adsabs/AstroCLIMB huggingface dataset:
Paper: A Panchromatic View of Late-time Shock Power in the Type II Supernova 2023ixf
Authors: W. V. Jacobson-Galán, L. Dessart, C. D. Kilpatrick, P. J. Patel, K. Auchettl, S. Tinyanont, R. Margutti, V. V. Dwarkadas, K. A. Bostroem, R. Chornock, R. J. Foley, H. Abunemeh, T. Ahumada, P. Arunachalam, M. J. Bustamante-Rosell, D. A. Coulter, C. Gall, H. Gao, X. Guo, D. O. Jones, J. Hjorth, M. Kaewmookda, M. M. Kasliwal, R. Kaur, C. Larison, N. LeBaron, H.-Y. Miao, G. Narayan, Y.-C. Pan, S. H. Park, K. C. Patra, Y. Qin, C. L. Ransome, A. Rest, J. Rho, S. Rose, H. Sears, J. J. Swift, K. Taggart, V. A. Villar, Q. Wang, Y. Zenati, H. Zhou
Figure caption: Multiband light curve of SN 2023ixf extending to δt ≈ 700 days and covering UV (polygons), optical (circles), NIR (squares), and MIR (stars) wavelengths. Radioactive decay power model decline rates shown as dashed (complete γ-ray trapping) and solid (incomplete γ-ray trapping) black lines. The decline rate for free–free X-ray emission from CSM interaction is shown as a dotted line. The dominance of shock-powered emission from CSM interaction over absorbed radioactive decay power is observed as excess emission in UV filters at >200 days and optical/NIR filters at >500 days.
Paper: A Panchromatic View of Late-time Shock Power in the Type II Supernova 2023ixf
Authors: W. V. Jacobson-Galán, L. Dessart, C. D. Kilpatrick, P. J. Patel, K. Auchettl, S. Tinyanont, R. Margutti, V. V. Dwarkadas, K. A. Bostroem, R. Chornock, R. J. Foley, H. Abunemeh, T. Ahumada, P. Arunachalam, M. J. Bustamante-Rosell, D. A. Coulter, C. Gall, H. Gao, X. Guo, D. O. Jones, J. Hjorth, M. Kaewmookda, M. M. Kasliwal, R. Kaur, C. Larison, N. LeBaron, H.-Y. Miao, G. Narayan, Y.-C. Pan, S. H. Park, K. C. Patra, Y. Qin, C. L. Ransome, A. Rest, J. Rho, S. Rose, H. Sears, J. J. Swift, K. Taggart, V. A. Villar, Q. Wang, Y. Zenati, H. Zhou
Figure caption: Left: unabsorbed 0.3–10 keV light curve of SN 2023ixf (cyan circles from method 1 and cyan squares from method 2; Section 2.3) compared to SNe 1993J (red polygons) and 1998S (yellow plus signs). The black solid line is the multishock model for SN 1993J from C. Fransson et al. (1996), where rising flux at >100 days is from the emerging RS. Black dotted lines represent the analytic predictions for free–free emission (e.g., Equation (1)) for the FS luminosity assuming ﹩\dot{M}=1{0}^{-4}﹩ M⊙ yr−1 (vw = 20 km s−1). Right: CSM density measurements from A. J. Nayana et al. (2025; blue circles) and modeling of late-time X-ray spectra (red stars). The derived CSM density profile continues to trace a steady-state mass-loss rate of ﹩\dot{M}=1{0}^{-4}﹩ M⊙ yr−1 (solid black line). Dashed gray lines represent lookback times assuming a shock velocity of 104 km s−1 and a CSM velocity of 20 km s−1.
