adsabs/AstroCLIMB huggingface dataset:
Paper: The Impact of Bayesian Hyperpriors on the Population-level Eccentricity Distribution of Imaged Planets
Authors: Vighnesh Nagpal, Sarah Blunt, Brendan P. Bowler, Trent J. Dupuy, Eric L. Nielsen, Jason J. Wang
Figure caption: Same as Figure 10, but for the sample of 18 brown dwarfs from BBN20. The hyperprior does not exert a significant influence on the inferred population-level eccentricity distributions for this sample.
Paper: The Impact of Bayesian Hyperpriors on the Population-level Eccentricity Distribution of Imaged Planets
Authors: Vighnesh Nagpal, Sarah Blunt, Brendan P. Bowler, Trent J. Dupuy, Eric L. Nielsen, Jason J. Wang
Figure caption: Results of applying the forward-modeling process described in Section 5.1 to simulated samples of imaged companions drawn from an underlying uniform eccentricity distribution. Four sample sizes (N = 5, 10, 20, and 45) and four hyperpriors (log-uniform, uniform, Gaussian, log-normal) are tested. In each panel, we show randomly drawn distributions from the posterior on (α, β) and highlight the eccentricity distribution corresponding to the median recovered hyperparameter values in bold. The corresponding value of ﹩{ \mathcal M }﹩ quantifies the level of agreement between the recovered and input underlying distributions. For reference, we plot the underlying eccentricity distribution (in this case, the uniform distribution) as a dashed line. For each sample size, the Gaussian hyperprior performs the best, followed by the log-uniform, uniform, and log-normal hyperpriors. The uniform, log-uniform and log-normal hyperpriors impose a strong bias toward narrowly peaked distributions that is most evident for small sample sizes.
Paper: Automatic Classification of Galaxy Morphology: A Rotationally-invariant Supervised Machine-learning Method Based on the Unsupervised Machine-learning Data Set
Authors: GuanWen Fang, Shuo Ba, Yizhou Gu, Zesen Lin, Yuejie Hou, Chenxin Qin, Chichun Zhou, Jun Xu, Yao Dai, Jie Song, Xu Kong
Figure caption: Distributions of the image S/Ns (left) and positional angles (right) of galaxies in the UML data set.
Paper: Automatic Classification of Galaxy Morphology: A Rotationally-invariant Supervised Machine-learning Method Based on the Unsupervised Machine-learning Data Set
Authors: GuanWen Fang, Shuo Ba, Yizhou Gu, Zesen Lin, Yuejie Hou, Chenxin Qin, Chichun Zhou, Jun Xu, Yao Dai, Jie Song, Xu Kong
Figure caption: A flow chart of the preprocess phase before feeding the network, including adaptive polar-coordinate transformation, and mirroring.
Paper: Automatic Classification of Galaxy Morphology: A Rotationally-invariant Supervised Machine-learning Method Based on the Unsupervised Machine-learning Data Set
Authors: GuanWen Fang, Shuo Ba, Yizhou Gu, Zesen Lin, Yuejie Hou, Chenxin Qin, Chichun Zhou, Jun Xu, Yao Dai, Jie Song, Xu Kong
Figure caption: Examples of the conventional polar-coordinate transformation. It shows that the conventional polar-coordinate transformation cannot transform the rotations of the raw images into perfect translations of the new images.
Paper: Automatic Classification of Galaxy Morphology: A Rotationally-invariant Supervised Machine-learning Method Based on the Unsupervised Machine-learning Data Set
Authors: GuanWen Fang, Shuo Ba, Yizhou Gu, Zesen Lin, Yuejie Hou, Chenxin Qin, Chichun Zhou, Jun Xu, Yao Dai, Jie Song, Xu Kong
Figure caption: The accuracy of the GoogLeNet with and without APCT on the validation sets as a function of rotational angles.
Paper: Automatic Classification of Galaxy Morphology: A Rotationally-invariant Supervised Machine-learning Method Based on the Unsupervised Machine-learning Data Set
Authors: GuanWen Fang, Shuo Ba, Yizhou Gu, Zesen Lin, Yuejie Hou, Chenxin Qin, Chichun Zhou, Jun Xu, Yao Dai, Jie Song, Xu Kong
Figure caption: The t-SNE visualization graphs of 2000 randomly selected images that are labeled by the three SML methods. There is no obvious evidence that one model has an advantage over the two.
