adsabs/AstroCLIMB huggingface dataset:
Paper: Element Nucleosynthetic Origins from Abundance Spatial Distributions beyond the Milky Way
Authors: Zefeng Li, Mark R. Krumholz, Anna F. McLeod, A. Mark Swinbank, Emily Wisnioski, J. Trevor Mendel, Francesco Belfiore, Giovanni Cresci, Giacomo Venturi, Jia-Lai Kang
Figure caption: [S III] electron temperature map (lower left) and nitrogen (upper left), oxygen (upper right), and sulfur (lower right) abundance maps for NGC 5253. The fields of view are centered on the galactic center and are the same in each panel. The coverage of the nitrogen abundance map is smaller than those of oxygen and sulfur maps because of the faintness of the auroral line [N II]λ5755 and the resulting low coverage of electron temperatures in the N+ zones.
Paper: Element Nucleosynthetic Origins from Abundance Spatial Distributions beyond the Milky Way
Authors: Zefeng Li, Mark R. Krumholz, Anna F. McLeod, A. Mark Swinbank, Emily Wisnioski, J. Trevor Mendel, Francesco Belfiore, Giovanni Cresci, Giacomo Venturi, Jia-Lai Kang
Figure caption: Posterior PDFs of true zero-lag cross correlations ΞXY(0) for each pair of elements, corrected for observational uncertainties. Correction increases from left to right. The vertical dashed lines indicate the zero-lag cross correlations predicted by simulations (C. Zhang et al. 2025). Our estimated zero-lag cross correlations suggest that all the elements are correlated (>0.4) in the spatial distributions of their abundances, among which oxygen and sulfur are the most correlated pair as they have the same origin site.
Paper: Element Nucleosynthetic Origins from Abundance Spatial Distributions beyond the Milky Way
Authors: Zefeng Li, Mark R. Krumholz, Anna F. McLeod, A. Mark Swinbank, Emily Wisnioski, J. Trevor Mendel, Francesco Belfiore, Giovanni Cresci, Giacomo Venturi, Jia-Lai Kang
Figure caption: The distributions of our three fit parameters (injection width winj, correlation length l, and the factor by which the autocorrelation is reduced by measurement uncertainty f), describing the autocorrelation functions of nitrogen (upper left), oxygen (upper right), and sulfur (lower right) measured in NGC 5253 as derived from our MCMC. In each panel, the heat map colors black, dark gray, light gray, and white (i.e., the four contours from inside to outside) show probability densities corresponding to 1σ, 2σ, 3σ, and 4σ, respectively. Partially transparent points show individual samples in regions of lower probability. We list the median and 90% confidence intervals of the fitted parameters [winj, l, f] = [61.5  ±  0.3 pc, 4.8  ±  3.6 pc, 2.347  ±  0.004] for the O abundance map; = [7.3  ±  0.2 pc, 7.0  ±  0.2 pc, 2.209  ±  0.008] for the N abundance map; and = [45.7  ±  0.7 pc, 54.9  ±  1.0 pc, 2.135  ±  0.007] for the S abundance map.
Paper: Element Nucleosynthetic Origins from Abundance Spatial Distributions beyond the Milky Way
Authors: Zefeng Li, Mark R. Krumholz, Anna F. McLeod, A. Mark Swinbank, Emily Wisnioski, J. Trevor Mendel, Francesco Belfiore, Giovanni Cresci, Giacomo Venturi, Jia-Lai Kang
Figure caption: Cross-correlation functions for N–O (purple), O–S (green), and N–S (orange), uncorrected for observational uncertainties. Correlation increases from lower (uncorrelated) to upper (nearly perfectly correlated). At scales smaller than 60 pc, the O–S cross-correlation functions are most correlated, followed by those of N–O and N–S, thanks to the same origin site (CC SNe) that oxygen and sulfur share.
Paper: Element Nucleosynthetic Origins from Abundance Spatial Distributions beyond the Milky Way
Authors: Zefeng Li, Mark R. Krumholz, Anna F. McLeod, A. Mark Swinbank, Emily Wisnioski, J. Trevor Mendel, Francesco Belfiore, Giovanni Cresci, Giacomo Venturi, Jia-Lai Kang
Figure caption: Alternative versions of Figures 3 (upper) and 4 (lower), where nitrogen abundance is measured using the inferred electron temperatures in the N+ zone (Equation (7)). The conclusion remains unchanged that oxygen and sulfur are better correlated with each other than with nitrogen [﹩{{\rm{\Xi }}}_{{{\rm{N}}}^{{\prime} }{\rm{O}}}(0)=1.028\pm 0.007﹩ and ﹩{{\rm{\Xi }}}_{{{\rm{N}}}^{{\prime} }{\rm{S}}}(0)=1.195\pm 0.06﹩].