Paper: A Panchromatic View of Late-time Shock Power in the Type II Supernova 2023ixf
Authors: W. V. Jacobson-Galán, L. Dessart, C. D. Kilpatrick, P. J. Patel, K. Auchettl, S. Tinyanont, R. Margutti, V. V. Dwarkadas, K. A. Bostroem, R. Chornock, R. J. Foley, H. Abunemeh, T. Ahumada, P. Arunachalam, M. J. Bustamante-Rosell, D. A. Coulter, C. Gall, H. Gao, X. Guo, D. O. Jones, J. Hjorth, M. Kaewmookda, M. M. Kasliwal, R. Kaur, C. Larison, N. LeBaron, H.-Y. Miao, G. Narayan, Y.-C. Pan, S. H. Park, K. C. Patra, Y. Qin, C. L. Ransome, A. Rest, J. Rho, S. Rose, H. Sears, J. J. Swift, K. Taggart, V. A. Villar, Q. Wang, Y. Zenati, H. Zhou
Figure caption: Time-series optical/NIR (black/red) spectra of SN 2023ixf spanning δt = 272–708 days. Ion identifications shown in blue are based on simulations from L. Dessart et al. (2023). Spectra at δt > 619 days are dominated by broad, “boxy” line profiles derived from emission in the dense shell and inner ejecta as a radiative RS is powered by ongoing CSM interaction.
Paper: A Panchromatic View of Late-time Shock Power in the Type II Supernova 2023ixf
Authors: W. V. Jacobson-Galán, L. Dessart, C. D. Kilpatrick, P. J. Patel, K. Auchettl, S. Tinyanont, R. Margutti, V. V. Dwarkadas, K. A. Bostroem, R. Chornock, R. J. Foley, H. Abunemeh, T. Ahumada, P. Arunachalam, M. J. Bustamante-Rosell, D. A. Coulter, C. Gall, H. Gao, X. Guo, D. O. Jones, J. Hjorth, M. Kaewmookda, M. M. Kasliwal, R. Kaur, C. Larison, N. LeBaron, H.-Y. Miao, G. Narayan, Y.-C. Pan, S. H. Park, K. C. Patra, Y. Qin, C. L. Ransome, A. Rest, J. Rho, S. Rose, H. Sears, J. J. Swift, K. Taggart, V. A. Villar, Q. Wang, Y. Zenati, H. Zhou
Figure caption: Late-time SED of SN 2023ixf covering radio, infrared, optical, UV, and X-ray wavelengths at δt ≈ 165–585 days. Emission mechanisms and power sources are labeled at the top of the figure.
Paper: Separating Flare and Secondary Atmospheric Signals with RADYN Modeling of Near-infrared JWST Transmission Spectroscopy Observations of TRAPPIST-1
Authors: Ward S. Howard, Adam F. Kowalski, Michael Radica, Laura Flagg, Valeriy Vasilyev, Benjamin V. Rackham, Guadalupe Tovar Mendoza, Meredith A. MacGregor, Alexander I. Shapiro, Jake Taylor, Louis-Philippe Coulombe, Olivia Lim, David Lafrenière
Figure caption: Illustration of transmission spectrum injection and recovery testing procedure using measured χ2 (left panels) and Δχ2 (right panels) distributions. The J. Lustig-Yaeger et al. (2019) TRAPPIST-1b 10 bar CO2 atmospheric transmission spectrum is scaled by 0.6, 1.2, and 1.8 times and injected into the precorrection spectrum of all integrations. Bootstrapped χ2 values are recorded for linear and model atmosphere fits to the detrended mean flare spectrum under four scenarios: (i) a linear model fit to the spectrum with the injected signal; (ii) an atmosphere model fit to the spectrum with the injected signal; (iii) a linear model fit to the spectrum in the absence of any injected signals; and (iv) an atmospheric model fit to the spectrum in the absence of any injected signals. The presence of correlated noise in the no-atmosphere samples is highlighted by comparison to the random-variable χ2 function with the same 13 degrees of freedom. Hypothesis tests for atmosphere versus linear fits are therefore computed using Δχ2 distributions where the correlated noise baselines have been subtracted.
