adsabs/AstroCLIMB huggingface dataset:
Paper: Mass Production of 2021 KMTNet Microlensing Planets. III. Analysis of Three Giant Planets
Authors: In-Gu Shin, Jennifer C. Yee, Andrew Gould, Kyu-Ha Hwang, Hongjing Yang, Ian A. Bond, Michael D. Albrow, Sun-Ju Chung, Cheongho Han, Youn Kil Jung, Yoon-Hyun Ryu, Yossi Shvartzvald, Weicheng Zang, Sang-Mok Cha, Dong-Jin Kim, Seung-Lee Kim, Chung-Uk Lee, Dong-Joo Lee, Yongseok Lee, Byeong-Gon Park, Richard W. Pogge, Fumio Abe, Richard Barry, David P. Bennett, Aparna Bhattacharya, Hirosane Fujii, Akihiko Fukui, Yuki Hirao, Stela Ishitani Silva, Yoshitaka Itow, Rintaro Kirikawa, Iona Kondo, Naoki Koshimoto, Yutaka Matsubara, Sho Matsumoto, Shota Miyazaki, Yasushi Muraki, Arisa Okamura, Greg Olmschenk, Clément Ranc, Nicholas J. Rattenbury, Yuki Satoh, Takahiro Sumi, Daisuke Suzuki, Taiga Toda, Paul . J. Tristram, Aikaterini Vandorou, Hibiki Yama
Figure caption: Distributions of host mass and system distance for confirmed exoplanets with low-mass host stars. The point shape corresponds to the detection method. The color of each point is set by the (log) mass of the planet (black: ﹩\mathrm{log}({m}_{p}/{M}_{J})\lt -2﹩, red: ﹩-2\lt \mathrm{log}({m}_{p}/{M}_{J})\lt -1.5﹩, yellow: ﹩-1.5\lt \mathrm{log}({m}_{p}/{M}_{J})\lt -1﹩, green: ﹩-1\lt \mathrm{log}({m}_{p}/{M}_{J})\lt -0.5﹩, cyan: ﹩-0.5\lt \mathrm{log}({m}_{p}/{M}_{J})\lt 0﹩, blue: ﹩0\lt \mathrm{log}({m}_{p}/{M}_{J})\lt 0.5﹩, magenta: ﹩0.5\lt \mathrm{log}({m}_{p}/{M}_{J})﹩). For radial velocity planets, ﹩{m}_{p}\sin i﹩ is plotted if m  p  is not available, and for microlensing planets a ⊥ (the projection of the semimajor axis on the sky) is plotted in place of a. Reference lines are drawn at a = 0.1  au and M host = 0.3 M ⊙. The location of KMT-2021-BLG-2294 in this plane is indicated by the arrow. The upper left panel shows all planets together, while the other panels show subsets by planet mass, as indicated in the titles. Data from the NASA Exoplanet Archive, accessed 2022 July 20.
Paper: Mass Production of 2021 KMTNet Microlensing Planets. III. Analysis of Three Giant Planets
Authors: In-Gu Shin, Jennifer C. Yee, Andrew Gould, Kyu-Ha Hwang, Hongjing Yang, Ian A. Bond, Michael D. Albrow, Sun-Ju Chung, Cheongho Han, Youn Kil Jung, Yoon-Hyun Ryu, Yossi Shvartzvald, Weicheng Zang, Sang-Mok Cha, Dong-Jin Kim, Seung-Lee Kim, Chung-Uk Lee, Dong-Joo Lee, Yongseok Lee, Byeong-Gon Park, Richard W. Pogge, Fumio Abe, Richard Barry, David P. Bennett, Aparna Bhattacharya, Hirosane Fujii, Akihiko Fukui, Yuki Hirao, Stela Ishitani Silva, Yoshitaka Itow, Rintaro Kirikawa, Iona Kondo, Naoki Koshimoto, Yutaka Matsubara, Sho Matsumoto, Shota Miyazaki, Yasushi Muraki, Arisa Okamura, Greg Olmschenk, Clément Ranc, Nicholas J. Rattenbury, Yuki Satoh, Takahiro Sumi, Daisuke Suzuki, Taiga Toda, Paul . J. Tristram, Aikaterini Vandorou, Hibiki Yama
Figure caption: The (D  L , M host) and (a ⊥, M planet) contours with probability distributions of the lens properties of each event. The blue contour shows the best-fit case of each event. We present an alternate solution as the red contour for comparison. In the histograms, dark gray indicates a 68% confidence interval. The black dashed line indicates the median value of each property. For KMT-2021-BLG-0119 (upper panels), we present the Local B family (blue) and Local A family (red). For KMT-2021-BLG-0192 (middle panels), we compare the APRX (s −, u 0−) case (blue; the best fit) with the STD s + case (red). For KMT-2021-BLG-0192 (bottom panels), we present the W case (blue; the best fit) and R C  (red).
