adsabs/AstroCLIMB huggingface dataset:
Paper: The AURORA Survey: Tracing Galactic Outflows at z ≳ 2.5 with JWST/NIRSpec Near-ultraviolet Absorption Lines
Authors: Emily Kehoe, Alice E. Shapley, Ryan L. Sanders, Naveen A. Reddy, Michael W. Topping, Natalie Lam, Leonardo Clarke, Fergus Cullen, Richard S. Ellis, N. M. Förster Schreiber, Tucker Jones, Ali Ahmad Khostovan, Derek J. McLeod, Ross J. McLure, Desika Narayanan, Pascal Oesch, Anthony J. Pahl
Figure caption: EW comparisons of the Mg II low bin (red) and high bin (black) stacks. The white numbers inside each bar are the average property values of each stack. The high bins of the stellar mass, β, and AV have much greater EWs than the low bins, whereas sSFR shows the inverse relationship.
Paper: The AURORA Survey: Tracing Galactic Outflows at z ≳ 2.5 with JWST/NIRSpec Near-ultraviolet Absorption Lines
Authors: Emily Kehoe, Alice E. Shapley, Ryan L. Sanders, Naveen A. Reddy, Michael W. Topping, Natalie Lam, Leonardo Clarke, Fergus Cullen, Richard S. Ellis, N. M. Förster Schreiber, Tucker Jones, Ali Ahmad Khostovan, Derek J. McLeod, Ross J. McLure, Desika Narayanan, Pascal Oesch, Anthony J. Pahl
Figure caption: Close-up Mg II profiles of absorption only (top), both absorption and emission (middle), and emission dominated (bottom). The dashed red lines indicate the rest-frame wavelengths of the Mg II λλ2796 and 2803 lines. The gray shaded region represents the error spectrum.
Paper: The AURORA Survey: Tracing Galactic Outflows at z ≳ 2.5 with JWST/NIRSpec Near-ultraviolet Absorption Lines
Authors: Emily Kehoe, Alice E. Shapley, Ryan L. Sanders, Naveen A. Reddy, Michael W. Topping, Natalie Lam, Leonardo Clarke, Fergus Cullen, Richard S. Ellis, N. M. Förster Schreiber, Tucker Jones, Ali Ahmad Khostovan, Derek J. McLeod, Ross J. McLure, Desika Narayanan, Pascal Oesch, Anthony J. Pahl
Figure caption: Comparisons of Fe II λ2600 (blue spectrum) and Mg II λ2796 (red spectrum) line profiles of the different composite spectra in velocity space. The solid vertical black line marks the systemic velocity, the dotted−dashed blue line marks the location of Fe II λ2587, and the dashed red line marks the location of Mg II λ2803. In most spectra, there is an excess blue wing of the Mg II λ2796 profile compared to the Fe II λ2600 profile.
Paper: The AURORA Survey: Tracing Galactic Outflows at z ≳ 2.5 with JWST/NIRSpec Near-ultraviolet Absorption Lines
Authors: Emily Kehoe, Alice E. Shapley, Ryan L. Sanders, Naveen A. Reddy, Michael W. Topping, Natalie Lam, Leonardo Clarke, Fergus Cullen, Richard S. Ellis, N. M. Förster Schreiber, Tucker Jones, Ali Ahmad Khostovan, Derek J. McLeod, Ross J. McLure, Desika Narayanan, Pascal Oesch, Anthony J. Pahl
Figure caption: Composite spectrum constructed from the 10 galaxies in our sample that exhibit potential Na D absorption, indicated by the orange dashed lines. The nearby He I emission line, located to the blue side of Na D, is marked with a lime-green dashed line.