Paper: Automatic Classification of Galaxy Morphology: A Rotationally-invariant Supervised Machine-learning Method Based on the Unsupervised Machine-learning Data Set
Authors: GuanWen Fang, Shuo Ba, Yizhou Gu, Zesen Lin, Yuejie Hou, Chenxin Qin, Chichun Zhou, Jun Xu, Yao Dai, Jie Song, Xu Kong
Figure caption: Comparisons between the input (left) and reconstructed (right) images in the noise reduction by using the CAE. The reconstructed images not only maintain the main morphological features but also remove the redundant pixels, and thus reduce the noises.
Paper: Automatic Classification of Galaxy Morphology: A Rotationally-invariant Supervised Machine-learning Method Based on the Unsupervised Machine-learning Data Set
Authors: GuanWen Fang, Shuo Ba, Yizhou Gu, Zesen Lin, Yuejie Hou, Chenxin Qin, Chichun Zhou, Jun Xu, Yao Dai, Jie Song, Xu Kong
Figure caption: The precision (top) and the recall (bottom) of each morphological types in the validation set for the three SML models, i.e., Attention56 (left), DenseNet121 (middle), and GoogLeNet (right).
Paper: Automatic Classification of Galaxy Morphology: A Rotationally-invariant Supervised Machine-learning Method Based on the Unsupervised Machine-learning Data Set
Authors: GuanWen Fang, Shuo Ba, Yizhou Gu, Zesen Lin, Yuejie Hou, Chenxin Qin, Chichun Zhou, Jun Xu, Yao Dai, Jie Song, Xu Kong
Figure caption: A comparison between the conventional and the adaptive polar-coordinate transformation. Our method (i.e., APCT) keeps the resulting images almost invariant regardless of how the raw images are rotated.
Paper: Automatic Classification of Galaxy Morphology: A Rotationally-invariant Supervised Machine-learning Method Based on the Unsupervised Machine-learning Data Set
Authors: GuanWen Fang, Shuo Ba, Yizhou Gu, Zesen Lin, Yuejie Hou, Chenxin Qin, Chichun Zhou, Jun Xu, Yao Dai, Jie Song, Xu Kong
Figure caption: An overview of the three SML networks.
Paper: The Impact of Bayesian Hyperpriors on the Population-level Eccentricity Distribution of Imaged Planets
Authors: Vighnesh Nagpal, Sarah Blunt, Brendan P. Bowler, Trent J. Dupuy, Eric L. Nielsen, Jason J. Wang
Figure caption: Results of applying HBM to the eccentricity posteriors for the sample of nine imaged planets from BBN20. Left panel: Eccentricity distributions inferred using a uniform hyperprior on (α, β) with bounds [0.01, 1000]. These results mirror those from BBN20. Middle panel: Distributions recovered by imposing a truncated Gaussian hyperprior with μ  =  0.69 and σ = 1.0. Shown for reference (dashed line) is the RV exoplanet eccentricity distribution. Based on the results of the hyperprior tests in Sections 4 and 5, we interpret this as evidence for a similarity between the eccentricity distributions of widely separated imaged giant planets and close-in RV exoplanets. Right panel: Results obtained by imposing a log-uniform hyperprior with bounds [0.01, 100]. We note the similarity between the distributions recovered for this sample (N = 9) and the distributions recovered by the log-uniform prior for the N = 10 case in Figure 9.
Paper: Automatic Classification of Galaxy Morphology: A Rotationally-invariant Supervised Machine-learning Method Based on the Unsupervised Machine-learning Data Set
Authors: GuanWen Fang, Shuo Ba, Yizhou Gu, Zesen Lin, Yuejie Hou, Chenxin Qin, Chichun Zhou, Jun Xu, Yao Dai, Jie Song, Xu Kong
Figure caption: Relationships between morphological types and other galaxy properties for massive galaxies in the UML remaining data set. The upper panels summarize the distribution of galaxies in rest frame U − V vs. V − J color space and in the G − M 20 space. The contour levels indicate the 20%, 50%, and 80% of the corresponding subclass from inner to outskirts. The data points are 100 galaxies randomly selected from each subclass, where the total numbers are given in the corner. The bottom panels are the distribution of n and r  e , with the median values represented as the upper bricks. The SPH, ETD, LTD, and IRR categories are represented by red, blue, green, and cyan colors, respectively.
