adsabs/AstroCLIMB huggingface dataset:
Paper: The Sensitivity of Eclipse Mapping to Planetary Rotation
Authors: Arthur D. Adams, Emily Rauscher
Figure caption: Maps and curves from fits to simulated data from model planets rotating at a range of rates ω rot/ω orb = 1–30, when observations span ±10% of the orbit centered on a secondary eclipse. The input map is shown at the top in green, as it would be oriented to the observer at the time of mid-ingress. The equivalent full-globe Mollweide projection is shown just below to compare with the equivalent projections of the retrieved maps. ω rot/ω orb is equivalent to how many times the planet rotates per orbit. We generate 10,000 realizations of Gaussian noise for each simulated phase curve, and fit each with some number n of eigencurves (i.e., parameters), where n is determined by a Bayesian Information Criterion (BIC). The mode and ±1σ of n are shown for each case. Recovered curves and maps are shown based on the rotation rate of the model used to generate the data (x-axis), vs. the rotation rate of the eigenbasis used to fit the data (y-axis). Each subplot of the phase curve grid shows the range of uncertainty used to artificially noise the simulated data, with one example realization of the 10,000 in colored points. The fit is shown in greyscale; the solid line is the median of all fits and a shaded region shows the ±1σ (≈16th and 84th percentiles) range of fits. We show maps generated using the median values of the coefficients from each eigenmap.
Paper: The Sensitivity of Eclipse Mapping to Planetary Rotation
Authors: Arthur D. Adams, Emily Rauscher
Figure caption: Eclipse maps and curves from fits to simulated data from model planets with polar hotspots with spin axes inclined from 0–90° with respect to the plane of the sky. The input maps are shown at the top in green, as it would be oriented to the observer at the time of mid-ingress. The equivalent full-globe Mollweide projections are shown just below to compare with the equivalent projections of the retrieved maps. ω rot/ω orb is equivalent to how many times the planet rotates per orbit. We generate 10,000 realizations of Gaussian noise for each simulated eclipse curve, and fit each with some number n of eigencurves (i.e., parameters), where n is determined by a Bayesian Information Criterion (BIC). The mode and ±1σ of n are shown for each case. Recovered curves and maps are shown based on the rotation rate of the model used to generate the data (x-axis), vs. the rotation rate of the eigenbasis used to fit the data (y-axis). Each subplot of the eclipse curve grid shows the range of uncertainty used to artificially noise the simulated data, with one example realization of the 10,000 in colored points. The fit is shown in greyscale; the solid line is the median of all fits and a shaded region shows the ±1σ (≈16th and 84th percentiles) range of fits. We show maps generated using the median values of the coefficients from each eigenmap.
Paper: The Sensitivity of Eclipse Mapping to Planetary Rotation
Authors: Arthur D. Adams, Emily Rauscher
Figure caption: Eclipse maps and curves from fits to simulated data from model planets rotating at a range of rates ω rot/ω orb = 1–30. The input map is shown at the top in green, as it would be oriented to the observer at the time of mid-ingress. The equivalent full-globe Mollweide projection is shown just below to compare with the equivalent projections of the retrieved maps. ω rot/ω orb is equivalent to how many times the planet rotates per orbit. We generate 10,000 realizations of Gaussian noise for each simulated eclipse curve, and fit each with some number n of eigencurves (i.e., parameters), where n is determined by a Bayesian Information Criterion (BIC). The mode and ±1σ of n are shown for each case. Recovered curves and maps are shown based on the rotation rate of the model used to generate the data (x-axis), vs. the rotation rate of the eigenbasis used to fit the data (y-axis). Each subplot of the eclipse curve grid shows the range of uncertainty used to artificially noise the simulated data, with one example realization of the 10,000 in colored points. The fit is shown in greyscale; the solid line is the median of all fits and a shaded region shows the ±1σ (≈16th and 84th percentiles) range of fits. We show maps generated using the median values of the coefficients from each eigenmap.
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 = 30°. 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 obliquities along the line of sight (ψ LOS). The maps shown here are purely latitudinal, i.e., are insensitive to rotation rate, to show the effects of axial orientation alone. 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 obliquities in the sky plane (ψ sky). The maps shown here are purely latitudinal, i.e., are insensitive to rotation rate, to show the effects of axial orientation alone. 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 that 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 as identified by PCA for the Warm Jupiter case, at a range of obliquities along the line of sight (ψ LOS). The maps shown here are purely latitudinal, i.e., are insensitive to rotation rate, to show the effects of axial orientation alone. 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.
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, at a range of obliquities in the sky plane (ψ sky). The maps shown here are purely latitudinal, i.e., are insensitive to rotation rate, to show the effects of axial orientation alone. 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.
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 schematic diagram of the Deformable DETR. This structural paradigm is the same as that of the DETR with the following differences. First of all, the backbone neural network in the Deformable DETR outputs feature vector maps c3, c4, and c5 from its last three layers. We transform the depth (number of channels) of these three feature vector maps to the same size, thus forming three feature vector maps of p3, p4, and p5 accordingly. Among them, we would convolve feature vector maps of c5 with convolutional layers to further extract feature information and obtain p6 for further processing. We would stack these feature vector maps together to form a multiscale feature, similar to the feature pyramid network (FPN). Second, in the encoder–decoder part, the attention mechanism is improved by introducing reference vectors and sampling vectors. Only the attention map between reference vectors and sampling vectors is calculated, thus greatly reducing the computation complexity and improving the training speed of the model. In this figure, “Flattened Feature Token” represents feature vectors that we input into the transformer. Object queries in different colors represent targets predicted by our model, which is far greater than the actual number of targets in the image. Therefore, in the bipartite matching, most original target instances will be led to “no object,” and only a few will be led to corresponding true 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: The performances of our algorithm in detection of CSGLs with and without foreground central galaxies. Panels (a), (b), and (c) show the detection ability of our method on data sets with no foreground central galaxies, all of them with foreground central galaxies, and half of them without foreground central galaxies at different IOU thresholds, respectively. It can be seen that the model performs best when there are no foreground central galaxies and worst when there are foreground central galaxies, indicating that foreground central galaxies have a certain influence on the performance of our algorithm. In panel (d) the vertical axis represents the area of the P–R curve (mAP) for different data sets of the model, and the horizontal axis represents different IOU thresholds. It can be seen that when the IOU threshold is small or large, the difference of the mAP is small, indicating that the foreground central galaxies have little interference to the model at this time. When the IOU threshold is around 0.3, the performance difference of the mAP is the largest. When the algorithm is only used for the classification task of the strong gravitational lens, the mAP is close to 100%, which indicates its strong classification ability.
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 self-attention map of four pixels in the last layer of the encoder. Panels (a) and (b) are CGSLs with and without foreground central galaxies, respectively. We find that the self-attention maps of them are similar, which indicates that our model can still identify CGSL features accurately and has good robustness even in the presence of foreground central galaxies. It also shows that the encoder is paying attention to the main structure of the CGSL, regardless of BCGs.
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 the schematic diagram of three different approaches. Panel (a) shows the classic method, which first detects images of galaxy clusters. Then, these stamp images would be cut from original observation images and classified as either CGSLs or other targets, as discussed in Jacobs et al. (2017), Petrillo et al. (2017), Ostrovski et al. (2017), Bom et al. (2017), Hartley et al. (2017), Avestruz et al. (2019), and Lanusse et al. (2018). Panel (b) shows the diagram to use our algorithm in a one-step way, which directly detects CGSLs from observational images. Panel (c) shows the two-step strategy. We first obtain candidates with low IOU and then detect CGSLs from these images.
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 detection results of the two different methods in four panels each. Panels (a)–(d) show the detection results of the DETR, and panels (e)–(h) show the detection results of the Deformable DETR. We find that the DETR gives correct results for panels (a) and (b), while the Deformable DETR could give correct results for panels (g) and (h). Meanwhile, for panels (g) and (h), we find that the arc is obscured by the foreground galaxy, so it is difficult for eyes to see it, but our algorithm can effectively identify and locate it.
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 cross-attention map of the last layer in the decoder. Input images are the same as those in Figure 14. In this figure, right panels are prediction results, and left panels are cross-attention maps of object queries matching predicted results in the decorder, whose legend represents the serial number of the object query. We can see that the decoder pays more attention to boundaries of CGSLs and gives similar results regardless of the interference of foreground central galaxies.
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 learning curve of the DETR and that of the Deformable DETR. As we see in this figure, the Deformable DETR will converge after around 100 epochs and the DETR will require 200 epochs to converge. However, the DETR has a higher mAP after training.
