adsabs/AstroCLIMB huggingface dataset:
Paper: Stringent Upper Bounds on Atmospheric Mass Loss from Three Neptune-sized Planets in the TOI-4010 System
Authors: Morgan Saidel, Shreyas Vissapragada, Michael Zhang, Heather A. Knutson, Matthäus Schulik, Jorge Fernández Fernández, Michelle Kunimoto, Peter J. Wheatley, Jessica Spake
Figure caption: Average Keck/NIRSPEC excess absorption spectra in percent for TOI-4010 b (first panel), TOI-4010 c (second panel), and TOI-4010 d (third panel) shown as black points. Red shaded regions mark the location of a telluric OH line that we masked in our analysis (see Figures 2 and 3). pyTPCI model predictions for the He* excess absorption signal as a function of planetary atmospheric metallicity are shown as purple (Z = 1), blue (Z = 10), green (Z = 30), and yellow (Z = 100) lines.
Paper: Stringent Upper Bounds on Atmospheric Mass Loss from Three Neptune-sized Planets in the TOI-4010 System
Authors: Morgan Saidel, Shreyas Vissapragada, Michael Zhang, Heather A. Knutson, Matthäus Schulik, Jorge Fernández Fernández, Michelle Kunimoto, Peter J. Wheatley, Jessica Spake
Figure caption: Keck/NIRSPEC average excess absorption spectra in percent for TOI-4010 b (first panel), TOI-4010 c (second panel), and TOI-4010 d (third panel) shown as black points. The shaded red region marks the location of a telluric OH line masked in our analysis (see Figures 2 and 3). AIOLOS model predictions for the He* excess absorption signal are shown as green lines.
Paper: Stringent Upper Bounds on Atmospheric Mass Loss from Three Neptune-sized Planets in the TOI-4010 System
Authors: Morgan Saidel, Shreyas Vissapragada, Michael Zhang, Heather A. Knutson, Matthäus Schulik, Jorge Fernández Fernández, Michelle Kunimoto, Peter J. Wheatley, Jessica Spake
Figure caption: Top panels: evolution of the radius (left panel) and envelope mass fraction (right panel) for TOI-4010 c, as described in Section 5.5. The solid lines show atmospheric evolution under different XUV irradiation histories and different values for the planet’s parameters within their 1σ uncertainty. The planet’s present-day location on each panel is plotted as a red circle. Bottom panels: evolution of the radius and envelope mass fraction for TOI-4010 d, following the top panels.
Paper: Physical Properties of Gas Departing from Circular Rotation at 50 pc Scales Using the PHANGS-MUSE Galaxies
Authors: Carlos López-Cobá, Lihwai Lin, Irene Cruz Gonzalez, Sebastián F. Sánchez, Hsi-An Pan, J. K. Barrera-Ballesteros, Bau-Ching Hsieh
Figure caption: Moment maps extracted from the observed (Iobs) and model (Imodel, e.g., Equation (5)) cubes. The white patches show the blanked regions from the automated 3D masking procedure. Each column shows, from left to right, the observed moment map, the model, and the residual map (observed minus model), respectively. Each row from top to bottom shows the intensity-weighted map, the velocity-weighted, and the dispersion-weighted (LSF and PSF affected) map. The black ellipse in each panel represents the best-fit geometry for the disk, while the central black cross shows the major and minor axes kinematic position angles. The PSF/beam size is shown at the bottom right of each panel. The top-left inset shows an Hα spectrum taken at an arbitrary position; the observation is shown in black, the model in orange, and the residual in red. The vertical dashed lines show the instrumental and intrinsic dispersion in black and orange colors, respectively. The flux units are 10−16 erg s−1 cm−2 Å−1.
Paper: Physical Properties of Gas Departing from Circular Rotation at 50 pc Scales Using the PHANGS-MUSE Galaxies
Authors: Carlos López-Cobá, Lihwai Lin, Irene Cruz Gonzalez, Sebastián F. Sánchez, Hsi-An Pan, J. K. Barrera-Ballesteros, Bau-Ching Hsieh
Figure caption: The 3D visualization and environmental masking applied to various physical properties. From top to bottom, each panel shows the continuum, ionized gas, and residual velocity maps, respectively. From left to right, different environmental masks are applied, where masked (i.e., blanked) regions are shown by the desaturated areas, and the unmasked region is indicated at the top. The first column represents the original map without masking. The relief in each panel represents the intensity in the MUSE r band. The RGB continuum image was constructed from the MUSE gri bands. The RGB emission-line image shows the [N II], Hα, and [O III] fluxes, respectively. The residual velocity map shows the noncircular motions, in  km s−1, after decoupling the circular rotation.