Paper: α-MC: Self-consistent α-enhanced Stellar Population Models Covering a Wide Range of Age, Metallicity, and Wavelength
Authors: Minjung Park, Charlie Conroy, Benjamin D. Johnson, Joel Leja, Aaron Dotter, Phillip A. Cargile
Figure caption: Separate effects of α-enhancement in isochrones vs. stellar spectra on SSP colors. All panels show the color differences between α-enhanced and solar-scaled models at [Fe/H] = +0.0 (same format as in Figure 8). The α-enhanced models in each column are as follows. (Left) “Self-consistent” α-enhanced models where both isochrones and stellar spectra are α-enhanced. (Middle) Only stellar spectra are α-enhanced, showing the “spectral” effects on the broadband colors. (Right) Only isochrones are α-enhanced, showing the “isochrone” effects. We find that α-enhancement in isochrones and stellar spectra have opposite effects on broadband colors for older stellar populations (>1 Gyr). α-enhancement in isochrones always makes SSPs redder in all colors by lowering the temperature of stars dominating the fluxes. On the other hand, α-enhancement in stellar spectra makes them bluer in bluer bands (u − g, g − r, and B − V).
Paper: α-MC: Self-consistent α-enhanced Stellar Population Models Covering a Wide Range of Age, Metallicity, and Wavelength
Authors: Minjung Park, Charlie Conroy, Benjamin D. Johnson, Joel Leja, Aaron Dotter, Phillip A. Cargile
Figure caption: Differences in broadband SSP colors (u − g, g − r, B − V, and G − K) between α-enhanced models (different colors for varying [α/Fe]) and solar-scaled models at constant [Fe/H] (each [Fe/H] shown in each column). For older stellar population (>1 Gyr), we find that α-enhanced models have redder G − K color (by ∼0.2 mag for [α/Fe] = +0.4) and do not show significant differences in colors at shorter wavelengths. The α-enhancement has significant impacts on the colors for young stellar populations (﹩\mathrm{log}({\rm{age}}/{\rm{yr}})=7\,\unicode{x02013}\,9﹩).
Paper: α-MC: Self-consistent α-enhanced Stellar Population Models Covering a Wide Range of Age, Metallicity, and Wavelength
Authors: Minjung Park, Charlie Conroy, Benjamin D. Johnson, Joel Leja, Aaron Dotter, Phillip A. Cargile
Figure caption: Resolution (R = λ/Δλ) of the C3K v2.3 spectral library implemented in FSPS for different segments of the wavelength range. A very high-resolution spectrum (R ∼ 300,000) for a wavelength range of 0.1–2.5 μm is computed by running a radiative transfer code, SYNTHE. Outside this wavelength range (100–1000 Å and 2.5–30 μm), a low-resolution SED computed by ATLAS is used. The high-resolution spectrum from SYNTHE is then smoothed to different resolutions for more convenient use: R = 3000 for 0.3–1.0 μm. The line list is tuned to the observed high-resolution spectra of the Sun and Arcturus for 4728 Å–1 μm.
Paper: α-MC: Self-consistent α-enhanced Stellar Population Models Covering a Wide Range of Age, Metallicity, and Wavelength
Authors: Minjung Park, Charlie Conroy, Benjamin D. Johnson, Joel Leja, Aaron Dotter, Phillip A. Cargile
Figure caption: Respective α-enhancement effects of isochrones and stellar spectra on SSP spectra. All three panels show the flux ratios between α-enhanced and solar-scaled SSPs at fixed [Fe/H] = –1.0 for 4 Gyr. The same color code is used for different [α/Fe] ratios. The α-enhanced models in each panel are produced as follows. (Left) α-enhanced isochrones and α-enhanced stellar spectra, the “self-consistent” α-enhanced models. (Middle) Solar-scaled isochrones and α-enhanced stellar spectra, showing the “spectral effect.” (Right) α-enhanced isochrones and solar-scaled stellar spectra, showing the “isochrone effect.” We find that the main effect of α-enhancement in isochrones is to lower the overall continuum and change the continuum shapes, whereas α-enhancement in stellar spectra mainly affects the individual spectral lines.
Paper: α-MC: Self-consistent α-enhanced Stellar Population Models Covering a Wide Range of Age, Metallicity, and Wavelength
Authors: Minjung Park, Charlie Conroy, Benjamin D. Johnson, Joel Leja, Aaron Dotter, Phillip A. Cargile
Figure caption: Lick indices as a function of SSP ages. Different colors represent SSPs with different [α/Fe] at constant [Fe/H] = +0.0. All indices are measured from SSP spectra at a resolution of 8.4 Å (FWMH). We find that spectral indices involving α-elements (e.g., Ca4227, Mg b, TiO) increase with [α/Fe], while most iron- and carbon-sensitive indices get weakened with increasing [α/Fe].
Paper: α-MC: Self-consistent α-enhanced Stellar Population Models Covering a Wide Range of Age, Metallicity, and Wavelength
Authors: Minjung Park, Charlie Conroy, Benjamin D. Johnson, Joel Leja, Aaron Dotter, Phillip A. Cargile
Figure caption: Flux ratios between α-enhanced and solar-scaled SSPs at fixed [Fe/H]: [Fe/H] = –1.0 (left) and +0.0 (right). From top to bottom, we show the flux ratios of SSPs for different ages, from 0.1 Gyr to 10 Gyr. For fixed [Fe/H], different color represents different [α/Fe]: –0.2 (blue), +0.2 (green), +0.4 (orange), and +0.6 (brown).
Paper: α-MC: Self-consistent α-enhanced Stellar Population Models Covering a Wide Range of Age, Metallicity, and Wavelength
Authors: Minjung Park, Charlie Conroy, Benjamin D. Johnson, Joel Leja, Aaron Dotter, Phillip A. Cargile
Figure caption: Comparison of SSP full spectra generated using the new MIST v2.5 (blue, used in this work) and previous MIST v1.2 (gray) for different ages. We find a good overall agreement between SSP spectra made with MIST v2.5 and v1.2.
Paper: α-MC: Self-consistent α-enhanced Stellar Population Models Covering a Wide Range of Age, Metallicity, and Wavelength
Authors: Minjung Park, Charlie Conroy, Benjamin D. Johnson, Joel Leja, Aaron Dotter, Phillip A. Cargile
Figure caption: Comparison of the color evolutions of SSP generated with MIST v1.2 (J. Choi et al. 2016; gray) and the new MIST v2.5 (this work, blue) for [Fe/H] = –1.0 (top) and [Fe/H] = +0.0 (bottom) at [α/Fe] = +0.0. We find good overall agreement in SSP colors between MIST v1.2 and MIST v2.5.
Paper: α-MC: Self-consistent α-enhanced Stellar Population Models Covering a Wide Range of Age, Metallicity, and Wavelength
Authors: Minjung Park, Charlie Conroy, Benjamin D. Johnson, Joel Leja, Aaron Dotter, Phillip A. Cargile
Figure caption: SSP spectra with ages of 0.1, 0.3, 1.0, 4.0, and 12 Gyr with fixed [Fe/H] = +0.0 and varying [α/Fe] (shown as different colors).
Paper: Gravitational Redshift for Rapidly Rotating Neutron Stars
Authors: Zsombor Jakab, Sharon M. Morsink
Figure caption: Redshift of ZAM photons as a function of latitude on the oblate stellar surface for the EOS DS(CMF)-1 star (upper panel) and the EOS A star (lower panel) whose properties are given in Table 1. The star’s equator corresponds to ﹩\cos \theta =0﹩ and the spin poles are at ﹩\cos \theta =1﹩. The solid black line is the exact value of the ZAM redshift computed using rns and Equation (8). The dashed yellow line corresponds to the approximation described in Section 4. The solid blue and green curves correspond to the Schwarzschild and Kerr metric approximations, respectively. Quasi-Kerr approximations are shown with the dotted curves. The green dotted curve shows the quasi-Kerr approximation when the correct values of a and q are used. The lower panel shows the quasi-Kerr approximation when overly large values of a and q are used.
Paper: Gravitational Redshift for Rapidly Rotating Neutron Stars
Authors: Zsombor Jakab, Sharon M. Morsink
Figure caption: Light curves (upper panels) for monochromatic light emitted from θ = 41° and observed at ζ = 20°, for stars constructed with EOS L, with M = 1.4M⊙ and spin frequencies of 300 Hz (left) and 600 Hz (right). The exact light curves obtained from ray tracing on the correct metric (C. Cadeau et al. 2007) are shown with solid lines. The original and corrected OS approximations are shown with dotted and dashed–dotted curves. The SD approximation is shown with a dashed–dotted curve with three dots for comparison. The percent differences between the two OS approximations with respect to the exact light curve is shown in the lower panel. Due to the very large errors in the SD approximation, the percent error for SD is not shown for 600 Hz.
