David Vartanyan
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Articles (10)
Year:
2023
Year:
2022
Year:
2022
Year:
2022
Year:
2022
In this paper, we present a novel method to estimate the time evolution of the proto-neutron star (PNS) structure from the neutrino signal in a core-collapse supernova (CCSN). Employing recent results from multidimensional CCSN simulations, we delve into a relation between the total emitted neutrino energy (TONE) and PNS mass/radius, and we find that they are strongly correlated with each other. We fit the relation by simple polynomial functions connecting the TONE to the mass and radius of the PNS as a function of time. By combining another fitting function representing the correlation between the TONE and the cumulative number of events at each neutrino observatory, the PNS mass and radius can be retrieved from purely observed neutrino data. We demonstrate retrievals of PNS mass and radius from mock data of the neutrino signal, and we assess the capability of our proposed method. While underlining the limitations of the method, we also discuss the importance of the joint analysis with the gravitational wave signal. This would reduce uncertainties of parameter estimations in our method, and may narrow down the possible neutrino oscillation model. The proposed method is a very easy and inexpensive computation, which will be useful in real data analysis of the CCSN neutrino signal.
Year:
2022
Year:
2021
Year:
2021
Year:
2021
Year:
2021
Collaborators (4)
Robert Farmer
Max-Planck-Institut für Astronomie
Matthew Coleman
Teaching Assistant Professor
Stevens Institute of Technology
Ylva Götberg
Assistant Professor
Institute of Science and Technology Austria
Adam Burrows
Professor
Princeton University
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