Paper: Physical Properties of Gas Departing from Circular Rotation at 50 pc Scales Using the PHANGS-MUSE Galaxies
Authors: Carlos López-Cobá, Lihwai Lin, Irene Cruz Gonzalez, Sebastián F. Sánchez, Hsi-An Pan, J. K. Barrera-Ballesteros, Bau-Ching Hsieh
Figure caption: Example of the spectral fitting in one object from the PHANGS-MUSE sample. Left panel: Voronoi map of NGC 4303. The color bar indicates the bin number, each comprising one or more spaxels. Right panel: implementation of the single stellar population analysis to an arbitrary spectrum within the galaxy. The original spectrum is shown in dark blue while the best SSP model is shown in pale blue; the gas spectrum (free of stellar continuum) is shown in gray, the emission-line model in red, and the residuals in dark red. Additionally, a zoom-in around the Hα line is shown for better appreciation.
Paper: Physical Properties of Gas Departing from Circular Rotation at 50 pc Scales Using the PHANGS-MUSE Galaxies
Authors: Carlos López-Cobá, Lihwai Lin, Irene Cruz Gonzalez, Sebastián F. Sánchez, Hsi-An Pan, J. K. Barrera-Ballesteros, Bau-Ching Hsieh
Figure caption: XS3D results for the Hα disk geometries for the PHANGS-MUSE objects compared to other works. Top figures from left to right show the position angle ϕ derived from XS3D on the y-axis compared to S4G (e.g., H. Salo et al. 2015), molecular gas CO from PHANGS-ALMA (e.g., P. Lang et al. 2020, their Table (2)), and from 3DBarolo (see Appendix C), respectively. Bottom figures show similar comparisons but for the disk inclination angle, with XS3D results on the y-axis. The straight line represents the 1:1 relation while box plots of the difference Δ = y − x are shown in the upper-left corner of each panel, with the mean and standard deviation indicated.
Paper: Physical Properties of Gas Departing from Circular Rotation at 50 pc Scales Using the PHANGS-MUSE Galaxies
Authors: Carlos López-Cobá, Lihwai Lin, Irene Cruz Gonzalez, Sebastián F. Sánchez, Hsi-An Pan, J. K. Barrera-Ballesteros, Bau-Ching Hsieh
Figure caption: Hα intrinsic dispersion and rotation curves of the PHANGS-MUSE galaxies. The color of the lines indicates the stellar mass of each galaxy, with reddish tones representing the most massive and bluish tones the least massive objects. Bottom panel: azimuthally averaged radial profiles of NC motions, as defined in Equation (9). The stellar rotation curves were derived from the stellar velocity maps.
Paper: Physical Properties of Gas Departing from Circular Rotation at 50 pc Scales Using the PHANGS-MUSE Galaxies
Authors: Carlos López-Cobá, Lihwai Lin, Irene Cruz Gonzalez, Sebastián F. Sánchez, Hsi-An Pan, J. K. Barrera-Ballesteros, Bau-Ching Hsieh
Figure caption: Physical properties explored, segregated by different environments in NGC 1566. Each panel shows the distribution of a given physical property across different galactic environments: center, bar, interarm region, and spiral arms. From left to right and from top to bottom: deprojected noncircular amplitude, LSF-corrected Hα LOS dispersion, negative perpendicular distance to the K01 curve (SF ionization), positive distance to the K01 curve (AGN-like ionization), equivalent width of Hα, specific star formation rate, star formation surface density, and electron density. Each box plot represents the distribution of spaxels within each environment. If a box plot for a given environment is not shown, it indicates that either there are no spaxels within that mask or the spaxels do not satisfy the required condition. Units are shown in the upper left of each panel.
Paper: Physical Properties of Gas Departing from Circular Rotation at 50 pc Scales Using the PHANGS-MUSE Galaxies
Authors: Carlos López-Cobá, Lihwai Lin, Irene Cruz Gonzalez, Sebastián F. Sánchez, Hsi-An Pan, J. K. Barrera-Ballesteros, Bau-Ching Hsieh
Figure caption: Relationship between the amplitude of noncircular (NC) motions and ionized gas properties across different galactic environments in the PHANGS-MUSE sample. Left: line-of-sight (LOS) LSF-corrected Hα velocity dispersion. Right: positive offset from the L. J. Kewley et al. (2001) demarcation, indicating AGN-like ionization. Each point represents the median value within each environment, and error bars indicate the standard deviation within each environment. The relationship is characterized by the Pearson correlation coefficient shown in the upper-right corner of each panel.
Paper: Physical Properties of Gas Departing from Circular Rotation at 50 pc Scales Using the PHANGS-MUSE Galaxies
Authors: Carlos López-Cobá, Lihwai Lin, Irene Cruz Gonzalez, Sebastián F. Sánchez, Hsi-An Pan, J. K. Barrera-Ballesteros, Bau-Ching Hsieh
Figure caption: Similar plots as in Figure 6, but this time including the 19 objects from PHANGS-MUSE. Each panel comprises ∼2.6 × 106 spaxels spanning the five different environments.
Paper: Physical Properties of Gas Departing from Circular Rotation at 50 pc Scales Using the PHANGS-MUSE Galaxies
Authors: Carlos López-Cobá, Lihwai Lin, Irene Cruz Gonzalez, Sebastián F. Sánchez, Hsi-An Pan, J. K. Barrera-Ballesteros, Bau-Ching Hsieh
Figure caption: Comparison of Vt(r) and σintrin(r) between XS3D (blue dots) and 3DBarolo (black dots) for a subsample of objects from PHANGS-MUSE. Top panels show the Hα rotational curves, while the bottom panels show the intrinsic velocity dispersion profiles. The box plots represent the differences between 3DBarolo and XS3D (i.e., Δ = XS3D − 3DBarolo), with the mean and standard deviation of the distribution indicated.
Paper: Physical Properties of Gas Departing from Circular Rotation at 50 pc Scales Using the PHANGS-MUSE Galaxies
Authors: Carlos López-Cobá, Lihwai Lin, Irene Cruz Gonzalez, Sebastián F. Sánchez, Hsi-An Pan, J. K. Barrera-Ballesteros, Bau-Ching Hsieh
Figure caption: Global comparison of the 19 rotational curves and intrinsic dispersion profiles derived with XS3D and 3DBarolo. Left panel: rotational curves,with values derived using XS3D on the y-axis and 3DBarolo on the x-axis. Right panel: comparison of the average intrinsic velocity dispersion profiles. In both panels the solid line represents the 1-to-1 relation, while the box plot indicates the relative error with respect to 3DBarolo (i.e., (XS3D-3DBarolo)/3DBarolo).
Paper: Physical Properties of Gas Departing from Circular Rotation at 50 pc Scales Using the PHANGS-MUSE Galaxies
Authors: Carlos López-Cobá, Lihwai Lin, Irene Cruz Gonzalez, Sebastián F. Sánchez, Hsi-An Pan, J. K. Barrera-Ballesteros, Bau-Ching Hsieh
Figure caption: Spaxel-based comparison of the Hα velocity dispersion maps (second moments) from 3DBarolo and pyPipe3D with those from XS3D. Each histogram (in log scale) shows, from left to right, the relative error with respect to 3DBarolo for the observed and model maps, while the third panel shows the relative error with respect to the pyPipe3D dispersion maps. Box plots are shown below, with the median and standard deviation indicated in the upper right. Each histogram comprises approximately 2.5 × 106 spaxels.
Paper: Physical Properties of Gas Departing from Circular Rotation at 50 pc Scales Using the PHANGS-MUSE Galaxies
Authors: Carlos López-Cobá, Lihwai Lin, Irene Cruz Gonzalez, Sebastián F. Sánchez, Hsi-An Pan, J. K. Barrera-Ballesteros, Bau-Ching Hsieh
Figure caption: Similar to Figure 14, but comparing the observed dispersion maps from 3DBarolo and pyPipe3D. Relative errors are computed with respect to pyPipe3D.
