adsabs/AstroCLIMB huggingface dataset:
Paper: Astrometric Redshifts of Supernovae
Authors: Jaemyoung (Jason) Lee, Masao Sako, Richard Kessler, Alex I. Malz
Figure caption: Diagnostics for ﹩{z}_{\mathrm{SN}+\mathrm{DCR}+\mathrm{Host}}﹩. The blue points show the distributions for outliers, while the red histograms show the relative number of candidates for nonoutliers. The red bars have been scaled such that the area under the η 0.10 histograms and Not η 0.10 histograms are equal. We show the error bars for the η 0.10 points only because Not η 0.10 has many more candidates than η 0.10 and hence smaller error bars. As expected, outliers have lower FITPROB than nonoutliers, as well as slightly higher prior χ 2. Additionally, outliers are more likely to occur in lower-mass host galaxies.
Paper: Astrometric Redshifts of Supernovae
Authors: Jaemyoung (Jason) Lee, Masao Sako, Richard Kessler, Alex I. Malz
Figure caption: Binned residuals (bias), error on the mean (bold error bars), and the size of σ MAD (light error bars) as a function of z spec for ﹩{z}_{\mathrm{SN}}﹩, with and without cosmology priors. Using a cosmology prior generally tends to reduce the bias and σ MAD except at 0.6 < z spec < 0.8.
Paper: Astrometric Redshifts of Supernovae
Authors: Jaemyoung (Jason) Lee, Masao Sako, Richard Kessler, Alex I. Malz
Figure caption:                       ﹩{z}_{\mathrm{SN}}﹩ vs. z spec with a weak cosmology prior for LCFIT+z. Compared to the top panels of Figure 16, where there is no cosmology prior, the estimates are closer to the true values throughout all redshifts, and the outlier fractions are lower.
Paper: Astrometric Redshifts of Supernovae
Authors: Jaemyoung (Jason) Lee, Masao Sako, Richard Kessler, Alex I. Malz
Figure caption: OVL depending on number of standard deviations of separation for two identical normalized Gaussian distributions with different means. Because the means of the two distributions are fixed, the larger the σ, the higher the overlap. We take the case when the means of the two distributions are ﹩3\sqrt{2}﹩ apart or when OVL > 0.0344 as a threshold when combining z DCR and z Host.
Paper: Astrometric Redshifts of Supernovae
Authors: Jaemyoung (Jason) Lee, Masao Sako, Richard Kessler, Alex I. Malz
Figure caption:  η 0.10 as a function of z spec for ﹩{z}_{\mathrm{SN}}﹩, with and without cosmology priors. Similar to the bias and σ MAD, a cosmology prior generally reduces η 0.10.
Paper: Large Dark Matter Content and Steep Metallicity Profile Predicted for Ultradiffuse Galaxies Formed in High-spin Halos
Authors: José A. Benavides, Laura V. Sales, Mario. G. Abadi, Mark Vogelsberger, Federico Marinacci, Lars Hernquist
Figure caption: Relation between stellar mass and metallicity ([Fe/H]) for all simulated galaxies in the TNG50 simulation (gray dots), separated by star forming (left panel) and quenched (right panel). The median relation for each population is highlighted with the thick black solid line in each panel. The sample of simulated UDGs from TNG50, star forming and quenched, are indicated with blue stars and red circles in each panel, with their respective median profile shown by thick solid lines. A compilation of different observational data of UDGs is highlighted with green symbols (R. J. Smith et al. 2009; A. Ferré-Mateu et al. 2018, 2023; M. Gu et al. 2018; C. E. Barbosa et al. 2020). The mass–metallicity relation from E. N. Kirby et al. (2013) is indicated in the dotted purple lines (thick dashed, dotted, and thin dashed, respectively). For reference, we also include the simulated mass–metallicity relation at z = 2.2 (dashed black line) and the observed relation at z = 2.2 from X. Ma et al. (2016; orange dotted–dashed). Simulated UDGs are typically more metal poor than non-UDG galaxies of similar stellar mass and show less overall metallicity dispersion than the observed samples.