Paper: Automatic Classification of Galaxy Morphology: A Rotationally-invariant Supervised Machine-learning Method Based on the Unsupervised Machine-learning Data Set
Authors: GuanWen Fang, Shuo Ba, Yizhou Gu, Zesen Lin, Yuejie Hou, Chenxin Qin, Chichun Zhou, Jun Xu, Yao Dai, Jie Song, Xu Kong
Figure caption: The t-SNE visualization graphs of 2000 randomly selected images that are labeled by different results. Image (a) is the results of the GoogLeNet model with APCT, image (b) is the results of the GoogLeNet model without APCT, and image (c) is the results of Huertas-Company et al. (2015). It shows that both SML models give the labeled galaxies with clear boundaries in the t-SNE visualization graphs, proving the effectiveness of the proposed method.
Paper: Automatic Classification of Galaxy Morphology: A Rotationally-invariant Supervised Machine-learning Method Based on the Unsupervised Machine-learning Data Set
Authors: GuanWen Fang, Shuo Ba, Yizhou Gu, Zesen Lin, Yuejie Hou, Chenxin Qin, Chichun Zhou, Jun Xu, Yao Dai, Jie Song, Xu Kong
Figure caption: Examples of galaxies that are correctly labeled by the rotationally-invariant SML model with APCT but incorrectly labeled by the same model without APCT. These galaxies are first randomly selected from galaxies with disputed labels by the SML models with and without APCT and then verified by visual inspection.
Paper: unTimely: a Full-sky, Time-domain unWISE Catalog
Authors: Aaron M. Meisner, Dan Caselden, Edward F. Schlafly, Frank Kiwy
Figure caption: All S/N >3 unTimely Catalog W2 detections (black plus marks) in the vicinity of the faint (W2 ≈15.4 mag) and fast-moving brown dwarf CWISEP J1130+3139 (Meisner et al. 2020a). The size of each black plus mark indicates its ±1σ positional uncertainties. The dotted black line represents a linear proper motion fit based on Meisner et al. (2020a). CWISEP J1130+3139 is moving toward the southwest (bottom right) over time. Red lines connect the 16 detections within 1 unWISE pixel (2.″75) of the linear motion trajectory with their corresponding epoch’s location on the linear trajectory. The cluster of detections centered at (Δα, Δδ) ≈ (15″, 2.″5) is a faint background galaxy with W2 = 16.65 mag. The isolated detection near (Δα, Δδ) = (8″, − 6″) is a very low-significance noise detection extracted from a single unWISE epochal coadd image. In this visualization, (Δα, Δδ) = (0, 0) is at (α, δ) = (172.°54118, 31.°66166).
Paper: unTimely: a Full-sky, Time-domain unWISE Catalog
Authors: Aaron M. Meisner, Dan Caselden, Edward F. Schlafly, Frank Kiwy
Figure caption: Six infrared variable quasars drawn from the SDSS DR14 quasar catalog (Pâris et al. 2018). The solid (dashed) line in each panel is the unTimely Catalog W1 (W2) Vega magnitude. The “radio = true” or “radio = false” annotation is determined by whether (true) or not (false) the SDSS DR14 quasar catalog reports a FIRST (Becker et al. 1994) counterpart. A lack of FIRST counterpart could indicate either a FIRST nondetection or a sky region outside the FIRST footprint. We visually inspected each of these quasars using WiseView (Caselden et al. 2018) to validate that the variability shown is not spurious. Note that the unTimely Catalog reports only fluxes, not magnitudes. The unTimely FLUX column’s units are Vega nanomaggies, which are also the native flux units for the unWISE Catalog (see https://catalog.unwise.me/catalogs.html for details).
