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Radiation Transport for Explosive Outflows: Opacity Regrouping

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abstract

Implicit Monte Carlo (IMC) and Discrete Diffusion Monte Carlo (DDMC) are methods used to stochastically solve the radiative transport and diffusion equations, respectively. These methods combine into a hybrid transport-diffusion method we refer to as IMC-DDMC. We explore a multigroup IMC-DDMC scheme that, in DDMC, combines frequency groups with sufficient optical thickness. We term this procedure "opacity regrouping". Opacity regrouping has previously been applied to IMC-DDMC calculations for problems in which the dependence of the opacity on frequency is monotonic. We generalize opacity regrouping to non-contiguous groups and implement this in \supernu, a code designed to do radiation transport in high-velocity outflows with non-monotonic opacities. We find that regrouping of non-contiguous opacity groups generally improves the speed of IMC-DDMC radiation transport. We present an asymptotic analysis that informs the nature of the Doppler shift in DDMC groups and summarize the derivation of the Gentile-Fleck factor for modified IMC-DDMC. We test \supernu\ using numerical experiments including a quasi-manufactured analytic solution, a simple ten-group problem, and the W7 problem for Type Ia supernovae. We find that the opacity regrouping is necessary to make our IMC-DDMC implementation feasible for the W7 problem and possibly Type Ia supernova simulations in general. We compare the bolometric light curves and spectra produced by the \supernu\ and \phoenix\ radiation transport codes for the W7 problem. The overall shape of the bolometric light curves are in good agreement, as are the spectra and their evolution with time. However, for the numerical specifications we considered, we find that the peak luminosity of the light curve calculated using \supernu\ is $\sim$10% less than that calculated using \phoenix.

fields

astro-ph.IM 1

years

2026 1

verdicts

UNVERDICTED 1

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Rapid and robust simulation-based inference for kilonovae

astro-ph.IM · 2026-05-13 · unverdicted · novelty 6.0

Simulation-based inference with a Gaussian process emulator trained on ~1300 POSSIS simulations enables rapid, robust kilonova parameter estimation that avoids MCMC biases from likelihood misspecification.

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  • Rapid and robust simulation-based inference for kilonovae astro-ph.IM · 2026-05-13 · unverdicted · none · ref 87 · internal anchor

    Simulation-based inference with a Gaussian process emulator trained on ~1300 POSSIS simulations enables rapid, robust kilonova parameter estimation that avoids MCMC biases from likelihood misspecification.