adsabs/AstroCLIMB huggingface dataset:
Paper: Cross-correlating Dark Sirens and Galaxies: Constraints on H 0 from GWTC-3 of LIGO–Virgo–KAGRA
Authors: Suvodip Mukherjee, Alex Krolewski, Benjamin D. Wandelt, Joseph Silk
Figure caption: The joint constraints on H 0 = 100h 0 km s−1 Mpc−1, Ω m , and ﹩{b}_{\mathrm{GW}}{(z)={b}_{\mathrm{GW}}(1+z)}^{\alpha }﹩ for different choices of bin width Δl, ﹩{z}_{\max }﹩, and Δz.
Paper: Cross-correlating Dark Sirens and Galaxies: Constraints on H 0 from GWTC-3 of LIGO–Virgo–KAGRA
Authors: Suvodip Mukherjee, Alex Krolewski, Benjamin D. Wandelt, Joseph Silk
Figure caption: The redshift distribution of the 2MPZ (in orange) and WSC (in blue) galaxies. The shaded region shows the redshift range that is shot-noise-dominated.
Paper: Cross-correlating Dark Sirens and Galaxies: Constraints on H 0 from GWTC-3 of LIGO–Virgo–KAGRA
Authors: Suvodip Mukherjee, Alex Krolewski, Benjamin D. Wandelt, Joseph Silk
Figure caption: The constraints on the value of H 0 obtained by cross-correlating with a random galaxy catalog having no spatial correlation and with a galaxy distribution n(z) matching 2MPZ and WSC.
Paper: Cross-correlating Dark Sirens and Galaxies: Constraints on H 0 from GWTC-3 of LIGO–Virgo–KAGRA
Authors: Suvodip Mukherjee, Alex Krolewski, Benjamin D. Wandelt, Joseph Silk
Figure caption: The sky map along with the mask (gray) in equatorial coordinates of 2MPZ (top) and WSC (bottom).
Paper: Cross-correlating Dark Sirens and Galaxies: Constraints on H 0 from GWTC-3 of LIGO–Virgo–KAGRA
Authors: Suvodip Mukherjee, Alex Krolewski, Benjamin D. Wandelt, Joseph Silk
Figure caption: The sky map in equatorial coordinates of the eight selected GW sources from GWTC-3 (top) and the GW sky mask (bottom) used in the analysis.
Paper: Testing the Accuracy of Spectral Energy Distribution Modeling Techniques Using the NIHAO-SKIRT-Catalog
Authors: Nicholas Faucher, Michael R. Blanton
Figure caption:  χ 2 values for UV–IR fits summed over UV–IR fluxes (top), UV–optical fits summed over UV–optical fluxes (middle), and UV–IR fits summed over UV–optical fluxes (bottom), averaged over all high-mass (>109.5 M ⊙) galaxies (large points) and low-mass (<109.5 M ⊙) galaxies (small points) for each dust model and modeling method. Red points correspond to edge-on galaxies, and blue points to face-on galaxies from left to right we show fits that have fixed SFHs, fixed sSFHs, fixed masses, and free SFHs. The horizontal lines are located at the number of bands being summed over in each χ 2 calculation and act only as a visual guide for assessing the goodness of fit.
Paper: Testing the Accuracy of Spectral Energy Distribution Modeling Techniques Using the NIHAO-SKIRT-Catalog
Authors: Nicholas Faucher, Michael R. Blanton
Figure caption: Distributions of inferred SFR residuals as a function of the true SFRs using UV–IR filters with total stellar masses fixed to their true values. Face-on orientations are denoted by circles and edge-on orientations by squares, with the colors indicating their axis ratios as shown in Figure 1.
Paper: Testing the Accuracy of Spectral Energy Distribution Modeling Techniques Using the NIHAO-SKIRT-Catalog
Authors: Nicholas Faucher, Michael R. Blanton
Figure caption: Simulated galaxy observed axis ratios as a function of the total stellar mass (left) and SFR (right) for face-on and edge-on orientations. Each point is colored by its axis ratio from 0.1 (red) to 1 (blue). This color scheme will be used throughout the rest of this work.
