Analysis and benchmarking for gravitational waves spectrogram’s classification by usage of machine learning techniques

Martínez, Ezequiel H.

trabajo final de especialización.page.titleprefix
Analysis and benchmarking for gravitational waves spectrogram’s classification by usage of machine learning techniques

dc.contributor.advisor	Ramele, Rodrigo
dc.contributor.author	Martínez, Ezequiel H.
dc.date.accessioned	2021-01-26T14:51:23Z
dc.date.available	2021-01-26T14:51:23Z
dc.date.issued	2020-09-06
dc.description.abstract	"Gravitatonal waves, the seed of the 2015 Nobel’s prize are the cause of several complex celestial phenomena that is non-observable for the naked eye. Their identification, classification and study is s(ll a handmade work which is s(ll nascent. There has been several approaches to produce novel tools to aid the scientists behind the discovery of these deep space events. One of the most thrilling examples has been the usage of artificial intelligence classification to aid in the preiden identification of certain signals. We took one of these tools, Gravity Spy, and study its base paper, trying to reproduce some of their classification results using the very same base dataset. This research aims to compare the results obtained from the original paper, with a binary classification approach and several different algorithms taken from the knowledge base of machine learning and deep learning, alike. We confirmed the original paper results and obtained a new approach for the same solution. In this study we trained several models that could be used for further development of an eventual alternative engines for gravita(onal waves signal’s classification or any other sort of signal heavily influenced by noise and analysed by spectrograms."	en
dc.description.notes	Trabajo Final Ciencia de Datos (especialización) - Instituto Tecnológico de Buenos Aires, Buenos Aires, 2020	es
dc.identifier.uri	http://ri.itba.edu.ar/handle/123456789/3351
dc.language.iso	en	en
dc.subject	APRENDIZAJE AUTOMATICO	es
dc.subject	PROCESAMIENTO DE SEÑALES	es
dc.subject	CLASIFICACION	es
dc.subject	ONDAS GRAVITACIONALES	es
dc.title	Analysis and benchmarking for gravitational waves spectrogram’s classification by usage of machine learning techniques	en
dc.title	Comparativa de rendimiento de clasificacion de espectrogramas de ondas gravitacionales mediante la utilizacion de tecnicas de machine learning	es
dc.type	Trabajo final de especialización	es
dspace.entity.type	Trabajo final de especialización
itba.description.filiation	Fil: Martínez, Ezequiel H. Instituto Tecnológico de Buenos Aires; Argentina.