Paper: Separating Flare and Secondary Atmospheric Signals with RADYN Modeling of Near-infrared JWST Transmission Spectroscopy Observations of TRAPPIST-1
Authors: Ward S. Howard, Adam F. Kowalski, Michael Radica, Laura Flagg, Valeriy Vasilyev, Benjamin V. Rackham, Guadalupe Tovar Mendoza, Meredith A. MacGregor, Alexander I. Shapiro, Jake Taylor, Louis-Philippe Coulombe, Olivia Lim, David Lafrenière
Figure caption: Top: residual flare contamination of 60–140 ppm in the R = 10 average detrended spectrum of all 189 integrations following application of the empirical (left, blue) and RADYN-model-based (gold, right) pipelines. Bottom: detection significance versus transmission signal strength for the detrended spectra, derived from injection and recovery tests of J. Lustig-Yaeger et al. (2019) CO2 atmospheric transmission signals scaled from 12 to 480 ppm. The estimated BFs of 3, 12, and 150 represent “weak,” “moderate,” and “strong” detections (R. Trotta 2008), respectively. The detection significance curve using the simple Planck fit approach from W. S. Howard et al. (2023) is shown for reference (gray).
Paper: Separating Flare and Secondary Atmospheric Signals with RADYN Modeling of Near-infrared JWST Transmission Spectroscopy Observations of TRAPPIST-1
Authors: Ward S. Howard, Adam F. Kowalski, Michael Radica, Laura Flagg, Valeriy Vasilyev, Benjamin V. Rackham, Guadalupe Tovar Mendoza, Meredith A. MacGregor, Alexander I. Shapiro, Jake Taylor, Louis-Philippe Coulombe, Olivia Lim, David Lafrenière
Figure caption: Left: best-fit RADYN model and uncertainty region for the F1 peak spectrum, determined from all models with residuals within 100% of the best-fit value. The XUV emission driving atmospheric escape, FUV–NUV emission driving photochemistry, and TESS wavelengths are highlighted. The prediction capability degrades with distance from the NIR. Middle: RADYN SEDs enable scaling of the TESS-band flare rate into the XUV, FUV, and NUV. Right: comparison of flaring and quiescent contributions to the XUV radiation environment, where the quiescent relation is reproduced from D. P. Fleming et al. (2020). The flaring XUV activity–age relation is constructed from the present-day XUV flare rate, the estimated saturation flare rate relative to the present day (R. R. Paudel et al. 2018), and the estimated ∼4 Gyr saturation knee (D. P. Fleming et al. 2020).
Paper: Separating Flare and Secondary Atmospheric Signals with RADYN Modeling of Near-infrared JWST Transmission Spectroscopy Observations of TRAPPIST-1
Authors: Ward S. Howard, Adam F. Kowalski, Michael Radica, Laura Flagg, Valeriy Vasilyev, Benjamin V. Rackham, Guadalupe Tovar Mendoza, Meredith A. MacGregor, Alexander I. Shapiro, Jake Taylor, Louis-Philippe Coulombe, Olivia Lim, David Lafrenière
Figure caption: Top: flare Teff time series for all 189 integrations in our GTI sample. Bottom: combined RADYN and quiescent fits for a range of fiducial flare temperatures. We note the addition of the PHOENIX component is not intended to claim these features are physically correlated with the RADYN continuum, only that the combined fit better describes the flare spectrum when quiescent bleed-through is present.