Paper: Element Nucleosynthetic Origins from Abundance Spatial Distributions beyond the Milky Way
Authors: Zefeng Li, Mark R. Krumholz, Anna F. McLeod, A. Mark Swinbank, Emily Wisnioski, J. Trevor Mendel, Francesco Belfiore, Giovanni Cresci, Giacomo Venturi, Jia-Lai Kang
Figure caption: Alternative versions of the upper left panel of Figure 1 (left) and the lower panel of Figure 2 (right), where nitrogen abundance is measured using the inferred electron temperatures in the N+ zone (Equation (7)). The conclusion remains unchanged that the initial injection for nitrogen (15.3  ±  0.2 pc) occurs on significantly smaller scales than those for oxygen and sulfur.
Paper: The Cosmic Microwave Background Optical Depth Constrains the Duration of Reionization
Authors: Christopher Cain, Alexander Van Engelen, Kevin S. Croker, Darby Kramer, Anson D'Aloisio, Garett Lopez
Figure caption: Representative reionization histories with different τCMB and ﹩{D}_{\ell =3000}^{{\rm{pkSZ}}}﹩. From left to right, the panels show the ionization history, τCMB(<z), and ﹩{D}_{\ell =3000}^{{\rm{pkSZ}}}(\lt z)﹩ for models with low zmid/low Δz (black solid lines), high zmid/high Δz (red dashed lines), and high zmid/low Δz (blue dotted lines). The left panel shows the range of zend allowed by the Lyα forest, the middle panel shows measurements of τCMB (see text), and the right panel shows measurements/upper limits on ﹩{D}_{\ell =3000}^{{\rm{pkSZ}}}﹩ from SPT. The models with high τCMB either end reionization too early for the Lyα forest (high zmid, low Δz) or have ﹩{D}_{\ell =3000}^{{\rm{pkSZ}}}﹩ too high for SPT (high zmid, high Δz). See text for details.
Paper: The Cosmic Microwave Background Optical Depth Constrains the Duration of Reionization
Authors: Christopher Cain, Alexander Van Engelen, Kevin S. Croker, Darby Kramer, Anson D'Aloisio, Garett Lopez
Figure caption: Dependence of τCMB on zmid and Δz90. Left: with no Lyα forest constraint and assuming symmetric reionization histories (Az = 1). We show contours for τCMB = 0.06 and 0.09 for reference. Middle: the same, but assuming highly asymmetric reionization histories (Az = 5). Right: including Lyα forest information as in Figure 1. The white region in the lower right has Δz < zlat − zmid (red dotted–dashed line), which is unphysical, or Az ≤ 0.5 (black dotted–dashed line), for which the Weibull function often does not have solutions. In the upper left, the τCMB contours are nearly diagonal, indicating sensitivity to both zmid and Δz.
Paper: The Cosmic Microwave Background Optical Depth Constrains the Duration of Reionization
Authors: Christopher Cain, Alexander Van Engelen, Kevin S. Croker, Darby Kramer, Anson D'Aloisio, Garett Lopez
Figure caption: Consistency of upper limits on the pkSZ signal from SPT (C. L. Reichardt et al. 2021, R20) with low and high values of τCMB. Left: ﹩{D}_{\ell =3000}^{{\rm{pkSZ}}}﹩ vs. τCMB vs. Δz90 with no Lyα forest constraint imposed. The solid vertical lines indicate low and high values of τCMB (see legend), and the contours denote different upper limits from C. L. Reichardt et al. (2021; see text). Low values of ﹩{D}_{\ell =3000}^{{\rm{pkSZ}}}﹩ are consistent with high τCMB if reionization happened early and had a short duration (and thus ended early, lower right of the plot). Right: the same, but imposing the Lyα forest constraint. Models with high τCMB and low Δz90 (and vice versa) are excluded, and τCMB = 0.09, as suggested by N. Sailer et al. (2025) to reconcile BAO tensions, requires ﹩{D}_{\ell =3000}^{{\rm{pkSZ}}}﹩ above the 2σ upper limit from C. L. Reichardt et al. (2021).