Paper: Testing the Accuracy of Spectral Energy Distribution Modeling Techniques Using the NIHAO-SKIRT-Catalog
Authors: Nicholas Faucher, Michael R. Blanton
Figure caption: Similar to Figure 7 but fit to UV–optical filters.
Paper: Testing the Accuracy of Spectral Energy Distribution Modeling Techniques Using the NIHAO-SKIRT-Catalog
Authors: Nicholas Faucher, Michael R. Blanton
Figure caption: Distributions of inferred mass residuals as a function of the true masses for each dust model using UV–IR filters with sSFHs fixed to their true values. Face-on orientations are denoted by circles and edge-on orientations by squares, with the colors indicating their axis ratios as shown in Figure 1.
Paper: Testing the Accuracy of Spectral Energy Distribution Modeling Techniques Using the NIHAO-SKIRT-Catalog
Authors: Nicholas Faucher, Michael R. Blanton
Figure caption: Fits of the face-on orientation of a low-stellar-mass (left) and high-stellar-mass (right) galaxy using the Cardelli dust attenuation model, and UV–IR (top) and UV–optical (bottom) bands with all SFH parameters free. Mock photometry is shown in black and fitted photometry in green. Error bars that extend to the bottom of the plot represent fluxes that are consistent with zero up to one sigma.
Paper: Testing the Accuracy of Spectral Energy Distribution Modeling Techniques Using the NIHAO-SKIRT-Catalog
Authors: Nicholas Faucher, Michael R. Blanton
Figure caption: Distributions of inferred mass residuals as a function of the true masses using UV–IR filters with all SFH parameters free. Face-on orientations are denoted by circles and edge-on orientations by squares, with the colors indicating their axis ratios as shown in Figure 1.
Paper: Testing the Accuracy of Spectral Energy Distribution Modeling Techniques Using the NIHAO-SKIRT-Catalog
Authors: Nicholas Faucher, Michael R. Blanton
Figure caption: Distribution of DustPedia galaxies’ inferred masses and distances (gray), along with the average binned ﹩{\mathrm{log}}_{10}(\mathrm{mass})﹩, and ﹩{\mathrm{log}}_{10}(\mathrm{distance})﹩ in green, and the line fitted to the average binned points. The equation for the fitted line is provided in the legend.
Paper: Testing the Accuracy of Spectral Energy Distribution Modeling Techniques Using the NIHAO-SKIRT-Catalog
Authors: Nicholas Faucher, Michael R. Blanton
Figure caption: Similar to Figure 5 but fit to UV–optical filters. Under these conditions, the power-law dust attenuation model was unable to converge in a reasonable amount of time.
Paper: Forecasts and Statistical Insights for Line Intensity Mapping Cross-correlations: A Case Study with 21 cm × [C II]
Authors: Hannah Fronenberg, Adrian Liu
Figure caption: Forecasted power-spectrum measurement at z = 6 (left), z = 7 (middle), and z = 8 (right) for the HERA × CCAT-Prime scenario described in Section 7.1, where foreground-contaminated wedge modes are cut. Top: distributions of power-spectrum estimates from our Monte Carlo ensemble, with violins showing the shapes of the distributions, the boxes giving 25th and 75th percentiles, the white bar giving the 50th percentile, and the whiskers showing the full extent of the distributions. Black lines indicate the true inputted power spectra. Note that the vertical axis is a hybrid scale that is linear for ∣P(k)∣ ≤ 106 μK Mpc3 and logarithmic otherwise. The cumulative signal-to-noise ratio, computed using Equation (24), is also indicated. The z = 6 sensitivity is limited by thermal noise, whereas the z = 7 and z = 8 measurements are more severely limited by a CO (2–1) interloper. Bottom: ﹩W(k,{k}^{{\prime} })﹩ as a function of ﹩{k}^{{\prime} }﹩ for each power-spectrum estimate in matching colors. Stars indicate the k-bin center used in power-spectrum estimation. The nontrivial shapes of the P(k) distributions and the window functions showcase the importance of forecasting via end-to-end simulations. The limited Fourier overlap between HERA and CCAT-Prime results in a limited cumulative signal-to-noise ratio for these measurements.