Paper: Large Dark Matter Content and Steep Metallicity Profile Predicted for Ultradiffuse Galaxies Formed in High-spin Halos
Authors: José A. Benavides, Laura V. Sales, Mario. G. Abadi, Mark Vogelsberger, Federico Marinacci, Lars Hernquist
Figure caption: Examples of inefficient simulated UDGs, highlighted with green unfilled squares in Figure 2. Left and center panels are star forming, and the galaxy on the right panel is quenched. In the top panels, face-on and edge-on views of each galaxy are shown, and in the bottom, the evolution of some properties: type (central:0, satellite:1), 3D stellar half-mass–radius (r h,⋆/kpc), star formation rate (SFR/M ⊙ yr−1), baryon mass within r gal (stars in red, and gas in cyan, in M ⊙), and halo mass (M 200/M ⊙) (dark matter in black, and M 200 in gray).
Paper: Large Dark Matter Content and Steep Metallicity Profile Predicted for Ultradiffuse Galaxies Formed in High-spin Halos
Authors: José A. Benavides, Laura V. Sales, Mario. G. Abadi, Mark Vogelsberger, Federico Marinacci, Lars Hernquist
Figure caption: Median metallicity profiles for the sample of simulated star-forming (blue) and quenched (red) UDGs in TNG50. The shaded regions correspond to the 25th and 75th percentiles in each distribution. We predict metallicity gradients that are steeper than the scenario where UDGs are formed from strong outflows: gray thick curve shows the median metallicity profile of UDGs from the NIHAO simulations (S. Cardona-Barrero et al. 2023). Simulated UDGs with tidal stripping also show flatter profiles in TNG50, with the orange dotted line indicating the median of all UDGs that have lost at least 50% of their stars. Three examples of these most extreme stripped cases are shown individually with thin dotted–dashed lines, which all seem in better agreement with the NIHAO results as well as with the observational data in DF44 by A. Villaume et al. (2022) shown with green symbols. This suggests metallicity profiles as promising candidates to help identify the formation scenario of UDGs.
Paper: Large Dark Matter Content and Steep Metallicity Profile Predicted for Ultradiffuse Galaxies Formed in High-spin Halos
Authors: José A. Benavides, Laura V. Sales, Mario. G. Abadi, Mark Vogelsberger, Federico Marinacci, Lars Hernquist
Figure caption: Vertical force per unit area as a function of cylindrical radii for two galaxy models: UDG in the left panel, and non-UDG in the right. In both cases, we include a stellar (blue dotted line) and gas (green dashed line) exponential disk, embedded in an NFW dark matter halo (magenta dashed–dotted line). In both panels, the total contribution (stars+gas+dark matter) is presented with a black solid line. The gray horizontal lines indicate the ram pressure (ρ ICM v 2) for typical values in three different host halo environments: clusters, groups, and galactic halos. All the gas below a given combination of gas density and velocity (gray horizontal lines) would be removed by ram pressure in this simplified model. Note that the larger mass density in non-UDG galaxies translate on larger restoring forces (y-axis) making them more resilient to ram pressure stripping.
Paper: Large Dark Matter Content and Steep Metallicity Profile Predicted for Ultradiffuse Galaxies Formed in High-spin Halos
Authors: José A. Benavides, Laura V. Sales, Mario. G. Abadi, Mark Vogelsberger, Federico Marinacci, Lars Hernquist
Figure caption: Halo spin parameter (λ) for simulated dwarf galaxies as a function of the dark matter halo formation redshift (z 50), defined as the redshift at which each halo attains half of their present-day virial mass. Normal star-forming dwarf galaxies (non-UDGs) are shown in gray individual symbols with their median trend indicated by the thick dashed line. Halos that assemble late have typically larger spins. Star-forming UDGs are highlighted in blue star symbols and follow the general trend outlined by the non-UDG dwarfs.
Paper: Large Dark Matter Content and Steep Metallicity Profile Predicted for Ultradiffuse Galaxies Formed in High-spin Halos
Authors: José A. Benavides, Laura V. Sales, Mario. G. Abadi, Mark Vogelsberger, Federico Marinacci, Lars Hernquist
Figure caption: Quenching timescale as a function of stellar mass for quenched non-UDG dwarfs (gray) and quenched UDGs (red) in TNG50. The medians of each population are indicated with thick lines, while the shaded regions are the 25th to 75th percentiles. On average, the UDGs (red line) have shorter quenching timescales than non-UDGs (dashed black line) at a given stellar mass.
