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Savoray, Inbar

Publications and source records attributed to Savoray, Inbar.

Supernova Axions Convert to Gamma Rays in Magnetic Fields of Progenitor Stars

It has long been established that axions could have been produced within the nascent proto-neutron star formed following the type II supernova SN1987A, escaped the star due to their weak interactions, and then converted to gamma rays in the Galactic magnetic fields; the nonobservation of a gamma-ray flash coincident with the neutrino burst leads to strong constraints on the axion-photon coupling for axion masses m a ≲10 –10 eV. In this Letter, we use SN1987A to constrain higher mass axions, all the way to m a ~10 –3 eV, by accounting for axion production from the Primakoff process, nucleon bremsstrahlung, and pion conversion along with axion-photon conversion on the still-intact magnetic fields of the progenitor star. Moreover, we show that gamma-ray observations of the next Galactic supernova, leveraging the magnetic fields of the progenitor star, could detect quantum chromodynamics axions for masses above roughly 50 μ⁢eV, depending on the supernova. We propose a new full-sky gamma-ray satellite constellation that we call the GALactic AXion Instrument for Supernova (GALAXIS) to search for such future signals along with related signals from extragalactic neutron star mergers.

79 ASTRONOMY AND ASTROPHYSICS

Learning new physics from data: A symmetrized approach

Thousands of person years have been invested in searches for new physics (NP), the majority of them motivated by theoretical considerations. Yet, no evidence of beyond the Standard Model physics has been found. This suggests that model-agnostic searches might be an important key to explore NP, and help discover unexpected phenomena which can inspire future theoretical developments. A possible strategy for such searches is identifying asymmetries between data samples that are expected to be symmetric within the Standard Model. We propose exploiting neural networks (NNs) to quickly fit and statistically test the differences between two samples. Our method is based on an earlier work, originally designed for inferring the deviations of an observed dataset from that of a much larger reference dataset. We present a symmetric formalism, generalizing the original one, avoiding fine-tuning of the NN parameters and any constraints on the relative sizes of the samples. Our formalism could be used to detect small symmetry violations, extending the discovery potential of current and future particle physics experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS