − K  s , and z − irac1. The contribution of each color to each principal eigenvector is shown in the bar chart in the bottom row of the figure. In the upper-left corners of the Q–Q plots, we print the fraction of the total variance in the COSMOS data (C) and model colors (m) explained by the projection along that axis. The first principal component is in the left-hand column, which explains around 56% of the total variance in the COSMOS data. The projection of the model colors along the same axis explains 55% of the total variance in the model colors. In the lower-right corner of the ith Q–Q panel, we print the cumulative fractional variance of the COSMOS data explained by principal components 1 through i. Cumulatively, the three components shown explain 93% of the COSMOS data’s total variance. On the Q–Q plots, we show the estimated ﹩{{ \mathcal W }}_{2}﹩ distance between the model and COSMOS, while on the P–P plots we show ﹩{{ \mathcal D }}_{\mathrm{KS}}﹩.
Paper: Data-space Validation of High-dimensional Models by Comparing Sample Quantiles
Authors: Stephen Thorp, Hiranya V. Peiris, Daniel J. Mortlock, Justin Alsing, Boris Leistedt, Sinan Deger
Figure caption: Residuals about the diagonal for P–P (top panel) and Q–Q (bottom panel) plots computed for the GMM fit in Section 3.4. The P–P residuals show the difference in the empirical distribution functions (for −∞ < y < ∞) of N = 106 draws from the fitted model and ground truth. The Q–Q residuals are unbinned, and show the difference between the nth largest draws (for n = 1, …, 106) from the model and truth, plotted at the location of the nth largest draw from the true distribution. Shaded regions in both panels show the 68% and 95% regions estimated from bootstrapping (Section A.3).
Paper: Data-space Validation of High-dimensional Models by Comparing Sample Quantiles
Authors: Stephen Thorp, Hiranya V. Peiris, Daniel J. Mortlock, Justin Alsing, Boris Leistedt, Sinan Deger
Figure caption: Color–color diagrams (z − K  s  and z − irac1 vs. g − z; see Figure 12 of J. R. Weaver et al. 2022). The first, second, and third principal eigenvectors of the COSMOS data are shown as black, gray, and brown arrows, respectively. The light blue and light red points show the colors of 7000 randomly selected galaxies from COSMOS (≈5% of the sample analyzed in A24) and the pop-cosmos model. Contours are computed from the full sample, with the inner contours containing 68% of galaxies and the outer contours containing 95%.
Paper: Data-space Validation of High-dimensional Models by Comparing Sample Quantiles
Authors: Stephen Thorp, Hiranya V. Peiris, Daniel J. Mortlock, Justin Alsing, Boris Leistedt, Sinan Deger
Figure caption: Sample-size dependence of ﹩{{ \mathcal W }}_{2}﹩ (top panel), ﹩{{ \mathcal D }}_{\mathrm{KS}}﹩ (middle panel), and the K-S test p-value ﹩\Pr (d\geqslant {{ \mathcal D }}_{\mathrm{KS}})﹩ (bottom panel) for the example in Section 3.4. The green curves show the metric computed along the first principal component direction of the  x  distribution, with the pink showing the second principal component direction.
Paper: Data-space Validation of High-dimensional Models by Comparing Sample Quantiles
Authors: Stephen Thorp, Hiranya V. Peiris, Daniel J. Mortlock, Justin Alsing, Boris Leistedt, Sinan Deger
Figure caption: Fitted GMM (black ellipses) and ground truth (green) for the example in Section 3.4. The crosses mark the means of the components, with the size of the crosses being proportional to the weights of the different components.
Paper: Data-space Validation of High-dimensional Models by Comparing Sample Quantiles
Authors: Stephen Thorp, Hiranya V. Peiris, Daniel J. Mortlock, Justin Alsing, Boris Leistedt, Sinan Deger
Figure caption: Same as Figure 7, but showing g − r vs. u − g, and r − z vs. g − r.
Paper: Data-space Validation of High-dimensional Models by Comparing Sample Quantiles
Authors: Stephen Thorp, Hiranya V. Peiris, Daniel J. Mortlock, Justin Alsing, Boris Leistedt, Sinan Deger
Figure caption: Scree plot showing the eigenvalues of the COSMOS colors’ covariance matrix on the left axis, and the cumulative variance explained by the corresponding eigenvectors on the right axis.
Paper: Ultraviolet Photometry and Habitable Zones of over 2700 Planet-hosting Stars
Authors: Xue Li, Song Wang, Henggeng Han, Jifeng Liu
Figure caption: Left panel: the relation between the effective temperatures of the host stars and the effective fluxes incident on their planets. The blue, green, orange, and red dots represent gas giants, Neptune-like planets, super-Earths, and terrestrial planets, respectively. The gray shaded area is the CHZ for each star, and the black, red, and purple lines represent the inner and outer boundaries of the CHZ for Earth mass, 5 Earth masses, and 0.1 Earth masses, respectively. Note that the three outer boundary lines overlap. The black dots represent the planets in our solar system. Right panel: the relation between the NUV luminosities of the host stars and the distances between the stars and their planets. The pink, green, and blue shaded areas represent the UHZ for different NUV transmission rates of planetary atmospheres. The blue, green, orange, and red asterisks represent gas giants, Neptune-like planets, super-Earths, and terrestrial planets that are located within both the CHZ and UHZ.
Paper: Ultraviolet Photometry and Habitable Zones of over 2700 Planet-hosting Stars
Authors: Xue Li, Song Wang, Henggeng Han, Jifeng Liu
Figure caption: Left panel: the relationship between the activity index ﹩{R}_{\mathrm{FUV}}^{{\prime} }﹩ and the FUV−NUV color. Gray points represent saturated sources and sources with upper-limit magnitudes, while colored points represent sources with general photometry. The color bar represents the effective temperature of stars. The black asterisk is the Sun. Upward arrows mark the UV emission of two hot subdwarfs exceeding the plotted range. Right panel: the relationship between the activity index ﹩{R}_{\mathrm{NUV}}^{{\prime} }﹩ and the FUV−NUV color.
Paper: Ultraviolet Photometry and Habitable Zones of over 2700 Planet-hosting Stars
Authors: Xue Li, Song Wang, Henggeng Han, Jifeng Liu
Figure caption: Left panel: the effect of stellar NUV luminosity variation on the UHZ. The symbols are the same as in Figure 8(b). the line lengths indicate the amplitude of luminosity variation during multiple GALEX NUV observations. Right panel: the effect of stellar NUV luminosity variation caused by flares on the UHZ.
Paper: Ultraviolet Photometry and Habitable Zones of over 2700 Planet-hosting Stars
Authors: Xue Li, Song Wang, Henggeng Han, Jifeng Liu
Figure caption: Left panel: FUV−NUV color vs. the effective temperature of the sample stars. The blue, green, and orange points represent stars with gas giants, Neptune-like planets, and super-Earths that are located within both the CHZ and UHZ, respectively. The black bar represents the FUV−NUV variation of the Sun over one activity cycle (M. Lovric et al. 2017). The shaded region marks the temperate range from 5500 to 5900 K, representing solar-like stars. Right Panel: ﹩{R}_{\mathrm{FUV}}^{{\prime} }﹩ vs. the effective temperature of the sample stars. The black star is the Sun.
Paper: Ultraviolet Photometry and Habitable Zones of over 2700 Planet-hosting Stars
Authors: Xue Li, Song Wang, Henggeng Han, Jifeng Liu
Figure caption: Left panel: the scaled distances between planets and their host stars in their current state (e.g., main sequence or giant). The gray points represent all planets. The blue, green, orange, and red asterisks represent gas giants, Neptune-like planets, super-Earths, and terrestrial planets that are located within both the CHZ and UHZ. The distances are scaled by dividing by the stellar radii. All stellar radii are normalized to 1 solar radius, represented by the dashed red circle, for better visual effect. Right panel: the scaled distances between planets and their host stars at the end of the red giant phase.
Paper: Ultraviolet Photometry and Habitable Zones of over 2700 Planet-hosting Stars
Authors: Xue Li, Song Wang, Henggeng Han, Jifeng Liu
Figure caption: Top panel: comparison between the upper-limit magnitudes from our aperture photometry with those calculated according to S. Gezari et al. (2013) in the FUV (left) and NUV (right) bands. Blue points represent the background values obtained from the GALEX archive, and red points represent background estimates with the interpolation method. Bottom panel: distribution of exposure times in the FUV (left) and NUV (right) bands.
Paper: Ultraviolet Photometry and Habitable Zones of over 2700 Planet-hosting Stars
Authors: Xue Li, Song Wang, Henggeng Han, Jifeng Liu
Figure caption: Left panel: the surface brightness profile in the NUV band for a saturated source, HD 19994. Right panel: the radial profiles of the FUV (black points) and NUV (red triangles) PSF models. The black and red lines represent the 15th-degree polynomial fittings to the radial profiles of the FUV and NUV PSF models.
Paper: Ultraviolet Photometry and Habitable Zones of over 2700 Planet-hosting Stars
Authors: Xue Li, Song Wang, Henggeng Han, Jifeng Liu
Figure caption: Comparison of atmospheric parameters from different catalogs and those from NASA Exoplanet Science Institute (2023). The panels from left to right display effective temperature, surface gravity, and metallicity, respectively. Above the main plot is the distribution of the parameters from the NASA Exoplanet Science Institute (2023). To the right of the main plot is the distribution of the parameters used in this paper.