Paper: unTimely: a Full-sky, Time-domain unWISE Catalog
Authors: Aaron M. Meisner, Dan Caselden, Edward F. Schlafly, Frank Kiwy
Figure caption: All-sky map (Galactic coordinates) of the median number of unTimely Catalog detections per time-resolved unWISE coadd in W2. The analogous plot for W1 looks materially the same, though with number count values scaled up by ∼18% overall. The Galactic plane is clearly visible. The two low-density regions at (l gal, b gal) ≈ (96°, +30°) and (l gal, b gal) ≈ (276°, −30°) are the north and south ecliptic poles, respectively. The unWISE coadd time-slicing rules change near the ecliptic poles (∣β∣ > 80°; Meisner et al. 2018b), resulting in many coadds with large regions of zero coverage, which imprints as a relatively low source density in this visualization.
Paper: unTimely: a Full-sky, Time-domain unWISE Catalog
Authors: Aaron M. Meisner, Dan Caselden, Edward F. Schlafly, Frank Kiwy
Figure caption: Photometric zero-point stability and bright-end scatter, assessed using a sample of bright but unsaturated stars in a set of four unWISE tiles (∼10 square degrees total) encompassing the COSMOS region. (a) W1 (b) W2. Top panels: in both bands, the zero point during the very first WISE sky pass appears to have fluxes consistently ∼2% brighter than in all following sky passes, which remain consistent with one another at the subpercent level. Bottom panels: in both bands, the bright-end scatter is ≲1%.
Paper: unTimely: a Full-sky, Time-domain unWISE Catalog
Authors: Aaron M. Meisner, Dan Caselden, Edward F. Schlafly, Frank Kiwy
Figure caption: Number of W1 coadd epochs per coadd_id astrometric footprint (ecliptic coordinates). Typically, there are 16 coadd epochs available in W1 (black dots). The red-colored ranges of ecliptic longitude have only 15 coadd epochs per band because they were impacted by the 2014 April WISE command timing anomaly. The green-colored ranges of ecliptic longitude have 17 coadd epochs in W1. The purple-colored regions have 18 coadd epochs in W1. The ecliptic poles (∣β∣ > 80°) are blue, indicating >18 available coadd epochs in W1 as a result of the modified unWISE time-slicing rules employed for these regions (Meisner et al. 2018b). Yellow boxes signify coadd_id footprints for which the number of W1 coadd epochs differs from the number of W2 coadd epochs.
Paper: unTimely: a Full-sky, Time-domain unWISE Catalog
Authors: Aaron M. Meisner, Dan Caselden, Edward F. Schlafly, Frank Kiwy
Figure caption: Differential completeness and reliability results for the unTimely Catalog (black plus marks) and for the WISE/NEOWISE Single Exposure (L1b) Source Tables (gray lines). The unTimely differential completeness and reliability are assessed via comparison against the Spitzer S-COSMOS catalog (Sanders et al. 2007), as COSMOS is representative of the sky at low ecliptic latitude and high Galactic latitude. Top left: W1 differential completeness. Top right: W1 differential reliability. Bottom left: W2 differential completeness. Bottom right: W2 differential reliability. Red annotations provide the unTimely 50% completeness and 90% reliability limits in the left and right column panels, respectively. The unTimely curves shown average together the results for all 16 unTimely coadd epochs per band in the COSMOS sky region. The plotted Single Exposure Source Table values average together the per-year curves from 2010–2020, to match unTimely’s temporal extent. The Single Exposure Source Table completeness values were tabulated as a function of W1 mag and W2 mag, but have been plotted in the left column as if W1 = ch1 and W2 = ch2.
Paper: Photometry of the Four Anti-Galactocentric Old Open Clusters: Czernik 30, Berkeley 34, Berkeley 75, and Berkeley 76
Authors: Hyobin Im, Sang Chul Kim, Jaemann Kyeong, Hong Soo Park, Joon Hyeop Lee
Figure caption:  B-band images of the four old open clusters: (a) Czerinik 30, (b) Berkeley 34, (c) Berkeley75, and (d) Berkeley 76. North is up, and east is to the left. The red cross symbols are the centers of the clusters, red circles show the scope of the clusters with the radii determined in this study. The radius for each cluster is shown in Table 2. Other X symbols indicate the centers of the clusters from previous studies (see the text for details).