Paper: The Cosmic Microwave Background Optical Depth Constrains the Duration of Reionization
Authors: Christopher Cain, Alexander Van Engelen, Kevin S. Croker, Darby Kramer, Anson D'Aloisio, Garett Lopez
Figure caption: Qualitative illustration of how including Lyα forest constraints on reionization’s endpoint changes the interpretation of τCMB. The top panel shows several symmetric reionization histories, with higher midpoints yielding larger τCMB. In this standard picture, τCMB mainly constrains zmid, subject only to the Lyα forest requirement that reionization end before z = 5.5. In the bottom panel, we pin the end of reionization (5% neutral) to z = 5.6, as suggested by the most recent forest data. In this case, higher τCMB requires both earlier and more extended reionization histories.
Paper: An Extremely Metal-poor Lyα Emitter Candidate at z = 6 Revealed through Absorption Spectroscopy
Authors: Dominika Ďurovčíková, Anna-Christina Eilers, Robert A. Simcoe, Louise Welsh, Romain A. Meyer, Jorryt Matthee, Emma V. Ryan-Weber, Minghao Yue, Harley Katz, Sindhu Satyavolu, George Becker, Frederick B. Davies, Emanuele Paolo Farina
Figure caption: Left: a smoothed, point-spread function (PSF)-subtracted SNR image (SMOOTH[χ]) from MUSE showing the newly identified LAE in the foreground of the quasar PSO J158-14 (offset by −1500 km s−1 from the Lyα emission of the quasar). Right: the LAE spectrum from the original (not PSF-subtracted) MUSE data cube extracted over the contour shown in the left panel is shown in red (1σ and 2σ uncertainties shown as shaded red regions). The quasar spectrum is shown in black with gray uncertainties, which are too small to be clearly visible, with its Lyα redshift marked by ﹩{z}_{{\rm{QSO}}}^{{\rm{Ly}}\alpha }﹩. The redshift of the LAE, ﹩{z}_{{\rm{LAE}}}^{{\rm{Ly}}\alpha }=6.0323﹩ aligns with the end of the quasar’s proximity zone, and an absorber with a column density of ﹩{\mathrm{log}}\,{N}_{{\rm{HI}}}/{{\rm{cm}}}^{-2}\approx 19.7﹩ (a sub-DLA) can be used to model the damping wing of this quasar. For comparison, we also overplot example damping wing profiles arising from a homogenous IGM at a volume-averaged neutral gas fraction of ﹩{\bar{x}}_{{\rm{HI}}}=10 \% ,20 \% ﹩, and 30% (J. Miralda-Escudé 1998). Note that the quasar spectrum has been continuum normalized to enable damping wing modeling (see Section 2.2 for details), while the LAE spectrum is shown in absolute flux density units (red y-axis on the right). No other emission lines are seen across the wavelength range of MUSE.
Paper: An Extremely Metal-poor Lyα Emitter Candidate at z = 6 Revealed through Absorption Spectroscopy
Authors: Dominika Ďurovčíková, Anna-Christina Eilers, Robert A. Simcoe, Louise Welsh, Romain A. Meyer, Jorryt Matthee, Emma V. Ryan-Weber, Minghao Yue, Harley Katz, Sindhu Satyavolu, George Becker, Frederick B. Davies, Emanuele Paolo Farina
Figure caption: Top: a deep (10.2 hr) medium-resolution J-band calibrated spectrum of the quasar PSO J158-14, observed with the Magellan/FIRE and VLT/X-Shooter spectrographs. This spectrum was further continuum normalized (see the main text) and used to stack regions corresponding to low-ionization absorption lines (shown as vertical red lines) that could be associated with the foreground LAE (marked by the blue vertical line). The orange vertical lines mark all absorption lines due to foreground systems identified by R. L. Davies et al. (2023b), and the gray vertical lines mask spectral regions heavily contaminated by telluric emission. Bottom: zoom-in on the absorption line regions in the continuum-normalized spectrum that could be associated with the proximate absorber, in velocity relative to the quasar’s rest frame. The dashed red line corresponds to the redshift of the LAE identified in MUSE observations, and orange regions mark absorption features due to other foreground systems (R. L. Davies et al. 2023b). Additionally, we show the telluric transmission in blue, and we overplot simulated absorption profiles at a range of metallicities [Z/H]. In both parts of the figure, the 1σ and 2σ uncertainties are shown as gray and light gray shaded regions.
