Rubin-LSST

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:

The analysis of photometric large-scale structure data is often complicated by the need to account for many observational and astrophysical systematics. The elaborate models needed to describe them often introduce many "nuisance parameters’', which can be a major inhibitor of an efficient parameter inference. In this paper we introduce an approximate method to analytically marginalise over a large number of nuisance parameters based on the Laplace approximation. We discuss the mathematics of the method, its relation to concepts such as volume effects and profile likelihood, and show that it can be further simplified for calibratable systematics by linearising the dependence of the theory on the associated parameters. We quantify the accuracy of this approach by comparing it with traditional sampling methods in the context of existing data from the Dark Energy Survey, as well as futuristic Stage-IV photometric data. The linearised version of the method is able to obtain parameter constraints that are virtually equivalent to those found by exploring the full parameter space for a large number of calibratable nuisance parameters, while reducing the computation time by a factor 3-10. Furthermore, the non-linearised approach is able to analytically marginalise over a large number of parameters, returning constraints that are virtually indistinguishable from the brute-force method in most cases, accurately reproducing both the marginalised uncertainty on cosmological parameters, and the impact of volume effects associated with this marginalisation. We provide simple recipes to diagnose when the approximations made by the method fail and one should thus resort to traditional methods. The gains in sampling efficiency associated with this method enable the joint analysis of multiple surveys, typically hindered by the large number of nuisance parameters needed to describe them.