Paper: Rotation Periods, Inclinations, and Obliquities of Cool Stars Hosting Directly Imaged Substellar Companions: Spin–Orbit Misalignments Are Common
Authors: Brendan P. Bowler, Quang H. Tran, Zhoujian Zhang, Marvin Morgan, Katelyn B. Ashok, Sarah Blunt, Marta L. Bryan, Analis E. Evans, Kyle Franson, Daniel Huber, Vighnesh Nagpal, Ya-Lin Wu, Yifan Zhou
Figure caption: Orbital and rotational geometry relevant for obliquity constraints of stars hosting imaged companions. The observer is looking down from the z-axis. The x–y plane is the plane of the sky, and the star is centered on the origin. The x-axis points north and the y-axis points east. Left: the inclination of the companion’s orbital plane, i  o , intersects the sky along the line of nodes, which is oriented by Ω o  from true north to the ascending node. In this configuration the orbital angular momentum vector ﹩\overrightarrow{{L}_{o}}﹩ points in the hemisphere facing the observer, with ﹩\overrightarrow{{L}_{o}}﹩ pointing toward the observer along the z-axis when i  o  = 0°. Middle: the star’s orientation in space determines the inclination of its equatorial plane, i *, and the position angle of its line of nodes. The sense of the stellar spin sets its rotational angular momentum vector ﹩\overrightarrow{{L}_{* }}﹩ and the longitude of ascending node Ω*. Right: if only the inclinations i  o  and i * are measured, but not their true (or relative) orientations in the plane of the sky (Ω o  and Ω*), then each angular momentum vector can fall anywhere along nested cones opening toward or away from the observer. If Ω o  is known (point A  1 , for example), but not Ω*, then the obliquity can be as small as ∣i  o –i *∣ (at B  1 ), can reach i  o  + i * for prograde orbits (B  2 ), or can be as large as π − ∣i  o  − i *∣ (B  3 ) for retrograde orbits.
Paper: Rotation Periods, Inclinations, and Obliquities of Cool Stars Hosting Directly Imaged Substellar Companions: Spin–Orbit Misalignments Are Common
Authors: Brendan P. Bowler, Quang H. Tran, Zhoujian Zhang, Marvin Morgan, Katelyn B. Ashok, Sarah Blunt, Marta L. Bryan, Analis E. Evans, Kyle Franson, Daniel Huber, Vighnesh Nagpal, Ya-Lin Wu, Yifan Zhou
Figure caption: Line-of-sight stellar inclinations (i *, orange) compared with the orbital inclinations of substellar companions (i  o , blue). In these “boomerang diagrams,” stellar inclinations are mirrored about the x–y sky plane (i = 90°) because angular momentum vectors may point toward or away from the observer looking down along the z-axis (as depicted in the bottom-right panel of Figure 10). If i  o  agrees with i * (e.g., HD 49197), this implies that the host and companion are consistent with spin–orbit alignment; however, the true spin–orbit angle may nevertheless be nonzero in these cases. If i  o  and i * disagree (e.g., Gl 504), the system is misaligned by at least the difference in the inclination angles. See Sections 2 and 4.7 for details.
Paper: Rotation Periods, Inclinations, and Obliquities of Cool Stars Hosting Directly Imaged Substellar Companions: Spin–Orbit Misalignments Are Common
Authors: Brendan P. Bowler, Quang H. Tran, Zhoujian Zhang, Marvin Morgan, Katelyn B. Ashok, Sarah Blunt, Marta L. Bryan, Analis E. Evans, Kyle Franson, Daniel Huber, Vighnesh Nagpal, Ya-Lin Wu, Yifan Zhou
Figure caption: Posterior distributions for line-of-sight stellar inclinations determined in this study. Blue shaded regions show 1σ and 2σ credible intervals. Stars viewed equator-on have inclinations of 90°, while pole-on orientations have inclinations of 0°. Note that the direction of the angular momentum vector in the sky plane is usually unknown, as is its orientation toward or away from the observer. For comparison, an isotropic prior distribution is shown in Figure 8.
Paper: Rotation Periods, Inclinations, and Obliquities of Cool Stars Hosting Directly Imaged Substellar Companions: Spin–Orbit Misalignments Are Common
Authors: Brendan P. Bowler, Quang H. Tran, Zhoujian Zhang, Marvin Morgan, Katelyn B. Ashok, Sarah Blunt, Marta L. Bryan, Analis E. Evans, Kyle Franson, Daniel Huber, Vighnesh Nagpal, Ya-Lin Wu, Yifan Zhou
Figure caption: Comparison between ﹩v\sin {i}_{* }﹩ values from this work and those from the literature for the same star. The dotted line is a 1:1 relationship. Above about 6 km s−1, the agreement is good, but our ﹩v\sin {i}_{* }﹩ values derived through cross correlation with slowly rotating standard stars are generally overestimated for low projected rotational velocities. The bottom panel shows fractional residuals.