Paper: An Extremely Metal-poor Lyα Emitter Candidate at z = 6 Revealed through Absorption Spectroscopy
Authors: Dominika Ďurovčíková, Anna-Christina Eilers, Robert A. Simcoe, Louise Welsh, Romain A. Meyer, Jorryt Matthee, Emma V. Ryan-Weber, Minghao Yue, Harley Katz, Sindhu Satyavolu, George Becker, Frederick B. Davies, Emanuele Paolo Farina
Figure caption: Left: metallicity constraint on the newly found proximate absorption system. Here we show the inverse-variance-weighted stack of low-ionization absorption line regions in the continuum-normalized spectrum of PSO J158-14, in terms of the velocity shift from the quasar’s rest frame, shown in the bottom part of Figure 3, and also marked by vertical red lines in the top part of Figure 3. The 1σ and 2σ uncertainties on the stacked spectrum are shown as gray and light gray shaded regions. The modeled absorption profiles are stacked using the same weights as the data and overplotted as colored lines. There is no detectable absorption at the redshift of the LAE at this spectral resolution, yielding a metallicity constraint of [Z/H] < −3. This absorber thus constitutes an extremely metal-poor candidate. The shaded regions around the modeled absorption line profiles reflect the 1σ uncertainties on the ionization corrections derived from CLOUDY, as shown in the right panel. Right: ionization corrections (with 1σ uncertainties from marginalizing over the modeled hydrogen densities) to the column densities of individual ions from CLOUDY used for absorption line profile modeling. Even though this absorption system is not fully self-shielding, the corrections are close to zero. This holds even when the radiation from the background quasar is taken into account (solid data points; compared to CLOUDY models without the quasar radiation shown as semitransparent data points).
Paper: An Extremely Metal-poor Lyα Emitter Candidate at z = 6 Revealed through Absorption Spectroscopy
Authors: Dominika Ďurovčíková, Anna-Christina Eilers, Robert A. Simcoe, Louise Welsh, Romain A. Meyer, Jorryt Matthee, Emma V. Ryan-Weber, Minghao Yue, Harley Katz, Sindhu Satyavolu, George Becker, Frederick B. Davies, Emanuele Paolo Farina
Figure caption: Archival imaging data of the quasar field from HST. Left: it is unlikely that this emission is not Lyα as no foreground emission is detected by HST ACS/WFC imaging with the F555W filter at a limiting magnitude of 26.88. Right: a nondetection in the F850LP filter at a limiting magnitude of 25.98 is consistent with the Lyα line flux measured in MUSE.
Paper: Multi-instrument Search for Gamma-Ray Counterpart of X-Ray Transients Detected by EP/WXT
Authors: Yan-Qiu Zhang, Wang-Chen Xue, Jin-Peng Zhang, Ce Cai, Shao-Lin Xiong, Cheng-Kui Li, Yuan Liu, Chen-Wei Wang, Hao-Xuan Guo, Shuo Xiao, Wen-Jun Tan, Chao Zheng, Jia-Cong Liu, Sheng-Lun Xie, Peng Zhang, Wen-Long Zhang, Yue Wang, Zheng-Hang Yu, Yang-Zhao Ren, Ping Wang, Yue Huang, Xiao-Bo Li, Xiao-Yun Zhao, Shi-Jie Zheng, Zhen Zhang, Shu-Xu Yi, Li-Ming Song, Shuang-Nan Zhang
Figure caption: Figure A1 continued.
Paper: Dissecting the Massive Pristine, Neutral Gas Reservoir of a Remarkably Bright Galaxy at z = 14.179
Authors: Kasper E. Heintz, Clara L. Pollock, Joris Witstok, Stefano Carniani, Kevin N. Hainline, Francesco D'Eugenio, Chamilla Terp, Aayush Saxena, Darach Watson
Figure caption: Dust-to-gas ratio, AV/NHI, as a function of gas-phase metallicity, [M/H]. GS-z14 is shown by the red star symbol and the high-redshift γ-ray burst sightlines through star-forming galaxies at z = 1.7–6.3 as the colored circles (measurements) or triangles (upper limits). The best-fit relation for the γ-ray burst sample is shown by the black solid line, with the 1σ and 2σ uncertainty indicated by the dark- and light-gray shaded region. The red shaded region marks the expected gas-phase metallicity from the relation, assuming the measured dust-to-gas ratio of GS-z14, AV/NHI = (1.3 ± 0.6) × 10−23 mag cm2.
