19 citations · 19 across the 4 of their papers we have counts for
4 papers
Application of Neural Networks for the Reconstruction of Supernova Neutrino Energy Spectra Following Fast Neutrino Flavor Conversions
Sajad Abbar, Meng-Ru Wu, Zewei Xiong
Neutrinos can undergo fast flavor conversions (FFCs) within extremely dense astrophysical environments such as core-collapse supernovae (CCSNe) and neutron star mergers (NSMs). In…
Using Bayesian Inference to Distinguish Neutrino Flavor Conversion Scenarios via a Prospective Supernova Neutrino Signal
Sajad Abbar, Maria Cristina Volpe
The upcoming galactic core-collapse supernova is expected to produce a considerable number of neutrino events within terrestrial detectors. By using Bayesian inference techniques,…
Machine Learning-Based Detection of Non-Axisymmetric Fast Neutrino Flavor Instabilities in Core-Collapse Supernovae
Sajad Abbar, Akira Harada, Hiroki Nagakura
In dense neutrino environments like core-collapse supernovae (CCSNe) and neutron star mergers (NSMs), neutrinos can undergo fast flavor conversions (FFC) when their angular distrib…
Applications of Machine Learning to Detecting Fast Neutrino Flavor Instabilities in Core-Collapse Supernova and Neutron Star Merger Models
Sajad Abbar
Neutrinos propagating in a dense neutrino gas, such as those expected in core-collapse supernovae (CCSNe) and neutron star mergers (NSMs), can experience fast flavor conversions on…