Paper: Unifying High- and Low-resolution Observations to Constrain the Dayside Atmosphere of KELT-20b/MASCARA-2b
Authors: David Kasper, Jacob L. Bean, Michael R. Line, Andreas Seifahrt, Madison T. Brady, Joshua Lothringer, Lorenzo Pino, Guangwei Fu, Stefan Pelletier, Julian Stürmer, Björn Benneke, Matteo Brogi, Jean-Michel Désert
Figure caption: Secondary eclipse spectra (planet-to-star flux ratio; left) and T–P profiles (right) for KELT-20b from PHOENIX model calculations (lines) compared to the HST and Spitzer data of Fu et al. (2022; points). The inset zooms in on the HST/WFC3 data. The nominal model assuming thermochemical equilibrium for a solar composition gas is shown in blue. Models with TiO and VO opacity artificially removed are shown in orange (solar metallicity) and green (10× solar metallicity). The primary impact of this opacity is its influence on the thermal structure of the atmosphere rather than specific spectral features in the plotted region. The sharp emission lines in the spectra are a result of absorption lines expected in the stellar spectrum.
Paper: Unifying High- and Low-resolution Observations to Constrain the Dayside Atmosphere of KELT-20b/MASCARA-2b
Authors: David Kasper, Jacob L. Bean, Michael R. Line, Andreas Seifahrt, Madison T. Brady, Joshua Lothringer, Lorenzo Pino, Guangwei Fu, Stefan Pelletier, Julian Stürmer, Björn Benneke, Matteo Brogi, Jean-Michel Désert
Figure caption: Maps of the CCF in the planet velocity (K  p ) vs. system velocity (V sys) plane using the combined data set and all 1D-RC model spectra explored (as labeled above each map). Dotted lines correspond to the known system velocity and exoplanet orbital speed of −24.5 km s−1 and 175 km s−1, respectively, as in Borsa et al. (2022). Continued below.
Paper: Unifying High- and Low-resolution Observations to Constrain the Dayside Atmosphere of KELT-20b/MASCARA-2b
Authors: David Kasper, Jacob L. Bean, Michael R. Line, Andreas Seifahrt, Madison T. Brady, Joshua Lothringer, Lorenzo Pino, Guangwei Fu, Stefan Pelletier, Julian Stürmer, Björn Benneke, Matteo Brogi, Jean-Michel Désert
Figure caption: Map of the CCF in the planet velocity (K  p ) vs. system velocity (V sys) plane using the theoretical Fe I mask. Dotted lines correspond to the known system velocity and exoplanet orbital speed of −24.5 km s−1 and 175 km s−1, respectively, as in Borsa et al. (2022).
Paper: Unifying High- and Low-resolution Observations to Constrain the Dayside Atmosphere of KELT-20b/MASCARA-2b
Authors: David Kasper, Jacob L. Bean, Michael R. Line, Andreas Seifahrt, Madison T. Brady, Joshua Lothringer, Lorenzo Pino, Guangwei Fu, Stefan Pelletier, Julian Stürmer, Björn Benneke, Matteo Brogi, Jean-Michel Désert
Figure caption: Detection S/N of the planetary atmospheric signal as a function of template stellar type. The responses of the red and blue channels are tracked individually by corresponding color. The combination of the two channels is shown in black.