Paper: A Wavelength-aware Unsupervised Learning Approach for Large, Multicolor, Photometric Surveys
Authors: Bradley D. Hutchinson, Catherine A. Pilachowski, Christian I. Johnson
Figure caption: SED shapes of randomly chosen stars from five different stellar types matched from SIMBAD across different photometric bands, highlighting the wavelength-dependent structure of the data. The SED shape is normalized to g for all bands. For example, the figure shows that the white dwarf is brightest in g (SED peaked in or before g), while the brown dwarf is brightest in y (SED peaked in or beyond y).
Paper: A Wavelength-aware Unsupervised Learning Approach for Large, Multicolor, Photometric Surveys
Authors: Bradley D. Hutchinson, Catherine A. Pilachowski, Christian I. Johnson
Figure caption: Differences between the reconstructed and observed magnitudes are shown as a function of uncertainty on the PS1 measurement for all five filter passbands considered here. Each sequential band is shifted vertically by a value of one-half for display purposes. Standard deviations of the differences are shown in bins of 0.01 in uncertainty, showing the value on the plot at the bin center. For example, the standard deviation in the difference for all stars with an uncertainty between 0.01 and 0.02 is shown at 0.015.
Paper: A Wavelength-aware Unsupervised Learning Approach for Large, Multicolor, Photometric Surveys
Authors: Bradley D. Hutchinson, Catherine A. Pilachowski, Christian I. Johnson
Figure caption: Differences between the reconstructed and observed magnitudes are shown as a function of observed magnitude for all five filter passbands considered here. Each sequential band is shifted vertically by a value of one-half for display purposes. Standard deviations of the differences are shown in bins of 1 mag, showing the value on the plot at the bin center. For example, the standard deviation in the difference for all stars with a magnitude between 16 and 17 is shown at 16.5.
Paper: A Wavelength-aware Unsupervised Learning Approach for Large, Multicolor, Photometric Surveys
Authors: Bradley D. Hutchinson, Catherine A. Pilachowski, Christian I. Johnson
Figure caption: Uncertainties on PS1 measurement of magnitudes are shown as a function of observed magnitude for all five filter passbands considered here. Each sequential band is shifted vertically by a value of one-tenth for display purposes.
Paper: A Wavelength-aware Unsupervised Learning Approach for Large, Multicolor, Photometric Surveys
Authors: Bradley D. Hutchinson, Catherine A. Pilachowski, Christian I. Johnson
Figure caption: These panels show the latent space colored by all five photometric bands and one color index, displaying clear gradients in apparent brightness and color.
Paper: A Wavelength-aware Unsupervised Learning Approach for Large, Multicolor, Photometric Surveys
Authors: Bradley D. Hutchinson, Catherine A. Pilachowski, Christian I. Johnson
Figure caption: Loss vs. epoch curves are shown for the five LSTM-AE runs in color. Five runs of a standard autoencoder (AE) with the same architecture (excluding LSTM units) and hyperparameters are shown in black, highlighting the superior performance of an LSTM-AE over an AE.
Paper: A Wavelength-aware Unsupervised Learning Approach for Large, Multicolor, Photometric Surveys
Authors: Bradley D. Hutchinson, Catherine A. Pilachowski, Christian I. Johnson
Figure caption: Latent spaces are shown for the five LSTM-AE runs, highlighting the different latent spaces produced by different runs, and how Run 5 is the easiest to interpret due to its clear global structure.
Paper: A Wavelength-aware Unsupervised Learning Approach for Large, Multicolor, Photometric Surveys
Authors: Bradley D. Hutchinson, Catherine A. Pilachowski, Christian I. Johnson
Figure caption: HR diagrams of both PS1 observed magnitudes (left) and reconstructed magnitudes from LSTM-AE (right).
Paper: A Wavelength-aware Unsupervised Learning Approach for Large, Multicolor, Photometric Surveys
Authors: Bradley D. Hutchinson, Catherine A. Pilachowski, Christian I. Johnson
Figure caption: The latent space of all stars is shown in hexbin plots with cmap Grays, and overlapped with stars within 0.15 of a degree of the centers of five Galactic GCs, with no membership cuts being made.
Paper: A Wavelength-aware Unsupervised Learning Approach for Large, Multicolor, Photometric Surveys
Authors: Bradley D. Hutchinson, Catherine A. Pilachowski, Christian I. Johnson
Figure caption: Histograms of the PS1 observed color in solid gray and the LSTM-AE reconstructed color in dashed red.