Paper: Forecasts and Statistical Insights for Line Intensity Mapping Cross-correlations: A Case Study with 21 cm × [C II]
Authors: Hannah Fronenberg, Adrian Liu
Figure caption: Same as Figure 11, but assuming that foregrounds in the 21 cm maps have been reduced to 1% of their original level. Compared to Figures 10 and 11, the extra foreground cleaning allows individual bins of the cross-power spectrum to be detected to high significance, permitting a detailed characterization of the spectrum and inference of parameters such as the crossover scale from negative to positive correlation.
Paper: Forecasts and Statistical Insights for Line Intensity Mapping Cross-correlations: A Case Study with 21 cm × [C II]
Authors: Hannah Fronenberg, Adrian Liu
Figure caption: Top and bottom: same as Figure 9, but for the futuristic scenario described in Section 8 with increased survey area to 40 deg2 and a [C II] instrument with greater spectral resolution, but no 21 cm foreground mitigation nor avoidance of the wedge. Middle: same as the top, but survey area is increased to 400 deg2.
Paper: Forecasts and Statistical Insights for Line Intensity Mapping Cross-correlations: A Case Study with 21 cm × [C II]
Authors: Hannah Fronenberg, Adrian Liu
Figure caption: Top and bottom: same as Figure 8, but for the scenario described in Section 7.2 where 21 cm foreground-wedge modes are included. Middle: same as the top, but assuming that 21 cm foregrounds have been cleaned to 1% of their original amplitude in the original maps. One sees that cross-correlations alone cannot suppress foregrounds to an acceptable level, but can be powerfully combined with other mitigation techniques to yield statistically significant detections. However, many bins that are important for a detailed characterization of the cross-power spectrum remain noise limited.
Paper: Forecasts and Statistical Insights for Line Intensity Mapping Cross-correlations: A Case Study with 21 cm × [C II]
Authors: Hannah Fronenberg, Adrian Liu
Figure caption: From left to right, the probability distribution of the measured cross-power for increasing wavenumber k. In blue are the distributions obtained from full Monte Carlo simulations, in pink are Gaussianized approximations in which each model component is included but not covaried, and in orange each source of variance is simply computed independently. As more approximations are made, the distributions deviate further from the blue distributions.
Paper: Forecasts and Statistical Insights for Line Intensity Mapping Cross-correlations: A Case Study with 21 cm × [C II]
Authors: Hannah Fronenberg, Adrian Liu
Figure caption: Dimensionless correlation matrices ﹩{\boldsymbol{ \mathcal R }}﹩, defined in Equation (25), for the futuristic HERA × CCAT-Prime forecasts shown in Figure 10. Substantial error correlations exist between all k bins, necessitating the incorporation of full covariance information in evaluating signal-to-noise or in propagating power-spectrum estimates to downstream parameter inferences.
Paper: Forecasts and Statistical Insights for Line Intensity Mapping Cross-correlations: A Case Study with 21 cm × [C II]
Authors: Hannah Fronenberg, Adrian Liu
Figure caption: Top and bottom: same as Figure 8, but for the futuristic scenario described in Section 8 with a wedge cut and where the survey area is 40 deg2 and the spectral resolution of the [C II] is increased. Middle: same as the top, but survey area is increased to 400 deg2. With the increased overlap both in Fourier space (due to increased spectral resolution) and in configuration space (due to increased survey area), the detection significance rises markedly compared to current-generation surveys.
Paper: Forecasts and Statistical Insights for Line Intensity Mapping Cross-correlations: A Case Study with 21 cm × [C II]
Authors: Hannah Fronenberg, Adrian Liu
Figure caption: Relative foreground standard deviation as a function of the number of simulation patches averaged together (equivalently, survey area). In yellow the foreground standard deviation for the k = 0.16 h Mpc−1 is plotted, and in pink the same quantity for k = 0.36 h Mpc−1 is plotted. As a reference, the expected ﹩1/\sqrt{{N}_{\mathrm{patches}}}﹩ relation for the N patches independent sample is shown in dashed black. One sees that the foreground residual power averages down with increasing sky area.
