0000000000329492
AUTHOR
António P. Morais
showing 3 related works from this author
Gravitational-wave parameter inference using Deep Learning
2021
We explore machine learning methods to detect gravitational waves (GW) from binary black hole (BBH) mergers using deep learning (DL) algorithms. The DL networks are trained with gravitational waveforms obtained from BBH mergers with component masses randomly sampled in the range from 5 to 100 solar masses and luminosity distances from 100 Mpc to, at least, 2000 Mpc. The GW signal waveforms are injected in public data from the O2 run of the Advanced LIGO and Advanced Virgo detectors, in time windows that do not coincide with those of known detected signals, and the data from each detector in the Advanced LIGO and Advanced Virgo network is combined into a unique RGB image. We show that a clas…
Gravitational footprints of massive neutrinos and lepton number breaking
2020
We investigate the production of primordial Gravitational Waves (GWs) arising from First Order Phase Transitions (FOPTs) associated to neutrino mass generation in the context of type-I and inverse seesaw schemes. We examine both "high-scale" as well as "low-scale" variants, with either explicit or spontaneously broken lepton number symmetry $U(1)_L$ in the neutrino sector. In the latter case, a pseudo-Goldstone majoron-like boson may provide a candidate for cosmological dark matter. We find that schemes with softly-broken $U(1)_L$ and with single Higgs-doublet scalar sector lead to either no FOPTs or too weak FOPTs, precluding the detectability of GWs in present or near future measurements.…
Exploring gravitational-wave detection and parameter inference using deep learning methods
2020
The data that support the findings of this study are openly available at the following URL/DOI: https://arxiv.org/abs/2011.10425.