Paper: Separating Flare and Secondary Atmospheric Signals with RADYN Modeling of Near-infrared JWST Transmission Spectroscopy Observations of TRAPPIST-1
Authors: Ward S. Howard, Adam F. Kowalski, Michael Radica, Laura Flagg, Valeriy Vasilyev, Benjamin V. Rackham, Guadalupe Tovar Mendoza, Meredith A. MacGregor, Alexander I. Shapiro, Jake Taylor, Louis-Philippe Coulombe, Olivia Lim, David Lafrenière
Figure caption: Common-mode spectra computed from the fiducial temperature flare spectra, shown for each combination of NIRISS and NIRSpec and empirical and RADYN-based pipelines. The smooth evolution of common-mode spectra with temperature motivates our interpolation of common modes from the grid values. Also note the dip in the NIRSpec modes at ∼1.4 μm
Paper: JWST Discovery of Strong Lensing from a Galaxy Cluster at Cosmic Noon: Giant Arcs and a Highly Concentrated Core of XLSSC 122
Authors: Kyle Finner, Sangjun Cha, Zachary P. Scofield, M. James Jee, Yu-heng Lin, Hyungjin Joo, Hyosun Park, Takahiro Morishita, Andreas Faisst, Bomee Lee, Wuji Wang, Ranga-Ram Chary
Figure caption: The left panels show the color-composite image (top) and denoised color-composite image (bottom) of the core of galaxy cluster XLSSC 122 with red, green, and blue colors from F356W, F277W, and F090W+F200W, respectively. The denoised image was produced using a Transformer-based reconstruction method (H. Park et al. 2024; Z. P. Scofield et al. 2025). The middle and right panels show the log-scale, single-filter images as labeled. The cluster galaxies are much fainter in the F090W filter because, at z = 1.98, the filter probes rest-frame wavelengths below the 4000 Å break. The candidate strong-lensing arcs are labeled with purple arrows in the top-left image and are visible in all filters. The intracluster light is prominent in the redder filters and stretches from the BCG to the southwest (H. Joo et al. 2025, in preparation).
Paper: JWST Discovery of Strong Lensing from a Galaxy Cluster at Cosmic Noon: Giant Arcs and a Highly Concentrated Core of XLSSC 122
Authors: Kyle Finner, Sangjun Cha, Zachary P. Scofield, M. James Jee, Yu-heng Lin, Hyungjin Joo, Hyosun Park, Takahiro Morishita, Andreas Faisst, Bomee Lee, Wuji Wang, Ranga-Ram Chary
Figure caption: Left: radial mass density profile (black circles) from the strong-lensing model with best-fit NFW relation (black curve). Mass–concentration relations (dashed curves) from literature are fit to the radial profile. Most mass–concentration relations are unable to reproduce the radial profile of XLSSC 122, leading to an overestimation of the mass and an underestimation of the concentration (the model of F. Prada et al. 2012 being an exception). Reduced chi-square ﹩{\chi }_{\nu }^{2}﹩ is presented for each model. Right: concentration as a function of mass at z = 0 (thin dashed curves) and z = 1.98 (thick solid curves) for the same mass–concentration relations. The black circle marks XLSSC 122 with uncertainties from our best-fit model. The fitted masses and concentrations of other models are plotted with circles of their respective color.
Paper: JWST Discovery of Strong Lensing from a Galaxy Cluster at Cosmic Noon: Giant Arcs and a Highly Concentrated Core of XLSSC 122
Authors: Kyle Finner, Sangjun Cha, Zachary P. Scofield, M. James Jee, Yu-heng Lin, Hyungjin Joo, Hyosun Park, Takahiro Morishita, Andreas Faisst, Bomee Lee, Wuji Wang, Ranga-Ram Chary
Figure caption: Galaxy cluster mass as a function of redshift for the SPT, ACT, and Planck surveys (open circles). Filled circles mark the galaxy clusters that have strong-lensing analyses from the CLASH, HFF, RELICS, and SLICE surveys. The black dashed line is the 95% full-sky exclusion curve that separates expected (below) versus unexpected (above) cluster masses in the Universe. XLSSC 122 (mass from ACT) straddles the exclusion curve and sits at a substantially higher redshift than the galaxy clusters in the previous strong-lensing surveys.
Paper: JWST Discovery of Strong Lensing from a Galaxy Cluster at Cosmic Noon: Giant Arcs and a Highly Concentrated Core of XLSSC 122
Authors: Kyle Finner, Sangjun Cha, Zachary P. Scofield, M. James Jee, Yu-heng Lin, Hyungjin Joo, Hyosun Park, Takahiro Morishita, Andreas Faisst, Bomee Lee, Wuji Wang, Ranga-Ram Chary
Figure caption: Left: our parametric strong-lensing convergence map (magenta contours) overplotted on the color image of XLSSC 122. Right: strong-lensing critical curves (cyan contours) for a source at z = 3.8 plotted over the denoised color image. Multiple-image groups are marked with open circles and labeled with their identifier.