Paper: Dissecting the Massive Pristine, Neutral Gas Reservoir of a Remarkably Bright Galaxy at z = 14.179
Authors: Kasper E. Heintz, Clara L. Pollock, Joris Witstok, Stefano Carniani, Kevin N. Hainline, Francesco D'Eugenio, Chamilla Terp, Aayush Saxena, Darach Watson
Figure caption: Schematic of the gas components seen in absorption and emission for GS-z14. The central star-forming H II regions also emit most of the rest-frame UV and [O III] emission. The more extended neutral gas region likely dominates the observed H I column density and more diffuse [C II] emission due to the lower metallicity of the gas. The compact size of the young stellar population increases the ionization parameter U and the [O III]/[C II] line ratio. Inspired by Y. Harikane et al. (2020).
Paper: Dissecting the Massive Pristine, Neutral Gas Reservoir of a Remarkably Bright Galaxy at z = 14.179
Authors: Kasper E. Heintz, Clara L. Pollock, Joris Witstok, Stefano Carniani, Kevin N. Hainline, Francesco D'Eugenio, Chamilla Terp, Aayush Saxena, Darach Watson
Figure caption: (Left) JWST/NIRSpec Prism 1D spectrum of GS-z14. The photometrically corrected flux density is shown by the black curve, and the associated uncertainty is shown by the gray shaded region. In the inset is shown a zoom-in on the rest-frame UV part of the spectrum, with the best-fit DLA model overplotted (red solid curve), an IGM-only model (blue dotted–dashed), and predictions for a 2γ nebular continuum emission (orange dashed line). (Right) Corner plot of the posterior distributions from the spectroscopic modeling, with the median and 16–84 percentiles marked.
Paper: Correlations between Event Rates of Short Gamma-Ray Bursts and Star Formation Rates with/without Time Delay
Authors: X. Y. Du, Z. B. Zhang, W. C. Du, G. A. Li, Y. Liu, H. C. Liu
Figure caption: Differential redshift (left panel) and luminosity (right panel) distribution of 95 Swift (red), 41 Fermi (green), and 32 Konus–Wind (blue) SGRBs. Poisson errors have been given to each data point by the error propagation. The solid lines on the right panel mark the best fits to observations with Equation (8).
Paper: Correlations between Event Rates of Short Gamma-Ray Bursts and Star Formation Rates with/without Time Delay
Authors: X. Y. Du, Z. B. Zhang, W. C. Du, G. A. Li, Y. Liu, H. C. Liu
Figure caption: Comparison of the SGRB event rates (step lines) reported by Swift/BAT (left panels), Fermi/GBM (middle panels), and Konus–Wind (right panels) detectors with the undelayed SFR of H. Yüksel et al. (2008) on the top row and the delayed SFRs in descending order for the power-law, lognormal, and Gaussian delay models, respectively. The filled purple circles, triangles, and squares represent the extra components when the SFRs are subtracted from the observed SGRB event rates of three satellites, respectively.
Paper: Correlations between Event Rates of Short Gamma-Ray Bursts and Star Formation Rates with/without Time Delay
Authors: X. Y. Du, Z. B. Zhang, W. C. Du, G. A. Li, Y. Liu, H. C. Liu
Figure caption: Comparison of the observed SGRB rates of Swift (filled circles, panel (a)), Fermi (filled triangles, panel (b)), and Konus–Wind (filled squares, panel (c)) satellites with different kinds of SFRs as shown in Figure 2. The thick lines depict the SFR models related with H. Yüksel et al. (2008), and the thin lines indicate the SFR models derived from P. Madau & M. Dickinson (2014). The purple solid curves represent the best fits to the SGRB rates with Equation (16).