Paper: Rotation Periods, Inclinations, and Obliquities of Cool Stars Hosting Directly Imaged Substellar Companions: Spin–Orbit Misalignments Are Common
Authors: Brendan P. Bowler, Quang H. Tran, Zhoujian Zhang, Marvin Morgan, Katelyn B. Ashok, Sarah Blunt, Marta L. Bryan, Analis E. Evans, Kyle Franson, Daniel Huber, Vighnesh Nagpal, Ya-Lin Wu, Yifan Zhou
Figure caption: Gaia G  BP –G  RP  color–magnitude diagram of stars hosting imaged substellar companions in this study. With the exception of 51 Eri, the bluest star in the figure, we focus on cool stars of F5 and later to avoid confusion between stellar rotation and pulsations in our light-curve analysis. Host stars are color-coded by rotation period and range from 0.08–44 days. Long rotation periods are either determined from multiple TESS sectors or are adopted from previous measurements in the literature.
Paper: Rotation Periods, Inclinations, and Obliquities of Cool Stars Hosting Directly Imaged Substellar Companions: Spin–Orbit Misalignments Are Common
Authors: Brendan P. Bowler, Quang H. Tran, Zhoujian Zhang, Marvin Morgan, Katelyn B. Ashok, Sarah Blunt, Marta L. Bryan, Analis E. Evans, Kyle Franson, Daniel Huber, Vighnesh Nagpal, Ya-Lin Wu, Yifan Zhou
Figure caption: Example of a single order from our observations with the Tull high-resolution optical spectrograph (black). Stars are organized by decreasing spectral type from late-B to mid-M. Standard stars are overplotted in blue and have been broadened and shifted to match the measured radial and projected rotational velocities of the substellar host stars in our sample.
Paper: Rotation Periods, Inclinations, and Obliquities of Cool Stars Hosting Directly Imaged Substellar Companions: Spin–Orbit Misalignments Are Common
Authors: Brendan P. Bowler, Quang H. Tran, Zhoujian Zhang, Marvin Morgan, Katelyn B. Ashok, Sarah Blunt, Marta L. Bryan, Analis E. Evans, Kyle Franson, Daniel Huber, Vighnesh Nagpal, Ya-Lin Wu, Yifan Zhou
Figure caption: Overview of the host star spectral types and rotation periods (left), projected rotational velocities (middle), and companion spectral types and separations (right). Systems with stellar obliquity constraints are plotted in dark blue. Companions with obliquity constraints in this study reside within ≈250 au where orbital motion is more readily detectable.
Paper: Rotation Periods, Inclinations, and Obliquities of Cool Stars Hosting Directly Imaged Substellar Companions: Spin–Orbit Misalignments Are Common
Authors: Brendan P. Bowler, Quang H. Tran, Zhoujian Zhang, Marvin Morgan, Katelyn B. Ashok, Sarah Blunt, Marta L. Bryan, Analis E. Evans, Kyle Franson, Daniel Huber, Vighnesh Nagpal, Ya-Lin Wu, Yifan Zhou
Figure caption: Posterior distributions for line-of-sight stellar inclinations. See Figure 6 for details. For comparison, an isotropic prior distribution is shown in the bottom-right panel.
Paper: Rotation Periods, Inclinations, and Obliquities of Cool Stars Hosting Directly Imaged Substellar Companions: Spin–Orbit Misalignments Are Common
Authors: Brendan P. Bowler, Quang H. Tran, Zhoujian Zhang, Marvin Morgan, Katelyn B. Ashok, Sarah Blunt, Marta L. Bryan, Analis E. Evans, Kyle Franson, Daniel Huber, Vighnesh Nagpal, Ya-Lin Wu, Yifan Zhou
Figure caption: Posterior distributions for line-of-sight stellar inclinations. See Figure 6 for details. For comparison, an isotropic prior distribution is shown in Figure 8.