Paper: A Wavelength-aware Unsupervised Learning Approach for Large, Multicolor, Photometric Surveys
Authors: Bradley D. Hutchinson, Catherine A. Pilachowski, Christian I. Johnson
Figure caption:                         PS1 images (with the bands used to color each image given on the lower left of each image in white) are shown alongside observed photometry, highlighting the SED shapes of detected anomalies. Observed SED shapes are in black, reconstructed SED shapes are in red. Anomalies here are defined as stars with ﹩| m-{m}^{{\prime} }| \,\geqslant \,0.15﹩. A manually selected subset is shown to highlight the diverse SED shapes detected via poor LSTM-AE reconstruction. Image cutouts are 0﹩\mathop{.}\limits^{^{\prime\prime} }﹩25 per pixel with a size of 240 pixels. All anomalies are shown in Figures 21 and 22 of the Appendix.
Paper: A Wavelength-aware Unsupervised Learning Approach for Large, Multicolor, Photometric Surveys
Authors: Bradley D. Hutchinson, Catherine A. Pilachowski, Christian I. Johnson
Figure caption: SED shapes and LSTM-AE’s prediction accuracy metric, ﹩m-{m}^{{\prime} }﹩, of both cool and dim (1.3 ≤ g − r ≤ 1.5, 18 ≤ Mg ≤ 19) and hot and bright (−0.6 ≤ g − r ≤ −0.4, 3 ≤ Mg ≤ 5) stars from the Gaia anomaly subset, showcasing that both types can be separated solely by using ﹩m-{m}^{{\prime} }﹩.
Paper: A Wavelength-aware Unsupervised Learning Approach for Large, Multicolor, Photometric Surveys
Authors: Bradley D. Hutchinson, Catherine A. Pilachowski, Christian I. Johnson
Figure caption: For a given object type (OType) from SIMBAD that has at least five stars, the percentage of stars in that OType that are detected as anomalies (﹩| m-{m}^{{\prime} }| \gt 0.05﹩) is shown. SIMBAD designations “Star” and “GlobCluster” are not included, and the symbol “*” refers to star, with “**” meaning double or multiple stars.
Paper: A Wavelength-aware Unsupervised Learning Approach for Large, Multicolor, Photometric Surveys
Authors: Bradley D. Hutchinson, Catherine A. Pilachowski, Christian I. Johnson
Figure caption: The latent space of all stars is shown in hexbin plots with cmap Grays. The various colored symbols indicate the locations in latent space for stars with various labels determined by matching with SIMBAD. SIMBAD labels are categorized into Compact Object, Main Sequence, He Core, RGB, and Post RGB, with plots of the subtypes also shown.
Paper: A Wavelength-aware Unsupervised Learning Approach for Large, Multicolor, Photometric Surveys
Authors: Bradley D. Hutchinson, Catherine A. Pilachowski, Christian I. Johnson
Figure caption: The latent space of all stars in the Gaia subset is shown in hexbin plots with cmap Grays. In addition, stars matched with Gaia parallaxes are overlapped and colored by distance d, in kiloparsec ranges, highlighting population changes as distance increases.
Paper: A Wavelength-aware Unsupervised Learning Approach for Large, Multicolor, Photometric Surveys
Authors: Bradley D. Hutchinson, Catherine A. Pilachowski, Christian I. Johnson
Figure caption: Color–color plot comparison showing all stars in hexbin plots with cmap Grays along with anomalous stars in hexbin plots with cmap Blues, highlighting the detection of several distinct stellar populations. Anomalous here is defined as any star in which ﹩| m-{m}^{{\prime} }| \gt 0.05﹩.
Paper: A Wavelength-aware Unsupervised Learning Approach for Large, Multicolor, Photometric Surveys
Authors: Bradley D. Hutchinson, Catherine A. Pilachowski, Christian I. Johnson
Figure caption: The latent space of all stars in the Gaia subset is shown in hexbin plots with cmap Grays. In addition, stars matched with Gaia parallaxes are overlapped and colored by absolute magnitude, Mg.