Paper: Forecasts and Statistical Insights for Line Intensity Mapping Cross-correlations: A Case Study with 21 cm × [C II]
Authors: Hannah Fronenberg, Adrian Liu
Figure caption: The normalized probability density of a selection of power-spectrum voxels (various shades of gray) and the resultant power-spectrum bin (red). As a reference, the Gaussian fit to the red curve is plotted in dashed black. Binning is seen to Gaussianize power-spectrum distributions to some extent, but there are residual discrepancies in the tails.
Paper: Stellar Metallicities and Gradients in the Faint M31 Satellites Andromeda XVI and Andromeda XXVIII
Authors: Sal Wanying Fu, Daniel R. Weisz, Else Starkenburg, Nicolas Martin, Michelle L. M. Collins, Alessandro Savino, Michael Boylan-Kolchin, Patrick Côté, Andrew E. Dolphin, Nicolas Longeard, Mario L. Mateo, Francisco J. Mercado, Nathan R. Sandford, Evan D. Skillman
Figure caption: Presenting the CMD, Ca H and K color data, and resulting MDF for And XVI (top row) and And XXVIII (bottom row). Left column: broadband CMD of each galaxy, centered on its RGB, with stars analyzed color-coded by their F935N S/N. Stars in cyan circles are velocity interlopers determined using the data sets of M. L. M. Collins et al. (2013, 2014, 2015). Middle column: the position of analyzed stars in the Pristine-like color–color space, color-coded by their inferred metallicities. Monometallic Ca H and K tracks, convolved by the AST error profile of a star of median luminosity in each galaxy’s sample, are overplotted to guide the eye. Since stars of different luminosity have different AST error profiles, the Ca H and K model tracks used to infer the metallicity of each star may be different from the ones presented here. Some of these metallicities are inferred upper limits due to either falling at the edge of the model grid for bright stars or being low S/N for fainter stars. Right column: the resulting MDF for each galaxy. Filled histograms are well-constrained measurements, and open histograms are stars for which we can only derive an upper limit.
Paper: Stellar Metallicities and Gradients in the Faint M31 Satellites Andromeda XVI and Andromeda XXVIII
Authors: Sal Wanying Fu, Daniel R. Weisz, Else Starkenburg, Nicolas Martin, Michelle L. M. Collins, Alessandro Savino, Michael Boylan-Kolchin, Patrick Côté, Andrew E. Dolphin, Nicolas Longeard, Mario L. Mateo, Francisco J. Mercado, Nathan R. Sandford, Evan D. Skillman
Figure caption: Left: spatial distribution of stars in And XVI (top) and And XXVIII (bottom), color-coded by their inferred metallicities. Also presented are each galaxy’s half-light contours at 1Re , 2Re , and 3R  e  and a scale bar indicating physical distance. Right: result of fitting a linear model to our data as a function of elliptical half-light radius. Gray circles are individual data points in each galaxy, while red circles are the mean metallicity in spatial bins of 0.5R  e  for And XVI and 0.25R  e  for And XXVIII. We are able to robustly recover a metallicity gradient for And XXVIII. We also tentatively detect a gradient in And XVI. These are among the few metallicity gradients of M31 satellites more generally and, for And XVI, one of the few gradients detected in UFDs to date.