Paper: Correlations between Event Rates of Short Gamma-Ray Bursts and Star Formation Rates with/without Time Delay
Authors: X. Y. Du, Z. B. Zhang, W. C. Du, G. A. Li, Y. Liu, H. C. Liu
Figure caption: Comparison of the SGRB event rates (step lines) reported by Swift/BAT (left panels), Fermi/GBM (middle panels), and Konus–Wind (right panels) detectors with the undelayed SFR of P. Madau & M. Dickinson (2014) on the first line and the delayed SFRs in downward order for the power-law, lognormal, and Gaussian delay models, respectively. All symbols are the same as in Figure 5.
Paper: Correlations between Event Rates of Short Gamma-Ray Bursts and Star Formation Rates with/without Time Delay
Authors: X. Y. Du, Z. B. Zhang, W. C. Du, G. A. Li, Y. Liu, H. C. Liu
Figure caption: The logarithmic relations between the local event rate density and the luminosity of SGRB samples detected by Swift (upper panels), Fermi (middle panels), and Konus–Wind (lower panels) satellites. Two undelayed SFR models of H. Yüksel et al. (2008) and P. Madau & M. Dickinson (2014) and three delayed SFR models built on H. Yüksel et al. (2008) for Gauss, lognormal, and power-law cases are symbolized by filled circles, stars, squares, diamonds, and triangles, respectively. The broken lines stand for the best fits with a triple power-law form of Equation (15).
Paper: Correlations between Event Rates of Short Gamma-Ray Bursts and Star Formation Rates with/without Time Delay
Authors: X. Y. Du, Z. B. Zhang, W. C. Du, G. A. Li, Y. Liu, H. C. Liu
Figure caption: Redshift distributions of differently delayed and undelayed SFRs symbolized with thick (H. Yüksel et al. 2008) and thin (P. Madau & M. Dickinson 2014) lines. All the distributions have been normalized to unity in the local Universe (z = 0). The colorful thick curves represent the best fits with the merged delay models in each. The black curves are given by the undelayed SFRs.
Paper: Decoding Long-duration Gravitational Waves from Binary Neutron Stars with Machine Learning: Parameter Estimation and Equations of State
Authors: Qian Hu, Jessica Irwin, Qi Sun, Christopher Messenger, Lami Suleiman, Ik Siong Heng, John Veitch
Figure caption: Lower left: corner plots for an example BNS event. The blue region shows the posterior distribution generated by MLPResNet embedded model, and the red is from ViT model after importance sampling. The initial samples from the two models are shown in dashed lines in the 1D marginal distributions, and they are not shown in the 2D contours. Gray represents the ground truth result obtained by bilby. The source is a 1.74 + 1.56 M⊙ system located at 2500 Mpc with a network SNR of 35. The true values of the source parameters are shown as black lines, and dashed vertical lines in 1D distributions show the 5% and 95% credible intervals. The chirp mass is presented in the detector frame. Middle right: P–P plots of all PE models. The x-axis is the credible interval and the y-axis the fraction of events included in the corresponding credible interval. Gray confidence regions are of 68%, 95%, and 99.7% confidence levels. Different models are represented by different colors, while parameters for each model are plotted with the same color. The p-values of different parameters are combined and are shown in the legend and indicate no significant statistical biases in the results.
Paper: Decoding Long-duration Gravitational Waves from Binary Neutron Stars with Machine Learning: Parameter Estimation and Equations of State
Authors: Qian Hu, Jessica Irwin, Qi Sun, Christopher Messenger, Lami Suleiman, Ik Siong Heng, John Veitch
Figure caption: BNS frequency-domain waveforms before (blue) and after (orange) heterodyning. Waveforms are truncated at 100 Hz for better illustration. Black dots represent the frequency nodes of the multibanding scheme.
Paper: Decoding Long-duration Gravitational Waves from Binary Neutron Stars with Machine Learning: Parameter Estimation and Equations of State
Authors: Qian Hu, Jessica Irwin, Qi Sun, Christopher Messenger, Lami Suleiman, Ik Siong Heng, John Veitch
Figure caption: 90% confidence intervals (CIs) of EOS constraints for simulated BNS events. The CIs are defined in the 12D representation space and mapped to the pressure–density plane by the autoencoder. Two BNS events, governed by the same EOS, have different network SNRs: 39 (low-SNR, in light blue) and 390 (high-SNR, in blue). The injected EOS is given in dark blue with the training prior bounds given in gray, illustrating the most stiff and most soft EOSs in our training data set.