Paper: Modeling Atmospheric Ion Escape from Kepler-1649 b and c over Time
Authors: Haitao Li, Chuanfei Dong, Lianghai Xie, Xinyi He, Laura Chin, Xinke Wang, Hong-Liang Yan, Jinxiao Qin, Nathan Mayne, Mei Ting Mak, Nikolaos Georgakarakos, Duncan Christie, Yajun Zhu, Zhaojin Rong, Jinlian Ma, Xiaobo Li, Shi Chen, Hai Zhou
Figure caption: Evolution of ion escape rates for Kepler-1649 b (left panels) and c (right panels) across stellar ages (0.8–4.0 Gyr). Top to bottom: O+, ﹩{{\rm{O}}}_{2}^{+}﹩, ﹩{{\rm{CO}}}_{2}^{+}﹩, and total ion escape rates (s−1). The data points were nonparametrically regressed using the LOWESS method (solid lines).
Paper: Modeling Atmospheric Ion Escape from Kepler-1649 b and c over Time
Authors: Haitao Li, Chuanfei Dong, Lianghai Xie, Xinyi He, Laura Chin, Xinke Wang, Hong-Liang Yan, Jinxiao Qin, Nathan Mayne, Mei Ting Mak, Nikolaos Georgakarakos, Duncan Christie, Yajun Zhu, Zhaojin Rong, Jinlian Ma, Xiaobo Li, Shi Chen, Hai Zhou
Figure caption: The prediction of evolution of transmission spectra for Kepler-1649 b at nine key evolutionary ages from 0.8 to 4.0 Gyr. Rp is the solid radius of planet, Hatm is the opaque component of the atmosphere, and Rstar is the radius of star.
Paper: Early Evidence for Isotropic Planetary Obliquities in Young Super-Jupiter Systems
Authors: Michael Poon, Marta L. Bryan, Hanno Rein, Jiayin Dong, Joshua S. Speagle, 佳士 沈, Dang Pham
Figure caption: Three illustrations highlighting the relative orientation of a planet's spin axis ﹩{\hat{{\boldsymbol{n}}}}_{p}﹩ and orbit normal ﹩{\hat{{\boldsymbol{n}}}}_{o}﹩: (a) as a cartoon, (b) in an orbit-oriented coordinate system, with the orbit normal along the Z-axis, and (c) in an observer-oriented coordinate system, with the observer along the ﹩{X}^{{\prime} }﹩-axis, and ﹩{Y}^{{\prime} }-{Z}^{{\prime} }﹩ as the sky plane. Panels (b) and (c) are related by a 90∘ − io rotation along the ﹩Y={Y}^{{\prime} }﹩-axis. These diagrams were inspired by J. Dong & D. Foreman-Mackey (2023), and adapted from M. Poon et al. (2024a).
Paper: Modeling Atmospheric Ion Escape from Kepler-1649 b and c over Time
Authors: Haitao Li, Chuanfei Dong, Lianghai Xie, Xinyi He, Laura Chin, Xinke Wang, Hong-Liang Yan, Jinxiao Qin, Nathan Mayne, Mei Ting Mak, Nikolaos Georgakarakos, Duncan Christie, Yajun Zhu, Zhaojin Rong, Jinlian Ma, Xiaobo Li, Shi Chen, Hai Zhou
Figure caption: Stellar spectra at Kepler-1649 b and c, and also solar spectra at Venus and Earth for comparison. Panel (a): flux (W m−2 μm) for Kepler-1649 at the orbital distances of planets b (0.0514 au, green line) and c (0.0882 au, orange line), compared to the solar spectrum at Venus (red line) and Earth (purple line). Kepler-1649’s spectrum is scaled from Proxima Centauri (M5.5V), matching its spectral type. Panels (b) and (c): the temporal evolution of the XUV flux (0.7 nm–118 nm, W m−2 μm) for Kepler-1649 at the orbital distances of planets b and c, respectively, spanning stellar ages of 0.8–4.0 Gyr.
Paper: Modeling Atmospheric Ion Escape from Kepler-1649 b and c over Time
Authors: Haitao Li, Chuanfei Dong, Lianghai Xie, Xinyi He, Laura Chin, Xinke Wang, Hong-Liang Yan, Jinxiao Qin, Nathan Mayne, Mei Ting Mak, Nikolaos Georgakarakos, Duncan Christie, Yajun Zhu, Zhaojin Rong, Jinlian Ma, Xiaobo Li, Shi Chen, Hai Zhou
Figure caption: Logarithmic ion number density (cm−3) distributions of O+ (left column) and ﹩{{\rm{O}}}_{2}^{+}﹩ (right column) in the X–Z-plane for Kepler-1649 b and c’s dayside at 0.8 Gyr (top row) and 4.0 Gyr (bottom row). Coordinates are normalized to the planetary radius RP.
Paper: Modeling Atmospheric Ion Escape from Kepler-1649 b and c over Time
Authors: Haitao Li, Chuanfei Dong, Lianghai Xie, Xinyi He, Laura Chin, Xinke Wang, Hong-Liang Yan, Jinxiao Qin, Nathan Mayne, Mei Ting Mak, Nikolaos Georgakarakos, Duncan Christie, Yajun Zhu, Zhaojin Rong, Jinlian Ma, Xiaobo Li, Shi Chen, Hai Zhou
Figure caption: Logarithmic ion number density (cm−3) distributions in the X–Z-plane for Kepler-1649 b and c at different stellar ages (0.8– 4.0 Gyr; top to bottom). Columns show O+, ﹩{{\rm{O}}}_{2}^{+}﹩, and ﹩{{\rm{CO}}}_{2}^{+}﹩ (left to right). Coordinates are normalized to planetary radius RP.
Paper: Early Evidence for Isotropic Planetary Obliquities in Young Super-Jupiter Systems
Authors: Michael Poon, Marta L. Bryan, Hanno Rein, Jiayin Dong, Joshua S. Speagle, 佳士 沈, Dang Pham
Figure caption: Graphical representation of a hierarchical model using quantities depicted in Figure 1. The arrows represent the direction of data generation from κ, the population-level obliquity distribution parameter, to ψi, the obliquity of planet i, to the planet’s observed variables ﹩{\hat{i}}_{p}﹩ and ﹩{\hat{i}}_{o}﹩.
Paper: Modeling Atmospheric Ion Escape from Kepler-1649 b and c over Time
Authors: Haitao Li, Chuanfei Dong, Lianghai Xie, Xinyi He, Laura Chin, Xinke Wang, Hong-Liang Yan, Jinxiao Qin, Nathan Mayne, Mei Ting Mak, Nikolaos Georgakarakos, Duncan Christie, Yajun Zhu, Zhaojin Rong, Jinlian Ma, Xiaobo Li, Shi Chen, Hai Zhou
Figure caption: The FXUV profile with time for Kepler-1649, spanning stellar ages of 0.8–4.0 Gyr.
Paper: Where did Heavy Binaries Go? Gravitational-wave Populations Using Delaunay Triangulation with Optimized Complexity
Authors: Rodrigo Tenorio, Alexandre Toubiana, Tristan Bruel, Davide Gerosa, Jonathan R. Gair
Figure caption: Delaunay triangulation to model the differential merger rate ﹩{\mathrm{log}}_{10}{d}_{\theta }N﹩ across two variables (on the horizontal and vertical axes). The central red dot represents a location θ where the rate needs to be computed. The highlighted area S(θ) represents the triangle (simplex in higher dimensions) containing θ whose vertices vi and weights wi are inferred from the data. The position of the four corners is fixed in advance, and their weights Wi are inferred from the data. The rate ﹩{\mathrm{log}}_{10}{d}_{\theta }N﹩ is computed by interpolating the weights at the vertices of S(θ) using the barycentric coordinates bi(θ) associated to θ.