Paper: Improved Particle Spray Algorithm for Modeling Globular Cluster Streams
Authors: Yingtian Chen, Monica Valluri, Oleg Y. Gnedin, Neil Ash
Figure caption: Tidal radius rtid vs. core radius ﹩{r}_{{\rm{core}}}﹩ for MW GCs from the M. Hilker et al. (2019) catalog. King models with the dimensionless central potential W varying from 2 to 14 are shown as gray lines.
Paper: Improved Particle Spray Algorithm for Modeling Globular Cluster Streams
Authors: Yingtian Chen, Monica Valluri, Oleg Y. Gnedin, Neil Ash
Figure caption: Initial position and velocity distributions for escaped particles as in Figure 2, but shown in 2D planes of three pairs of parameters. For clarity, we only plot the 1σ ellipses fit to all escaped particles in the r–α space (left), ϕ–θ space (middle), and α–β space (right). We also show the parameters of our particle spray model as thick gray ellipses.
Paper: Improved Particle Spray Algorithm for Modeling Globular Cluster Streams
Authors: Yingtian Chen, Monica Valluri, Oleg Y. Gnedin, Neil Ash
Figure caption: From top to bottom: stream morphology in the ϕ1–ϕ2 space (first row) and ϕ1–R space (second row); and proper motions μϕ1 (third row), μϕ2 (fourth row), and radial velocity VR (fifth row) plotted along ϕ1. We subtract the values of the progenitor cluster's orbits from R, μϕ1, and VR such that the cluster is always represented by zero. We fix (M0, Rapo) = (105 M⊙, 40 kpc) and study in-plane orbits with e = 0 (left column) and e = 0.6 (pericenter in the middle column, and apocenter in the right column). The contours in each panel with different colors and line styles refer to the same simulations/particle spray models as in Figure 6.
Paper: Improved Particle Spray Algorithm for Modeling Globular Cluster Streams
Authors: Yingtian Chen, Monica Valluri, Oleg Y. Gnedin, Neil Ash
Figure caption: Distributions of stream particles in the action space relative to the progenitor cluster from circular (left column) and eccentric (right column) orbits with in-plane (top row) and polar (bottom row) inclinations. We fix (M0, Rapo) = (105 M⊙, 40 kpc). For each panel, we show the distribution from N-body simulations as background contours, highlighting the contour enclosing 50% of total particles as the thin magenta curve. We plot the 50% contours for particle spray models as thick curves.
Paper: Improved Particle Spray Algorithm for Modeling Globular Cluster Streams
Authors: Yingtian Chen, Monica Valluri, Oleg Y. Gnedin, Neil Ash
Figure caption: Initial position and velocity distributions for escaped particles as in Figure 3, but with fixed Rapo = 40 kpc and varying M0 and e (top row). We also show the distributions for polar and in-plane orbits in the bottom row.
Paper: Improved Particle Spray Algorithm for Modeling Globular Cluster Streams
Authors: Yingtian Chen, Monica Valluri, Oleg Y. Gnedin, Neil Ash
Figure caption: Initial position and velocity distributions for escaped particles as in Figure 3, but varying the W parameter for the King model (first row), time step τ (second row), softening length ε (third row), and particle mass m (fourth row). The physical parameters are (M0, Rapo, e) = (105 M⊙, 40 kpc, 0.6), with the in-plane orbital inclination.
Paper: Improved Particle Spray Algorithm for Modeling Globular Cluster Streams
Authors: Yingtian Chen, Monica Valluri, Oleg Y. Gnedin, Neil Ash
Figure caption: Initial position and velocity distributions for escaped particles from N-body simulations with fixed M0 = 105 M⊙ and varying Rapo and e. We show our particle spray model distributions as thick gray curves. The probability distribution functions (PDFs) are obtained from the Gaussian KDE method, with bandwidths set to 1/5 of our model standard deviations. We normalize the PDFs such that the peak values of our model distributions are unity.
Paper: Improved Particle Spray Algorithm for Modeling Globular Cluster Streams
Authors: Yingtian Chen, Monica Valluri, Oleg Y. Gnedin, Neil Ash
Figure caption: Stream morphology and velocities as in Figure 7, but evolved for another 500 Myr with the progenitor cluster removed.
Paper: Improved Particle Spray Algorithm for Modeling Globular Cluster Streams
Authors: Yingtian Chen, Monica Valluri, Oleg Y. Gnedin, Neil Ash
Figure caption: Stream morphology and velocities as in Figure 7, but for polar orbits.
