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Abstract:

It has been proposed that core-collapse supernovae (CCSNe) can take place along with the generation of a jet that fails to emerge from the stellar envelope of the progenitor star, i.e., a choked jet. Although the fraction of CCSNe that harbour such jets is unknown, it remains as an interesting possibility that can give rise to the production of high-energy neutrinos. In this work, we focus on the particular case of the recent type II supernova, SN 2023ixf, the closest of its class in the last decade. We describe the particle distributions of protons, pions, and muons in a putative jet applying a simple model to account for the relevant interactions, which are synchrotron cooling and interactions with the soft photon field in the ambient. After evaluating the produced fluence for different values of the viewing angle $i_{\rm j}$ with respect to the jet axis, and comparing with the upper bound by IceCube, we conclude that the generation of a choked jet in SN 2023ixf can not be ruled out. Specifically, for typical jet Lorentz factors of the jet $Γ=100$, the jet could have been produced with an half-opening angle $θ_{\rm op}=0.2\,{\rm rad}$ but for a viewing angle $i_{\rm j}\gtrsim θ_{\rm op}$, no significant Doppler boosting would take place along this direction. Therefore, the choked jet scenario in SN 2023ixf still remains compatible with observations provided our line of sight corresponds to an off-axis view of the jet.16 pages, 7 figures. Matches published versio

Abstract:

When choosing between competing symbolic models for a data set, a human will naturally prefer the “simpler” expression or the one which more closely resembles equations previously seen in a similar context. This suggests a non-uniform prior on functions, which is, however, rarely considered within a symbolic regression (SR) framework. In this paper we develop methods to incorporate detailed prior information on both functions and their parameters into SR. Our prior on the structure of a function is based on a ngram language model, which is sensitive to the arrangement of operators relative to one another in addition to the frequency of occurrence of each operator. We also develop a formalism based on the Fractional Bayes Factor to treat numerical parameter priors in such a way that models may be fairly compared though the Bayesian evidence, and explicitly compare Bayesian, Minimum Description Length and heuristic methods for model selection. We demonstrate the performance of our priors relative to literature standards on benchmarks and a real-world dataset from the field of cosmology.