Paper: FUMES. III. Ultraviolet and Optical Variability of M-dwarf Chromospheres
Authors: Girish M. Duvvuri, J. Sebastian Pineda, Zachory K. Berta-Thompson, Kevin France, Allison Youngblood
Figure caption: Each panel on the left side shows the posterior distribution for fitting the intrinsic variability in fractional flux for a different optical line: Hα on top, Hη in the middle, and Ca II K at the bottom. Each panel on the right side shows the posterior distribution for fitting the intrinsic variability in fractional flux for a different ultraviolet emission feature: the C IV doublet on top, He II 1640 line in the middle, and the Si IV doublet on the bottom. The distributions have been smoothed by a kernel density estimate and are color coded by star. The median measurement uncertainty for each star’s time series is also plotted as a vertical dotted line with the same color-coding although some of these vertical lines are very similar values and appear to overlap. The median measurement uncertainty falls well short of the 16th percentile for most of the optical distributions plotted here, indicating a detection of intrinsic variability that is distinguishable from photometric uncertainty. For most of the ultraviolet distributions, the opposite case is true with the median measurement uncertainty exceeding the 16th percentile and demonstrating that we are unable to detect intrinsic variability that is distinguishable from the noise of the data.
Paper: FUMES. III. Ultraviolet and Optical Variability of M-dwarf Chromospheres
Authors: Girish M. Duvvuri, J. Sebastian Pineda, Zachory K. Berta-Thompson, Kevin France, Allison Youngblood
Figure caption: The top panels show the LP 247-13 time-series data for Hα in absolute equivalent width (left) and dimensionless fractional flux (right) using red dots with error bars. The bottom panels show the LP 247-13 time-series data for C IV in a similar fashion, absolute flux on the left and fraction flux on the right. In each panel we use the median measurement uncertainty (σ median) of the panel’s light curve to shade the ± 1σ region in gray. Each panel’s plotted light curve has been fit individually and we use the median of the s parameter posterior distribution to shade the extra contribution in purple such that the boundaries of the shaded region are ﹩\mu \pm \sqrt{{\sigma }_{\mathrm{median}}^{2}+{s}^{2}}﹩ where μ is the median of the light curve. The size of the gray swath for the UV data shows how our signal-to-noise prevents us from detecting variability at the same scale as the optical.
Paper: FUMES. III. Ultraviolet and Optical Variability of M-dwarf Chromospheres
Authors: Girish M. Duvvuri, J. Sebastian Pineda, Zachory K. Berta-Thompson, Kevin France, Allison Youngblood
Figure caption: This figure shows the intrinsic variability fit distributions to the optical line equivalent width time series as a function of stellar Rossby number (as calculated in Pineda et al. 2021a). Each distribution is color coded by star and each panel shows a different line. Smaller Rossby numbers show higher variability and the Ca II H and K lines show more variability.
Paper: FUMES. III. Ultraviolet and Optical Variability of M-dwarf Chromospheres
Authors: Girish M. Duvvuri, J. Sebastian Pineda, Zachory K. Berta-Thompson, Kevin France, Allison Youngblood
Figure caption: This figure shows the intrinsic variability fit distributions to the optical line fractional flux time series as a function of stellar Rossby number (as calculated in Pineda et al. 2021a). Each distribution is color coded by star and each panel shows a different line. In this fractional space Hα is consistently less variable than the other optical chromospheric lines, but trends with Rossby number are less apparent.
Paper: FUMES. III. Ultraviolet and Optical Variability of M-dwarf Chromospheres
Authors: Girish M. Duvvuri, J. Sebastian Pineda, Zachory K. Berta-Thompson, Kevin France, Allison Youngblood
Figure caption: The panels correspond to individual stars, ordered by Rossby number with the lowest Rossby numbers (corresponding to faster rotating and more active stars) on top. Within the panels, the posterior distributions for fitting the intrinsic variability of the fractional flux light curves of the Balmer series are plotted with a color-scheme corresponding to the Balmer line order.