Paper: Large Dark Matter Content and Steep Metallicity Profile Predicted for Ultradiffuse Galaxies Formed in High-spin Halos
Authors: José A. Benavides, Laura V. Sales, Mario. G. Abadi, Mark Vogelsberger, Federico Marinacci, Lars Hernquist
Figure caption: Mass–metallicity relation for all simulated galaxies in TNG50, where the color code indicates the stellar half-mass–radius.
Paper: Large Dark Matter Content and Steep Metallicity Profile Predicted for Ultradiffuse Galaxies Formed in High-spin Halos
Authors: José A. Benavides, Laura V. Sales, Mario. G. Abadi, Mark Vogelsberger, Federico Marinacci, Lars Hernquist
Figure caption: Magnesium to Iron ([Mg/Fe]) abundance as a function of stellar mass for simulated non-UDG dwarfs (gray) and quenched UDGs (red symbols). The median relation for each sample is highlighted with the thick solid black line and red line, respectively. UDGs are predicted to have an elevated [Mg/Fe] ratio compared to non-UDGs of the same mass. A sample of observational UDGs by A. Ferré-Mateu et al. (2018, 2023) and T. Ruiz-Lara et al. (2018) are included with green symbols.
Paper: Cross-correlation of Luminous Red Galaxies with Machine Learning Selected Active Galactic Nuclei in HSC-SSP: Unobscured AGN Residing in More Massive Halos
Authors: Rodrigo Córdova Rosado, Andy D. Goulding, Jenny E. Greene, Grayson C. Petter, Ryan C. Hickox, Nickolas Kokron, Michael A. Strauss, Jahmour J. Givans, Yoshiki Toba, Cassandra Starr Henderson
Figure caption: (a) The luminosity distribution of the unobscured + reddened (blue contours, objects we infer to have broad emission lines) and obscured (red contours, objects inferred to have narrow emission lines) AGN samples as a function of the fiducial redshift (z best) for each object. The contours are set at the (0.25, 0.5, 0.75) quantiles of each distribution. (b) The g − W3 (AB magnitude) color vs. the z best distribution for unobscured + reddened and obscured AGN, following the same color scheme and contour quantile levels in the upper panel. We divide the AGN sample into unobscured, reddened, and obscured classes based on their distribution in this color–redshift space (A. D. Goulding et al. 2024, in preparation). We convert the WISE magnitudes in the Vega system to AB with m AB = m Vega + Δm, where Δm(W3) = 5.174 (T. H. Jarrett et al. 2011). The dashed lines are the g − W3 color–redshift evolution for three AGN templates from M. Polletta et al. (2007). We use their unobscured quasar template (QSO 1, blue line), their unobscured quasar with a B. T. Draine (2003) extinction law applied with A  V  = 0.5 and R  V  = 3.1 (to create a reddened quasar template; green line), and their obscured quasar template (QSO 2, red line). Histograms show the individual distributions for a given quantity, where colors indicate the AGN sample specified.
Paper: Cross-correlation of Luminous Red Galaxies with Machine Learning Selected Active Galactic Nuclei in HSC-SSP: Unobscured AGN Residing in More Massive Halos
Authors: Rodrigo Córdova Rosado, Andy D. Goulding, Jenny E. Greene, Grayson C. Petter, Ryan C. Hickox, Nickolas Kokron, Michael A. Strauss, Jahmour J. Givans, Yoshiki Toba, Cassandra Starr Henderson
Figure caption: Cross-correlation between HSC LRGs and all AGN measured, fitted with CCL in different fields across redshift bins, shown in θ ω(θ) to reduce the plotted dynamic range. The AGN sample is limited to L 6 μm > 3 × 1044 erg s−1. The open symbols are the per-bin average ω  GA (θ) across fields. The 1σ uncertainties are drawn from the square root of the diagonal of the jackknife covariance matrix for the AGN sample. The solid lines represent the fitted halofit DM model to each field. The points considered in this analysis are those past the gray shaded region, at ﹩s\gt 1\,{h}^{-1}\mathrm{Mpc}\,(\theta \gtrsim 3^{\prime} )﹩.