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 mAP of the DETR and that of the Deformable DETR with different IOU criteria. As shown in this figure, we find that performance of the DETR is better than that of the Deformable DETR.
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 results of our algorithm in detection of CSGLs with and without foreground central galaxies. We can see that our algorithm can detect CSGLs regardless of foreground galaxies.
Paper: The Eclipsing Binaries from the LAMOST Medium-resolution Survey. III. A High-precision Empirical Stellar Mass Library
Authors: Jianping Xiong, Chao Liu, Jiao Li, Jiadong Li, Bo Zhang, Xiaodian Chen, Changqing Luo, Zihuang Cao, Yongheng Zhao
Figure caption:  T eff–logg diagram of samples. From left to right and top to bottom, the panels display the samples with [M/H] ≤ −0.3, −0.3 < [M/H] ≤ −0.1, −0.1 < [M/H] ≤ +0.1, and [M/H] > +0.1, respectively. The blue dots are the samples compiled from the literature and the red rectangles are the samples from LAMOST MRS. The lines are the isochrones given by PARSEC models (Bressan et al. 2012) with color-coded ages ranging from 10 Myr to 10 Gyr.
Paper: The Eclipsing Binaries from the LAMOST Medium-resolution Survey. III. A High-precision Empirical Stellar Mass Library
Authors: Jianping Xiong, Chao Liu, Jiao Li, Jiadong Li, Bo Zhang, Xiaodian Chen, Changqing Luo, Zihuang Cao, Yongheng Zhao
Figure caption: (a) Radial velocity curves. In the top panel, the red and blue dots are the observed radial velocities of KIC 5359678 (Wang et al. 2021). The best-fit result derived by PHOEBE is shown with black solid lines. The bottom panel shows the residuals of radial velocities between the model and observations. (b) The top panel shows the light curve for KIC 5359678 with black dots. The red solid line represents the best-fit model derived by PHOEBE. The bottom panel shows the residuals between model and observations.
Paper: The Eclipsing Binaries from the LAMOST Medium-resolution Survey. III. A High-precision Empirical Stellar Mass Library
Authors: Jianping Xiong, Chao Liu, Jiao Li, Jiadong Li, Bo Zhang, Xiaodian Chen, Changqing Luo, Zihuang Cao, Yongheng Zhao
Figure caption: As a sample of the orbital solution, this figure shows the MCMC-derived probability density function of the orbital parameters for KIC 5359678 (Wang et al. 2021). From the left column to the right, the histograms are for q, i, ﹩\sqrt{e}\cos \omega ﹩, ﹩\sqrt{e}\sin \omega ﹩, a, V  γ , T 2, R 1, and R 2.
Paper: The Eclipsing Binaries from the LAMOST Medium-resolution Survey. III. A High-precision Empirical Stellar Mass Library
Authors: Jianping Xiong, Chao Liu, Jiao Li, Jiadong Li, Bo Zhang, Xiaodian Chen, Changqing Luo, Zihuang Cao, Yongheng Zhao
Figure caption: The [M/H] distribution of sample stars compiled in this work.
Paper: The Eclipsing Binaries from the LAMOST Medium-resolution Survey. III. A High-precision Empirical Stellar Mass Library
Authors: Jianping Xiong, Chao Liu, Jiao Li, Jiadong Li, Bo Zhang, Xiaodian Chen, Changqing Luo, Zihuang Cao, Yongheng Zhao
Figure caption: The T eff–M diagram. (a) Total samples with various metallicities. The solid lines stand for the ZAMS with color-coded [M/H] from PARSEC, while the dots are the samples compiled from the literature. The rectangles and triangles are the primary and secondary stars, respectively, from LAMOST MRS. (b)–(e) T eff–M diagrams for stars with different ranges of [M/H]. The theoretical ZAMS lines from PARSEC are shown as black dashed lines. The blue dots, open red rectangles, and open cyan triangles indicate the stars from the literature, and the primary and secondary stars from LAMOST MRS, respectively. The parameters of the secondary stars are estimated from PHOEBE. The thick solid gray lines display the best-fit quartic polynomial of M as a function of T eff and [M/H]. The bottom of each of panels (b)–(e) illustrates the relative residuals, defined as D relative = (M obs − M poly)/M poly, in which M obs and M poly are masses from observation and polynomial model respectively.