Paper: A Wavelength-aware Unsupervised Learning Approach for Large, Multicolor, Photometric Surveys
Authors: Bradley D. Hutchinson, Catherine A. Pilachowski, Christian I. Johnson
Figure caption: HR diagram of detected anomalies, ﹩| m-{m}^{{\prime} }| \gt 0.05﹩, and extreme anomalies, ﹩| m-{m}^{{\prime} }| \,\geqslant \,0.1﹩, highlighting that a majority of the detected anomalies in the Gaia subset are not in the SIMBAD database and are unlikely to have a classification.
Paper: A Wavelength-aware Unsupervised Learning Approach for Large, Multicolor, Photometric Surveys
Authors: Bradley D. Hutchinson, Catherine A. Pilachowski, Christian I. Johnson
Figure caption: The latent space of all stars is shown in a hexbin plot with cmap Grays. Stars within 0﹩\mathop{.}\limits^{\unicode{x000b0}}﹩05 of Leo II are shown in red, highlighting the RGB stars of Leo II.
Paper: A Wavelength-aware Unsupervised Learning Approach for Large, Multicolor, Photometric Surveys
Authors: Bradley D. Hutchinson, Catherine A. Pilachowski, Christian I. Johnson
Figure caption: PS1 images alongside observed photometry highlighting SED shapes of detected anomalies in which the LSTM-AE overestimates the observed PS1 magnitude (﹩m-{m}^{{\prime} }\,\geqslant \,0.15﹩). Observed SED shapes are in black, reconstructed SED shapes are in red. Image cutouts are 0﹩\mathop{.}\limits^{\unicode{x02033}}﹩25 per pixel with a size of 240 pixels.
Paper: A Wavelength-aware Unsupervised Learning Approach for Large, Multicolor, Photometric Surveys
Authors: Bradley D. Hutchinson, Catherine A. Pilachowski, Christian I. Johnson
Figure caption: SED shapes and LSTM-AE’s prediction accuracy metric, ﹩m-{m}^{{\prime} }﹩, of a selected group of MS stars (0.6 ≤ g − r ≤ 0.9, 7 ≤ Mg ≤ 8), showing how in a region of similar colors, one can use ﹩m-{m}^{{\prime} }﹩ to separate the populations.
Paper: A Wavelength-aware Unsupervised Learning Approach for Large, Multicolor, Photometric Surveys
Authors: Bradley D. Hutchinson, Catherine A. Pilachowski, Christian I. Johnson
Figure caption: PS1 images alongside observed photometry highlighting SED shapes of detected anomalies in which the LSTM-AE underestimates the observed PS1 magnitude (﹩m-{m}^{{\prime} }\leqslant -0.15﹩). Observed SED shapes are in black, reconstructed SED shapes are in red. Image cutouts are 0﹩\mathop{.}\limits^{^{\prime\prime} }﹩25 per pixel with a size of 240 pixels.
Paper: Primordial Black Holes as Coma Cluster Dark Matter and the Unresolved γ-Ray Background
Authors: Jeremy Mould, Bhashin Thakore
Figure caption: Coma’s high energy spectral energy distribution. Top left: contours of dark matter density. The outer contour is 0.0002 gm cm−3 and the inner area >10 times that. Top right: the output flux as a function of the clustercentric radius. The dashed curve shows the loss due to pair production at 5 GeV. Bottom right: Open circles are measurements that seem discordant. The solid line is from Planck’s Model D. Bottom left: the predicted GeV emission with the observation of V. Baghmanyan et al. (2022) shown as an error bar with extended energy coverage. The downward slope of the prediction with decreasing energy reflects the assumed PBH IMF. The cutoff just above 5 GeV is due to exhaustion of lower mass PBH, whose mass loss rate dm/dt is proportional to m−2.
Paper: Primordial Black Holes as Coma Cluster Dark Matter and the Unresolved γ-Ray Background
Authors: Jeremy Mould, Bhashin Thakore
Figure caption: The normalised redshift distribution n(z) of the DES Y3 source galaxies, spanning a weak lensing area of 4143 deg2, with a total of 100 million galaxies. The data for the redshift distribution is taken from the publicly available DES database: https://des.ncsa.illinois.edu/releases/y3a2/Y3key-catalogs. The calculation of the redshift bin centres has been detailed as〈z1〉 = 0.339, 〈z2〉 = 0.528, 〈z3〉 = 0.752, and〈z4〉 = 0.952.