Paper: Cross-correlation of Luminous Red Galaxies with Machine Learning Selected Active Galactic Nuclei in HSC-SSP: Unobscured AGN Residing in More Massive Halos
Authors: Rodrigo Córdova Rosado, Andy D. Goulding, Jenny E. Greene, Grayson C. Petter, Ryan C. Hickox, Nickolas Kokron, Michael A. Strauss, Jahmour J. Givans, Yoshiki Toba, Cassandra Starr Henderson
Figure caption: The measured HSC LRG projected angular autocorrelation in our three HSC fields across three redshift bins in θ ω(θ). We use this scaling by a power of θ to reduce the dynamic range of the plot and more clearly see the differences in the measured points. The open symbols are the per-bin average correlation function across fields. The 1σ uncertainties are drawn from the square root of the diagonal of the jackknife covariance matrix for the sample. The solid lines represent the fitted halofit DM model to each field. We fit points for physical scales s > 1 h −1 Mpc, while the gray region contains the interior points excluded from the fit. Note that the model does not include the one-halo term, which is why the data rise significantly above the model on small scales.
Paper: Cross-correlation of Luminous Red Galaxies with Machine Learning Selected Active Galactic Nuclei in HSC-SSP: Unobscured AGN Residing in More Massive Halos
Authors: Rodrigo Córdova Rosado, Andy D. Goulding, Jenny E. Greene, Grayson C. Petter, Ryan C. Hickox, Nickolas Kokron, Michael A. Strauss, Jahmour J. Givans, Yoshiki Toba, Cassandra Starr Henderson
Figure caption: The HSC galaxy sample used in this analysis after isolating the LRG population with an optical color–color cut, split by HSC field. The fields (and approximate central R.A., decl. coordinates) are XMM-LSS (34°, −2°), VVDS (346°, 2°), and GAMA (176°, 0°). The bespoke (see Section 2.2) masking strategy, visualized here by stripes that have been removed from the HSC fields, is reflective of areas where there are correlated photometry errors in the WISE data. Other removed areas are primarily due to bright stars and Galactic foreground objects. This figure is produced with the plotting tool skymapper (P. Melchior 2021).
Paper: Cross-correlation of Luminous Red Galaxies with Machine Learning Selected Active Galactic Nuclei in HSC-SSP: Unobscured AGN Residing in More Massive Halos
Authors: Rodrigo Córdova Rosado, Andy D. Goulding, Jenny E. Greene, Grayson C. Petter, Ryan C. Hickox, Nickolas Kokron, Michael A. Strauss, Jahmour J. Givans, Yoshiki Toba, Cassandra Starr Henderson
Figure caption:  L 6 μm luminosity distributions for the wide redshift bin for this analysis combining all considered HSC fields. The vertical dashed line indicates the L 6 μm lower limit for this analysis at 3 × 1044 erg s−1. The different colors indicate the different AGN subsamples that are considered in the wide redshift bin analysis.
Paper: Cross-correlation of Luminous Red Galaxies with Machine Learning Selected Active Galactic Nuclei in HSC-SSP: Unobscured AGN Residing in More Massive Halos
Authors: Rodrigo Córdova Rosado, Andy D. Goulding, Jenny E. Greene, Grayson C. Petter, Ryan C. Hickox, Nickolas Kokron, Michael A. Strauss, Jahmour J. Givans, Yoshiki Toba, Cassandra Starr Henderson
Figure caption: Normalized dN/dz measured from the LRG (top) and full AGN samples (bottom) for auto- and cross-correlations in the three narrow redshift bins, across different HSC fields. These are constructed following the procedure outlined in Appendix B.