Paper: Gravitational Redshift for Rapidly Rotating Neutron Stars
Authors: Zsombor Jakab, Sharon M. Morsink
Figure caption: ZAM redshift correction, χ, (upper panel) and the residual fractional errors (lower panel) in z0 as functions of the dimensionless parameters M/R and ﹩\overline{{\rm{\Omega }}}﹩. The surface represents the function defined by Equation (28) with n = 3 and m = 2. Colored points correspond to the individual calculations for each stellar model in the library.
Paper: Machine Learning Inference of Stellar Properties Using Integrated Photometric and Spectroscopic Data
Authors: Ilay Kamai, Alex M. Bronstein, Hagai B. Perets
Figure caption: UMAP of fast rotators from I. Kamai & H. B. Perets (2025b). Upper left—colors correspond to the luminosity excess from a single-star model (ΔKiso, see I. Kamai & H. B. Perets (2025b) for details). Upper right—color corresponds to peculiar velocity dispersion (σ) from D.-C. Chen et al. (2021). Note that not all samples have a σ value. The black line is a separation line found by a linear SVM classifier and a clustering of the points into two classes. Lower left—σ histograms of the two clusters from the upper right panel. Lower right—Prot histograms of the same clusters.
Paper: Machine Learning Inference of Stellar Properties Using Integrated Photometric and Spectroscopic Data
Authors: Ilay Kamai, Alex M. Bronstein, Hagai B. Perets
Figure caption: Upper panel—few-shot learning of Teff and ﹩{\mathrm{log}}(L/{L}_{\odot })﹩ using linear regression on 20% of the test set. Lower panel—the resulting HR diagram (right) and true HR diagram (left). Colors proportional to the MAE.
Paper: Machine Learning Inference of Stellar Properties Using Integrated Photometric and Spectroscopic Data
Authors: Ilay Kamai, Alex M. Bronstein, Hagai B. Perets
Figure caption: UMAP of DESA final embeddings. All points are in gray, and points with RUWE > 1.4 are colored according to their RUWE value.
Paper: Machine Learning Inference of Stellar Properties Using Integrated Photometric and Spectroscopic Data
Authors: Ilay Kamai, Alex M. Bronstein, Hagai B. Perets
Figure caption: Feature sensitivity study. The x-axis shows the feature index in the final features of DESA on the test set. The y-axis shows the difference between the original features and features that were generated with only spectral input (red) or only light-curve input (gray). The blue circles mark indices where the light-curve-only features are closer to the original features compared to the spectra-only features.
Paper: Machine Learning Inference of Stellar Properties Using Integrated Photometric and Spectroscopic Data
Authors: Ilay Kamai, Alex M. Bronstein, Hagai B. Perets
Figure caption: UMAP of all models. All points are in shaded gray, and 10 stars with pairs of spectra are colored on top. For each pair, all samples are colored with the same color and connected by a line. We can see the visual difference between DESA (upper figure) and all other models—in DESA, pairs arrange very close to each other, implying that they have very similar embeddings. In other models, there are samples of the same star that are very distant, which implies very different embeddings.
Paper: Machine Learning Inference of Stellar Properties Using Integrated Photometric and Spectroscopic Data
Authors: Ilay Kamai, Alex M. Bronstein, Hagai B. Perets
Figure caption: Experimental results of binary detection for different models. The left panel shows TNR vs. FNR. The right panel shows precision–recall curves.
Paper: Machine Learning Inference of Stellar Properties Using Integrated Photometric and Spectroscopic Data
Authors: Ilay Kamai, Alex M. Bronstein, Hagai B. Perets
Figure caption: Confusion matrix of alternative models on a binary prediction task.
Paper: Machine Learning Inference of Stellar Properties Using Integrated Photometric and Spectroscopic Data
Authors: Ilay Kamai, Alex M. Bronstein, Hagai B. Perets
Figure caption: Confusion matrix of our model on the binary detection task.
Paper: Machine Learning Inference of Stellar Properties Using Integrated Photometric and Spectroscopic Data
Authors: Ilay Kamai, Alex M. Bronstein, Hagai B. Perets
Figure caption: Box plots of MAE vs. SNR for the APOGEE test set and the LAMOST test set.