Paper: FUMES. III. Ultraviolet and Optical Variability of M-dwarf Chromospheres
Authors: Girish M. Duvvuri, J. Sebastian Pineda, Zachory K. Berta-Thompson, Kevin France, Allison Youngblood
Figure caption: This figure shows the Hα spectra for the three exposures of GJ 410 corresponding to the minimum (blue), median (black), and maximum (red) equivalent width measurements in the GJ 410 time series.
Paper: The Sensitivity of Eclipse Mapping to Planetary Rotation
Authors: Arthur D. Adams, Emily Rauscher
Figure caption: The uniform brightness curve and the first 10 eigencurves with uniform components added for our Warm Jupiter model, at a range of rotation rates with ψ LOS = 90°. Rotation rates span the scale from “slow” where the planet rotates a small fraction of a full rotation across eclipse (N tot < 1), to “fast” where the planet rotates through a considerable fraction of a full rotation. At the fastest rate the planet’s rotation period approaches the duration of ingress or egress (N i,e → 1). For reference, with our prototypical 10 day Warm Jupiter, N tot = 1 corresponds to ω rot/ω orb ≈ 71, and N i,e = 1 corresponds to ω rot/ω orb ≈ 432. One might notice that some eigencurves dip below the baseline where the planet is completely behind the star and contributes nothing to the observed flux. The eigencurves are initially generated from spherical harmonics, which each have zero net global brightness. Therefore, the eigencurves themselves start out as combinations of maps which each have zero net brightness. To “reconstitute” the eigencurves, we plot them here as additions on top of the uniform brightness curve. This allows for an eclipse curve composed of a single eigenmode to be unphysical, i.e., have net negative flux at some points.
Paper: The Sensitivity of Eclipse Mapping to Planetary Rotation
Authors: Arthur D. Adams, Emily Rauscher
Figure caption: The uniform brightness curve and the first 10 eigencurves with uniform components added for our Warm Jupiter model, at a range of rotation rates with zero obliquity. Rotation rates span the scale from “slow” where the planet rotates a small fraction of a full rotation across eclipse (N tot < 1), to “fast” where the planet rotates through a considerable fraction of a full rotation. At the fastest rate the planet’s rotation period approaches the duration of ingress or egress (N i,e → 1). For reference, with our prototypical 10 day Warm Jupiter, N tot = 1 corresponds to ω rot/ω orb ≈ 71, and N i,e = 1 corresponds to ω rot/ω orb ≈ 432. One might notice that some eigencurves dip below the baseline where the planet is completely behind the star and contributes nothing to the observed flux. The eigencurves are initially generated from spherical harmonics, which each have zero net global brightness. Therefore, the eigencurves themselves start out as combinations of maps which each have zero net brightness. To “reconstitute” the eigencurves, we plot them here as additions on top of the uniform brightness curve. This allows for an eclipse curve composed of a single eigenmode to be unphysical, i.e., have net negative flux at some points.
Paper: The Sensitivity of Eclipse Mapping to Planetary Rotation
Authors: Arthur D. Adams, Emily Rauscher
Figure caption: The first 10 modes of eigenmaps identified by PCA for the Warm Jupiter case with ψ LOS = 90°. We show the maps in a Mollweide projection that shows all longitudes and latitudes. The reference hemisphere, which is the hemisphere that faces the observer at the midpoint phase of ingress, is centered in each projection and outlined with a dotted white circle. Green dashed contours represent the equator and ±45° latitude lines, with arrows indicating the direction of rotation. Rotation rates span the scale from “slow” where the planet rotates a small fraction of a full rotation across eclipse (N tot < 1), to “fast” where the planet rotates through a considerable fraction of a full rotation. At the fastest rate the planet’s rotation period approaches the duration of ingress or egress (N i,e → 1). For reference, with our prototypical 10 day Warm Jupiter, N tot = 1 corresponds to ω rot/ω orb ≈ 71, and N i,e = 1 corresponds to ω rot/ω orb ≈ 432.
