arXiv subjects

Charlie D. Kilpatrick

Publications and source records attributed to Charlie D. Kilpatrick.

2 recordsLinked to original sources

arXiv2025

SN 2022acko and the Properties of its Red Supergiant Progenitor: Direct Detection, Light Curves, and Nebular Spectroscopy

We present ultraviolet, optical, and infrared observations of the Type II-P supernova SN 2022acko in NGC 1300, located at a distance of 19.0 +/- 2.9 Mpc. Our dataset spans 1-350 days post-explosion in photometry, complemented by late-time optical spectroscopy covering 200-600 days, and includes deep pre-explosion imaging. We use this extensive multiwavelength dataset for both direct and indirect constraints on the progenitor system. Using the early-time photometry and shock-cooling models, we infer that SN 2022acko likely originated from a red supergiant with a radius of R ~ 580 solar radii and an initial mass of M ~ 9-10 solar masses. From the radioactive decay tail, we infer a synthesized Ni56 mass of 0.014 +/- 0.004 solar masses. We further model nebular-phase spectra using radiative transfer models and nucleosynthesis yields for core-collapse supernovae, which suggest a progenitor initial mass in the range of 10-15 solar masses. Meanwhile, blackbody fitting of the detected pre-explosion counterpart in the F814W and F160W bands indicates a red supergiant with a lower initial mass of approximately 7.5 solar masses. The light curve exhibits a 116 days plateau, indicative of a massive hydrogen-rich envelope, inconsistent with the pre-explosion analysis. We investigated the discrepancy between direct and indirect progenitor mass estimates, focusing on the roles of binary interaction, early-time modeling limitations, and systematic uncertainties in spectral calibration. Our results indicate that the tension among mass estimates likely arises from modeling limitations and flux calibration uncertainties rather than from insufficient data, highlighting the need for more physically realistic models and a deeper understanding of systematic effects.

astro-ph.HE↗

arXiv2023

Kilonova Spectral Inverse Modelling with Simulation-Based Inference: An Amortized Neural Posterior Estimation Analysis

Kilonovae represent a category of astrophysical transients, identifiable as the electromagnetic observable counterparts associated with the coalescence events of binary systems comprising neutron stars and neutron star-black hole pairs. They act as probes for heavy-element nucleosynthesis in astrophysical environments. These studies rely on inference of the physical parameters (e.g., ejecta mass, velocity, composition) that describe kilonovae based on electromagnetic observations. This is a complex inverse problem typically addressed with sampling-based methods such as Markov-chain Monte Carlo (MCMC) or nested sampling algorithms. However, repeated inferences can be computationally expensive due to the sequential nature of these methods. This poses a significant challenge to ensuring the reliability and statistical validity of the posterior approximations and, thus, the inferred kilonova parameters themselves. We present a novel approach: Simulation-Based Inference (SBI) using simulations produced by KilonovaNet. Our method employs an ensemble of Amortized Neural Posterior Estimation (ANPE) with an embedding network to directly predict posterior distributions from simulated spectral energy distributions (SEDs). We take advantage of the quasi-instantaneous inference time of ANPE to demonstrate the reliability of our posterior approximations using diagnostics tools, including coverage diagnostic and posterior predictive checks. We further test our model with real observations from AT2017gfo, the only kilonova with multi-messenger data, demonstrating agreement with previous likelihood-based methods while reducing inference time down to a few seconds. The inference results produced by ANPE appear to be conservative and reliable, paving the way for testable and more efficient kilonova parameter inference.

astro-ph.HE↗