Paper: Cross-correlation of Luminous Red Galaxies with Machine Learning Selected Active Galactic Nuclei in HSC-SSP: Unobscured AGN Residing in More Massive Halos
Authors: Rodrigo Córdova Rosado, Andy D. Goulding, Jenny E. Greene, Grayson C. Petter, Ryan C. Hickox, Nickolas Kokron, Michael A. Strauss, Jahmour J. Givans, Yoshiki Toba, Cassandra Starr Henderson
Figure caption: Recovered galaxy bias from the autocorrelation of HSC-selected LRGs, in three redshift bins, for each analysis field. The inverse variance weighted mean and 1σ uncertainty are plotted with a dashed line and shaded region.
Paper: Cross-correlation of Luminous Red Galaxies with Machine Learning Selected Active Galactic Nuclei in HSC-SSP: Unobscured AGN Residing in More Massive Halos
Authors: Rodrigo Córdova Rosado, Andy D. Goulding, Jenny E. Greene, Grayson C. Petter, Ryan C. Hickox, Nickolas Kokron, Michael A. Strauss, Jahmour J. Givans, Yoshiki Toba, Cassandra Starr Henderson
Figure caption:  L 6 μm luminosity distributions for the specified redshift bins for this analysis combining all considered HSC fields. The vertical dashed line indicates the L 6 μm lower limit for this analysis at 3 × 1044 erg s−1. The different colors indicate the different AGN subsamples.
Paper: Cross-correlation of Luminous Red Galaxies with Machine Learning Selected Active Galactic Nuclei in HSC-SSP: Unobscured AGN Residing in More Massive Halos
Authors: Rodrigo Córdova Rosado, Andy D. Goulding, Jenny E. Greene, Grayson C. Petter, Ryan C. Hickox, Nickolas Kokron, Michael A. Strauss, Jahmour J. Givans, Yoshiki Toba, Cassandra Starr Henderson
Figure caption: Our measured AGN bias and inferred halo masses for the full AGN sample and unobscured + reddened and obscured samples in comparison with other analyses. Left: the recovered AGN bias as a function of redshift in comparison with X-ray, optical, and IR selected AGN clustering studies. Marker styles represent the analysis used in the comparison. Marker color indicates whether it is an analysis of all the AGN in the sample, or of a subsample for unobscured + reddened or obscured AGN. The colored boxes illustrate our measurements and statistical uncertainties (solid outlines), and we include our estimate of the possible magnitude of inferred bias and halo mass shifts were there to be a systematic bias in the dN/dz for Δz/(1 + z) = 0.03 (lower-opacity red rectangle). Plotted lines illustrate the nominal b(z, M  h ) tracks for different halo masses (J. L. Tinker et al. 2010). Right: inferred halo masses as a function of bolometric luminosity (L bol); markers are the same as those in the left panel. We convert studies’ recorded L  ν  to L bol using the correction from P. F. Hopkins et al. (2007). We compare, where available, with values from projected angular correlation function analyses, rather than projected real-space derived values (see R. C. Hickox et al. 2011). This halo mass comparison serves as a visualization of other results in the field and the halo mass differences between subtypes they find, but absolute value comparisons are difficult since the studies presented here use different b–M  h  formulations.
Paper: Cross-correlation of Luminous Red Galaxies with Machine Learning Selected Active Galactic Nuclei in HSC-SSP: Unobscured AGN Residing in More Massive Halos
Authors: Rodrigo Córdova Rosado, Andy D. Goulding, Jenny E. Greene, Grayson C. Petter, Ryan C. Hickox, Nickolas Kokron, Michael A. Strauss, Jahmour J. Givans, Yoshiki Toba, Cassandra Starr Henderson
Figure caption: Cross-correlation between HSC galaxies and the full AGN sample and subtype samples in the wide redshift bin (z ∈ 0.7–1.0) and with L 6 μm > 3 × 1044 erg s−1, shown in θ ω(θ). The open symbols are the per-bin average correlation function across fields. The 1σ uncertainties are drawn from the square root of the diagonal of the jackknife covariance matrix for each AGN subtype sample. The solid lines represent the fitted halofit DM model to each field. The points considered in this analysis are those past the gray shaded region, at s > 1 h −1 Mpc, where we are confident the model is able to describe the data. We note the model still agrees with the measured points well within the shaded region. We observe a departure from the DM model on small scales, as expected for the one-halo term. The dashed line represents the full AGN sample average best-fit halofit model, and is repeated in all panels as a reference line for ease of comparison. Top: the cross-correlation of the full, unobscured + reddened, and obscured samples of AGN across three fields. Bottom: the cross-correlation of the unobscured, reddened, and reddened + obscured samples with the LRG sample. The symbol shapes used for each sample here match those in Figure 11.