Paper: The Sensitivity of Eclipse Mapping to Planetary Rotation
Authors: Arthur D. Adams, Emily Rauscher
Figure caption: The uniform brightness curve and the first 10 eigencurves with uniform components added for our Warm Jupiter model, at a range of rotation rates with ψ sky = 90°. Rotation rates span the scale from “slow” where the planet rotates a small fraction of a full rotation across eclipse (N tot < 1), to “fast” where the planet rotates through a considerable fraction of a full rotation. At the fastest rate the planet’s rotation period approaches the duration of ingress or egress (N i,e → 1). For reference, with our prototypical 10 day Warm Jupiter, N tot = 1 corresponds to ω rot/ω orb ≈ 71, and N i,e = 1 corresponds to ω rot/ω orb ≈ 432. One might notice that some eigencurves dip below the baseline where the planet is completely behind the star and contributes nothing to the observed flux. The eigencurves are initially generated from spherical harmonics, which each have zero net global brightness. Therefore, the eigencurves themselves start out as combinations of maps which each have zero net brightness. To “reconstitute” the eigencurves, we plot them here as additions on top of the uniform brightness curve. This allows for an eclipse curve composed of a single eigenmode to be unphysical, i.e., have net negative flux at some points.
Paper: The Sensitivity of Eclipse Mapping to Planetary Rotation
Authors: Arthur D. Adams, Emily Rauscher
Figure caption: The first 10 modes of eigenmaps identified by PCA for the zero-obliquity Warm Jupiter case. We show the maps in a Mollweide projection that shows all longitudes and latitudes. The reference hemisphere, which is the hemisphere that faces the observer at the midpoint phase of ingress, is centered in each projection and outlined with a dotted white circle. Green dashed contours represent the equator and ±45° latitude lines, with arrows indicating the direction of rotation. Rotation rates span the scale from “slow” where the planet rotates a small fraction of a full rotation across eclipse (N tot < 1), to “fast” where the planet rotates through a considerable fraction of a full rotation. At the fastest rate the planet’s rotation period approaches the duration of ingress or egress (N i,e → 1). For reference, with our prototypical 10 day Warm Jupiter, N tot = 1 corresponds to ω rot/ω orb ≈ 71, and N i,e = 1 corresponds to ω rot/ω orb ≈ 432.
Paper: The Sensitivity of Eclipse Mapping to Planetary Rotation
Authors: Arthur D. Adams, Emily Rauscher
Figure caption: The first 10 modes of eigenmaps as identified by PCA for the Warm Jupiter case with ψ sky = 90°. We show the maps in a Mollweide projection that shows all longitudes and latitudes. The reference hemisphere, which is the hemisphere that faces the observer at the midpoint phase of ingress, is centered in each projection and outlined with a dotted white circle. Green dashed contours represent the equator and ±45° latitude lines, with arrows indicating the direction of rotation. Rotation rates span the scale from “slow” where the planet rotates a small fraction of a full rotation across eclipse (N tot < 1), to “fast” where the planet rotates through a considerable fraction of a full rotation. At the fastest rate the planet’s rotation period approaches the duration of ingress or egress (N i,e → 1). For reference, with our prototypical 10 day Warm Jupiter, N tot = 1 corresponds to ω rot/ω orb ≈ 71, and N i,e = 1 corresponds to ω rot/ω orb ≈ 432.
Paper: The Sensitivity of Eclipse Mapping to Planetary Rotation
Authors: Arthur D. Adams, Emily Rauscher
Figure caption: The first 10 eigencurves identified by PCA for the zero-obliquity, spin-synchronous Warm Jupiter case. Brightness variations are approximately isolated to a single hemisphere, since the planet does not rotate very much over the duration of the secondary eclipse. The maps represent the simplest two-dimensional patterns that produce sinusoid-like one-dimensional variations in the light curve during ingress and egress, with time “wavelengths” in half-multiples of the ingress and egress durations. Even and odd eigenmodes are symmetric and antisymmetric, respectively, in reflection across the subobserver longitude.