Paper: Application of Machine Learning to Background Rejection in Very-high-energy Gamma-Ray Observation
Authors: Jie Li, Hongkui Lv, Yang Liu, Jiajun Huang, Yu Wang, Wenbin Lin
Figure caption: The characteristic contribution plots for the four data sets. Top left: characteristics of the top 10 contributors in 1012–1013 eV. Top right: characteristics of the top 10 contributors in 1013–1014 eV. Bottom left: characteristics of the top 10 contributors in 1014–1015 eV. Bottom right: characteristics of the top 10 contributors in 1015–1016 eV.
Paper: Application of Machine Learning to Background Rejection in Very-high-energy Gamma-Ray Observation
Authors: Jie Li, Hongkui Lv, Yang Liu, Jiajun Huang, Yu Wang, Wenbin Lin
Figure caption: The deep neural network structure used for the training. It has eight neurons in the input layer, and each neuron corresponds to one input. The hidden layer has 128 neurons in the first layer, 64 neurons in the second, and 32 neurons in the third. The output layer has two neurons, which are the probabilities of belonging to the 0/1 (proton/gamma) category; we can then determine which category the particle belongs to based on the size of the probability.
Paper: Application of Machine Learning to Background Rejection in Very-high-energy Gamma-Ray Observation
Authors: Jie Li, Hongkui Lv, Yang Liu, Jiajun Huang, Yu Wang, Wenbin Lin
Figure caption: Characteristic boxplots for the different energy segments. The figure shows the distribution of data of NfiltM, Base, NuM2, NuM3–NuM2, NuM4–NuM1, NuM1–NuM3, NhitM, and rec_Eage in different categories over the four energy segments. The lower and upper whiskers represent the minimum and maximum, the bottom and top edges of the box represent the first quartile (Q1) and the third quartile (Q3), and the lines inside the boxes represent the median. The x-axis is four energy segments, and the y-axis is the value of a feature.
Paper: Application of Machine Learning to Background Rejection in Very-high-energy Gamma-Ray Observation
Authors: Jie Li, Hongkui Lv, Yang Liu, Jiajun Huang, Yu Wang, Wenbin Lin
Figure caption: ROC plots of the models for the four data sets. The small boxes show a larger picture of the dashed box at the point of inflection. Top left: ROC plot for 1012–1013 eV. Top right: ROC plot for 1013–1014 eV. Lower left: ROC plot of 1014–1015 eV. Lower right: ROC plot for 1015–1016 eV.
Paper: Application of Machine Learning to Background Rejection in Very-high-energy Gamma-Ray Observation
Authors: Jie Li, Hongkui Lv, Yang Liu, Jiajun Huang, Yu Wang, Wenbin Lin
Figure caption: Average performance of the models on the four test data sets. Top left: average performance of accuracy. Top right: average performance of F1 score. Bottom left: average performance of recall. Bottom right: average performance of precision.
Paper: Application of Machine Learning to Background Rejection in Very-high-energy Gamma-Ray Observation
Authors: Jie Li, Hongkui Lv, Yang Liu, Jiajun Huang, Yu Wang, Wenbin Lin
Figure caption: The t-SNE plots for the four data sets. Top left: t-SNE plot of the first two components of 1012–1013 eV. Top right: t-SNE plot of the first two components of 1013–1014 eV. Bottom left: t-SNE plot of the first two components of 1014–1015 eV. Bottom right: t-SNE plot of the first two components of 1015–1016 eV.
Paper: Application of Machine Learning to Background Rejection in Very-high-energy Gamma-Ray Observation
Authors: Jie Li, Hongkui Lv, Yang Liu, Jiajun Huang, Yu Wang, Wenbin Lin
Figure caption: Predicted probability distribution of the STACK classifier on the four test sets. The x-axis denotes the probability of a particle being gamma in the range [0, 1]. The y-axis denotes the number of particles observed in each probability bin as a proportion of the total number of particles.
Paper: Ultraviolet Photometry and Habitable Zones of over 2700 Planet-hosting Stars
Authors: Xue Li, Song Wang, Henggeng Han, Jifeng Liu
Figure caption: (a) Comparison between background counts obtained from the interpolation method and those derived from GALEX background images in the FUV band. The aperture size for background estimation is 6 pixels, and the unit is counts per second. Colors represent number density. (b) Comparison between background counts obtained from the interpolation method and those derived from GALEX background images in the NUV band. (c) Distribution of exposure times for images with different background values using the interpolation method in the FUV band. (d) Distribution of exposure times for images with different background values using the interpolation method in the NUV band.