Paper: Cross-correlation of Luminous Red Galaxies with Machine Learning Selected Active Galactic Nuclei in HSC-SSP: Unobscured AGN Residing in More Massive Halos
Authors: Rodrigo Córdova Rosado, Andy D. Goulding, Jenny E. Greene, Grayson C. Petter, Ryan C. Hickox, Nickolas Kokron, Michael A. Strauss, Jahmour J. Givans, Yoshiki Toba, Cassandra Starr Henderson
Figure caption: Summary of recovered halo masses (M  h ) from the AGN cross-correlation of the full AGN sample (top row) and the AGN subtype samples (bottom row). The left panels illustrate this as a function of redshift. The right panels show the halo masses as a function of estimated L 6 μm. The position and uncertainties in z and L 6 μm are drawn from the 16%, 50%, and 84% quantiles of each bin’s underlying distribution. We find that unobscured + reddened AGN are found in significantly more massive halos than obscured AGN. We additionally estimate the effect of a systematic redshift error in the dN/dz on our inferred halo masses. As indicated by the red arrow, an upward z shift in the dN/dz by Δz/(1 + z) = 0.03 would lead to a boosting of the inferred M  h  by a magnitude of 0.4 dex, a linear factor of ∼2.5. The values represented are also found in Table 2.
Paper: Cross-correlation of Luminous Red Galaxies with Machine Learning Selected Active Galactic Nuclei in HSC-SSP: Unobscured AGN Residing in More Massive Halos
Authors: Rodrigo Córdova Rosado, Andy D. Goulding, Jenny E. Greene, Grayson C. Petter, Ryan C. Hickox, Nickolas Kokron, Michael A. Strauss, Jahmour J. Givans, Yoshiki Toba, Cassandra Starr Henderson
Figure caption: HSC color–color diagram for the HSC galaxy sample in the XMM field, across three narrow redshift bins. As described in Appendix A, we isolate an LRG sample from the whole galaxy catalog via a color–color cut, g − r > 1.2 and r − i > 1.0 (as indicated by the red dashed lines). The objects are isolated in a specific redshift bin given the fiducial redshift (z best) for every object. The histograms illustrate the distribution of galaxies in color space, and illustrate the scale at which we separate the LRG population from the rest of the galaxies. The red solid lines in the histograms highlight the color distributions of the final selection of objects.
Paper: “SNe Ia Twins for Life”: Toward a Precise Determination of H 0 
Authors: P. Ruiz-Lapuente, J. I. González Hernández
Figure caption: Left-hand panel: comparison of early-time spectra of SN 2013aa and SN 2017cbv at −2 days before maximum light. Right-hand panel: 1σ, 2σ, and 3σ contours for the posterior probability distribution. The dashed vertical values correspond to 1σ error in each variable.
Paper: Cross-correlation of Luminous Red Galaxies with Machine Learning Selected Active Galactic Nuclei in HSC-SSP: Unobscured AGN Residing in More Massive Halos
Authors: Rodrigo Córdova Rosado, Andy D. Goulding, Jenny E. Greene, Grayson C. Petter, Ryan C. Hickox, Nickolas Kokron, Michael A. Strauss, Jahmour J. Givans, Yoshiki Toba, Cassandra Starr Henderson
Figure caption: Normalized dN/dz measured from the AGN samples for cross-correlations in the wide redshift bin (z ∈ 0.7–1.0) for the L 6 μm > 3 × 1044 erg s−1 threshold, for the three fields of interest.
Paper: “SNe Ia Twins for Life”: Toward a Precise Determination of H 0 
Authors: P. Ruiz-Lapuente, J. I. González Hernández
Figure caption: Left-hand panel: comparison of late-time spectra of SN 2013aa and SN 2017cbv at 361 days past maximum light. Right-hand panel: as in Figure 1 but for the 361 days phase.