Paper: Where did Heavy Binaries Go? Gravitational-wave Populations Using Delaunay Triangulation with Optimized Complexity
Authors: Rodrigo Tenorio, Alexandre Toubiana, Tristan Bruel, Davide Gerosa, Jonathan R. Gair
Figure caption: Left: posterior distribution of the volumetric differential rate reconstructed using Delaunay triangulation for two representative redshift values, z = 0.2 (purple) and z = 1 (orange). Solid curves indicate the medians, while shaded regions encompass 90% symmetric credible intervals. The upper panel shows the DKL between the posterior and prior volumetric differential rate distributions. Right: posterior distribution of the ratio of the volumetric differential rate at the same two representative redshift values. The black solid curve denotes the posterior median. The shaded region contains the symmetric 90% credible interval. The horizontal red line corresponds to ﹩{{d}}_{\theta }{ \mathcal R }(z=0.2)={{d}}_{\theta }{ \mathcal R }(z=1)﹩. The dotted line corresponds to the 1.5% credibility interval. The observed difference in rates is thus inconsistent with no evolution of the rate with z at ∼98% credibility but remains consistent with a mass-uniform evolution (the envelope is consistent with a straight line).
Paper: Where did Heavy Binaries Go? Gravitational-wave Populations Using Delaunay Triangulation with Optimized Complexity
Authors: Rodrigo Tenorio, Alexandre Toubiana, Tristan Bruel, Davide Gerosa, Jonathan R. Gair
Figure caption: The color scale shows the median (50% quantile) posterior volumetric rate inferred using Delaunay triangulation on GWTC-4.0. The white contours indicate the relative uncertainty on the rate itself, defined as the ratio between the 95% quantile and the 5% quantile.
Paper: Where did Heavy Binaries Go? Gravitational-wave Populations Using Delaunay Triangulation with Optimized Complexity
Authors: Rodrigo Tenorio, Alexandre Toubiana, Tristan Bruel, Davide Gerosa, Jonathan R. Gair
Figure caption: Evolution of the volumetric differential merger rate as a function of cosmic time t in log–log scale evaluated at Mtot = 63 M⊙, i.e., a nominal location where evolution with redshift is tightly constrained. The solid curve indicates the median, while the shaded region encloses the 90% symmetric credible interval. The dashed (dotted) line corresponds to a merger rate evolving as a power law with index –1 (–4) and an arbitrary normalization for reference.
Paper: Where did Heavy Binaries Go? Gravitational-wave Populations Using Delaunay Triangulation with Optimized Complexity
Authors: Rodrigo Tenorio, Alexandre Toubiana, Tristan Bruel, Davide Gerosa, Jonathan R. Gair
Figure caption: Left: posterior predictive distribution of the mass-ratio distribution. The red (gray) solid line represents the median, while the shaded (dotted) envelope contains the 90% credible interval assuming a broken (single) power-law distribution. Right: posterior distribution of the power-law indices. The gray area represents the posterior probability on (α1, α2) for the broken power-law model, while the dotted histogram represents the posterior probability on α1 for the single power-law model.
Paper: Where did Heavy Binaries Go? Gravitational-wave Populations Using Delaunay Triangulation with Optimized Complexity
Authors: Rodrigo Tenorio, Alexandre Toubiana, Tristan Bruel, Davide Gerosa, Jonathan R. Gair
Figure caption: Joint reconstruction of the (m1, z) BBH volumetric merger rate, mirroring Figure 2 in the main body of the Letter. A constant rate is excluded with 95% credibility (i.e., the solid red line is included in the 90% symmetric credible interval, here shaded in gray).
Paper: Assembling GW231123 in Star Clusters through the Combination of Stellar Binary Evolution and Hierarchical Mergers
Authors: Lavinia Paiella, Cristiano Ugolini, Mario Spera, Marica Branchesi, Manuel Arca Sedda
Figure caption: Distribution of primary BH generations for simulated BBH mergers with component masses in the 90% CI of GW231123. We distinguish between models with metallicity Z = 0.002 (filled steps) or Z = 0.0002 (open steps) and among YCs (blue steps), GCs (purple steps), and NCs (orange steps).