Paper: The Eclipsing Binaries from the LAMOST Medium-resolution Survey. III. A High-precision Empirical Stellar Mass Library
Authors: Jianping Xiong, Chao Liu, Jiao Li, Jiadong Li, Bo Zhang, Xiaodian Chen, Changqing Luo, Zihuang Cao, Yongheng Zhao
Figure caption: The T eff–R diagram. (a) The overall distribution of all samples using dots (known stars from the literature), rectangles (primary of the new samples from LAMOST), and triangles (secondary stars from LAMOST). The solid lines indicate the ZAMS lines with color-coded [M/H] from PARSEC. The dots also with color-coded [M/H] are the samples compiled from the literature and the rectangles are the samples selected from LAMOST MRS. (b) The T eff–R diagram with [M/H]< −0.3, in which the filled blue circles, open red rectangles, and open cyan triangles are same as in Figures 5(b)–(e). The black solid and dashed lines are the ZAMS lines from PARSEC for Z = 0.008 and 0.004, respectively. The magenta dotted–dashed, blue dotted, and cyan dashed lines stand for three isochrones with ages of 1, 5, and 10 Gyr, respectively, at Z = 0.008. Panels (c)–(e) are similar to panel (b), but at different metallicities.
Paper: The Eclipsing Binaries from the LAMOST Medium-resolution Survey. III. A High-precision Empirical Stellar Mass Library
Authors: Jianping Xiong, Chao Liu, Jiao Li, Jiadong Li, Bo Zhang, Xiaodian Chen, Changqing Luo, Zihuang Cao, Yongheng Zhao
Figure caption: (a) The M–L diagram. The solid lines with different colors are the ZAMS lines with color-coded [M/H] from PARSEC. The dots, rectangles, and triangles stand for the stars from the literature and the primary and secondary stars from LAMOST MRS, respectively. The colors of the symbols are the same as those of the ZAMS lines. (b)–(e) The M–L diagrams with different ranges of [M/H]. The dashed lines are the ZAMS lines from PARSEC. The thick solid lines are the best-fit polynomial model. The other symbols are the same as in Figures 5(b)–(e).
Paper: The Eclipsing Binaries from the LAMOST Medium-resolution Survey. III. A High-precision Empirical Stellar Mass Library
Authors: Jianping Xiong, Chao Liu, Jiao Li, Jiadong Li, Bo Zhang, Xiaodian Chen, Changqing Luo, Zihuang Cao, Yongheng Zhao
Figure caption: (a) Histogram of the relative residuals (D relative = (M  x  − M)/M, where x represents either g or LG) between dynamic mass (M) and the mass derived from model or relations. (b) Histogram of the relative residuals (D relative = (R  x  − R)/R, where x stands for g, LG, SED, or Mg) between dynamic radius (R) and the radius derived from model or relations.
Paper: The Eclipsing Binaries from the LAMOST Medium-resolution Survey. III. A High-precision Empirical Stellar Mass Library
Authors: Jianping Xiong, Chao Liu, Jiao Li, Jiadong Li, Bo Zhang, Xiaodian Chen, Changqing Luo, Zihuang Cao, Yongheng Zhao
Figure caption: The mass–radius diagram. (a) The overall M–R distribution of the samples including both the compiled data (dots with color-coded metallicities) and LAMOST data (rectangles and triangles represent primary and secondary stars, respectively). The solid lines with color-coded metallicities are the ZAMS lines from PARSEC. (b)–(e) The M–R relations in different ranges of [M/H]. The dotted or dashed lines indicate ZAMS lines with similar metallicity. The symbols are the same as in Figures 5(b)–(e).
Paper: The Eclipsing Binaries from the LAMOST Medium-resolution Survey. III. A High-precision Empirical Stellar Mass Library
Authors: Jianping Xiong, Chao Liu, Jiao Li, Jiadong Li, Bo Zhang, Xiaodian Chen, Changqing Luo, Zihuang Cao, Yongheng Zhao
Figure caption: Summary of approaches to measuring parameters. The rectangles in the second layer are measurements for mass and radius. In the third layer, the solid blocks are the parameters directly obtained from observation and the dotted blocks are the parameters derived from some relations. The green circles are relationships between parameters.