Banca de DEFESA: TIBÉRIO AZEVEDO PEREIRA

Uma banca de DEFESA de DOUTORADO foi cadastrada pelo programa.
STUDENT : TIBÉRIO AZEVEDO PEREIRA
DATE: 26/01/2023
TIME: 09:00
LOCAL: online
TITLE:

Deep learning anomaly detector for numerical relativistic waveforms

KEY WORDS:

Gravitational Waves, Deep Learning, Numerical Relativity, Anomaly Detection.

PAGES: 52
BIG AREA: Ciências Exatas e da Terra
AREA: Física
SUMMARY:

Gravitational Wave Astronomy is an emerging field revealing hidden information from Astrophysics and Cosmology. The
increasing volume of observational data and Numerical Relativity simulations has promoted several analyzes and modeling
of compact binaries’ gravitational waves. Specially, Machine Learning has become a great support to boost research. In this
project, we developed a U-Net Deep Learning model that detects possible anomalous waveforms in a Numerical Relativity
catalog. We use binary black hole simulations with varying masses and spins. We categorized seven different anomaly types
during the coalescence stages with a dataset of dominant and higher modes waveforms.

COMMITTEE MEMBERS:
Interno - 1674079 - LEO GOUVEA MEDEIROS
Externo à Instituição - LUCIANO CASARINI - UFS
Interno - 1672854 - RAIMUNDO SILVA JUNIOR
Interno - ***.141.018-** - RICCARDO STURANI - UFRN
Externo à Instituição - VALERIO MARRA - UFES