Paper: Stellar Metallicities and Gradients in the Faint M31 Satellites Andromeda XVI and Andromeda XXVIII
Authors: Sal Wanying Fu, Daniel R. Weisz, Else Starkenburg, Nicolas Martin, Michelle L. M. Collins, Alessandro Savino, Michael Boylan-Kolchin, Patrick Côté, Andrew E. Dolphin, Nicolas Longeard, Mario L. Mateo, Francisco J. Mercado, Nathan R. Sandford, Evan D. Skillman
Figure caption: Comparing M31 and MW satellite metallicity gradients to predictions from the FIRE-2 simulations. Left: presenting gradients of M31 satellites relative to the age–metallicity gradient strength relation from FIRE-2 simulations (F. J. Mercado et al. 2021). All galaxies, simulated and observed, are color-coded by their stellar masses. The linear form of the relation is orange, and light-colored circles denote the publicly available FIRE-2 simulated galaxies. Diamonds are M31 satellites from the literature, and the cross and star are And XVI and And XXVIII, respectively. Our dwarf galaxy gradient measurements trace out the age–gradient relation. Right: presenting gradients of MW satellites as compared to the FIRE-2 simulations. The simulated relations are presented in the same fashion as in the previous panel. Gradient measurements are sourced from S. Taibi et al. (2022). The MW satellites show large scatter about the age–gradient relation.
Paper: Stellar Metallicities and Gradients in the Faint M31 Satellites Andromeda XVI and Andromeda XXVIII
Authors: Sal Wanying Fu, Daniel R. Weisz, Else Starkenburg, Nicolas Martin, Michelle L. M. Collins, Alessandro Savino, Michael Boylan-Kolchin, Patrick Côté, Andrew E. Dolphin, Nicolas Longeard, Mario L. Mateo, Francisco J. Mercado, Nathan R. Sandford, Evan D. Skillman
Figure caption: Contextualizing our gradient measurements for And XVI and And XXVIII in the current landscape of gradients in M31 dwarfs. Literature gradients for M31 satellites are from the reanalysis of the N. Ho et al. (2015) data set in S. Taibi et al. (2022; NGC 205, NGC 185, NGC 147, And VII), and K. Kvasova et al. (2024; And XVIII). The galaxies are color-coded by the number of stars used to make the gradient measurement. We have measured gradients in these faint galaxies using a comparable number of stars to what Keck/DEIMOS spectroscopy was able to reach in dwarf galaxies several orders of magnitude more luminous.
Paper: Stellar Metallicities and Gradients in the Faint M31 Satellites Andromeda XVI and Andromeda XXVIII
Authors: Sal Wanying Fu, Daniel R. Weisz, Else Starkenburg, Nicolas Martin, Michelle L. M. Collins, Alessandro Savino, Michael Boylan-Kolchin, Patrick Côté, Andrew E. Dolphin, Nicolas Longeard, Mario L. Mateo, Francisco J. Mercado, Nathan R. Sandford, Evan D. Skillman
Figure caption: Illustrating the coverage of our HST imaging. The black dots are detected sources from the archival ACS archival image for each galaxy from which we measure broadband F475W and F814W photometry. The blue squares are the WFC3/F395N FOV. Each galaxy’s elliptical half-light contours are plotted in red, corresponding to 1, 2, and 3 half-light elliptical radii.
Paper: Predicting the Energetic Proton Flux with a Machine Learning Regression Algorithm
Authors: Mirko Stumpo, Monica Laurenza, Simone Benella, Maria Federica Marcucci
Figure caption: Left panel: joint histogram of SOHO/EPHIN proton flux data integrated in the energy range from 7.8 MeV up to 53 MeV and GOES proton >10 MeV integral channel data. The latter exhibit generally has a high noise level, whereas the former is more sensitive to the variability of the background since the noise level is lower. The R 2 coefficient, computed by discarding GOES background, is 0.9. Right panel: correlation plot between the peak reached by the proton flux data set (integrated in the channels P8+P25+P41) and the peak of the electron flux in the channel E300 during the SPEs cataloged in M. Laurenza et al. (2009) and M. Stumpo et al. (2021) and in the ASPIS database (M. Laurenza et al. 2023). The fit of the empirical law is very close to the bisector (black dashed line).
Paper: Predicting the Energetic Proton Flux with a Machine Learning Regression Algorithm
Authors: Mirko Stumpo, Monica Laurenza, Simone Benella, Maria Federica Marcucci
Figure caption: RF algorithm diagram adapted from J. Riebesell & S. Bringuier (2023). The paths highlighted in red represent the series of decisions made by each tree in the forest to make the final prediction. The RF prediction is the average of the tree’s final decisions. Each node represents a logical operation on a single feature. For example, a node may be asking if the variable E300 < 100 (cm2 s sr MeV)−1. If yes, go to the next node on the right; otherwise, go to the next node on the left. The graph and the logical operations are inferred from data during the training phase of the model.
Paper: Predicting the Energetic Proton Flux with a Machine Learning Regression Algorithm
Authors: Mirko Stumpo, Monica Laurenza, Simone Benella, Maria Federica Marcucci
Figure caption: EPHIN proton/electron differential channels P8 (blue), E150 (orange), and E300 (green) during the event of 2000 October 25. The black vertical lines mark the onset in proton and electron data. In this event, the electrons measured in E150 and E300 differential channels arrive 01:30 hr earlier than protons in the P8 channel. Furthermore, the time profiles are very similar.
Paper: Binary Yellow Supergiants in the Magellanic Clouds. I. Photometric Candidate Identification
Authors: Anna J. G. O'Grady, Maria R. Drout, Kathryn F. Neugent, Bethany Ludwig, Ylva Götberg, B. M. Gaensler
Figure caption: Color–color diagrams displaying the stellar atmosphere models used to design photometric criteria for identifying YSG+OB binaries. Pickles YSG models are shown as large off-white, yellow, light orange, and dark orange (A-, F-, G-, and K-type, respectively) circles, outlined in black. Single OB-type MS stars are shown as blue circles in the lower left. The combined models are shown as smaller circles with pale blue outlines. SMC models are shown here; LMC models show the same behavior. Left panels: Colors shown are V − I vs. U − B (top) and W1 − U (bottom), with a YSG luminosity of log(L/L ⊙) = 4.5. The dashed gray vertical lines correspond to T eff = 9000 K. Top right: For U – B colors, the effect of zero extinction is shown with a gray diamond, and the 3σ uncertainty in U – B (assuming an uncertainty of 0.05 mag) is shown with a green square and error bar. Bottom right: The effects of four different YSG luminosities are shown: log(L/L ⊙) = 4.2 (square), 4.5 (circle), 4.8 (triangle), and 5.1 (diamond). Luminosities other than 4.5 are horizontally offset for clarity.
Paper: Binary Yellow Supergiants in the Magellanic Clouds. I. Photometric Candidate Identification
Authors: Anna J. G. O'Grady, Maria R. Drout, Kathryn F. Neugent, Bethany Ludwig, Ylva Götberg, B. M. Gaensler
Figure caption: Color–color diagrams displaying observed candidate YSGs for V − I vs. U – B, at SMC (left) and LMC (right) metallicities. The stellar atmosphere models from Figure 2 are also shown. Top: Shown in gray are YSG candidates, selected as described in Section 3.1. Middle: The YSG candidates are classified as described in Section 3.2.1. Gold circles are YSGs with no photometric evidence of a blue excess. Green circles are YSGs with photometric evidence of a blue excess, and are possibly binary candidates. Dark blue-gray circles are YSGs that have V − I colors corresponding to T eff > 9000 K. Bottom: YSG candidates are classified by a “visual overdensity” method, described in Section 3.2.2. Colors are the same as the panel above.
Paper: A Potential Dynamical Origin of the Galactic Disk Warp: The Gaia–Sausage–Enceladus Major Merger
Authors: Mingji Deng, Cuihua Du, Yanbin Yang, Jiwei Liao, Dashuang Ye
Figure caption: Top-left panel: the orbital decay profile, the merger completed within the first 3 Gyr. Top-middle panel: the all-sky density profile of the GSE (blue points/lines). There are two breaks in our simulation and the red dashed line is the observational stellar halo profile (A. J. Deason et al. 2014). Top-right panel: the anisotropy parameter β of the GSE debris with the Galactocentric distance in a spherical coordinate system, compared with observational data. Bottom panels: the most representative characteristic of the GSE potted with a contour profile. The “sausage” structure is in the left panel and the distribution of the particle binding energy vs. angular momentum is in the right panel. Here, the negative value of V  ϕ  and L  Z  means that the stars on a retrograde orbit. Except for the top-left panel, all the other panels are plotted at 9.05 Gyr.