Paper: The Sensitivity of Eclipse Mapping to Planetary Rotation
Authors: Arthur D. Adams, Emily Rauscher
Figure caption: Diagrams of the geometry and timing of the eclipse, as well as the reference frame for the two dimensions defining the orientation of the planet’s spin axis.
Paper: Detection of Strongly Lensed Arcs in Galaxy Clusters with Transformers
Authors: Peng Jia, Ruiqi Sun, Nan Li, Yu Song, Runyu Ning, Hongyan Wei, Rui Luo
Figure caption: The performance of the DETR and the Deformable DETR in detection of CGSLs. The vertical axis represents the precision rate, and the horizontal axis represents the recall rate. The farther to the right of the curves, the higher the recall, and the higher the curves, the better the precision. We can use the area of the P–R curve to evaluate the performance. The larger the area surrounded by the curves, the better the overall performance of the algorithm. IOU represents the degree of overlap between the predicted targets and the real targets. The larger IOU is, the greater the overlap is, indicating that the location of the targets predicted by the algorithm is more accurate. We take different IOUs as the threshold, and only when the overlap degree between the targets predicted by the algorithm and the real targets is larger than the threshold do we consider the prediction to be accurate.
Paper: Detection of Strongly Lensed Arcs in Galaxy Clusters with Transformers
Authors: Peng Jia, Ruiqi Sun, Nan Li, Yu Song, Runyu Ning, Hongyan Wei, Rui Luo
Figure caption: The structure of the DETR. The input image is first extracted by the backbone neural network to obtain features. After adding the encoded position information of feature vectors, feature vectors are sent to an encoder–decoder structure. The output decoded vectors will get the category and the position information of the target in the input image through two FFN branches. The last bipartite-matching structure of the network is to match the prediction output with the label of the input to help the network to achieve the correct prediction result. The variables ﹩\hat{p}﹩ and ﹩\hat{b}﹩ represent the category and position box predicted by the algorithm, respectively, l and b represent the real category and position box of the target, respectively, and lines represent the process of cross-matching between the predicted results and real labels of this bipartite-matching part. Each oval is an object, which could be either the background or a target. The circles inside the ovals represent the value of the position or category.
Paper: Detection of Strongly Lensed Arcs in Galaxy Clusters with Transformers
Authors: Peng Jia, Ruiqi Sun, Nan Li, Yu Song, Runyu Ning, Hongyan Wei, Rui Luo
Figure caption: This figure shows a simulated image of the same strong gravitational lensing system with and without foreground central galaxies. Panels (a) and (b) show the same CSGL, but there are obvious foreground central galaxies in panel (a) and no foreground central galaxies in panel (b). We can easily see the morphological characteristics of the CSGL. The arrow in panel (a) points to the foreground central galaxies. We can see the arc of the strong gravitational lensing system after we remove foreground central galaxies, which is pointed to by the arrow in panel (b). Therefore, CSGLs with foreground central galaxies interference like in panel a pose high requirements and challenges to our detection algorithm.
Paper: Detection of Strongly Lensed Arcs in Galaxy Clusters with Transformers
Authors: Peng Jia, Ruiqi Sun, Nan Li, Yu Song, Runyu Ning, Hongyan Wei, Rui Luo
Figure caption: The encoder–decoder structure of the transformer. Features of images are first extracted by the CNN backbone, and then the multihead self-attention layer would generate attention of these features in the encoder. In the decoder, several object queries will sample features automatically, and the mask multihead self-attention layer will process these features with the self-attention mechanism. Finally, in the multihead cross-attention layer, features will further be processed to generate cross-attention to detect targets.
Paper: Detection of Strongly Lensed Arcs in Galaxy Clusters with Transformers
Authors: Peng Jia, Ruiqi Sun, Nan Li, Yu Song, Runyu Ning, Hongyan Wei, Rui Luo
Figure caption: This figure shows several images without and with CGSLs. Panel (a)–(e) show images without CGSLs, and panels (f)–(j) show images with CGSLs. We find that in the images with CGSLs there are arcs, but in the images without CGSLs there are no similar structures.