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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.

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Machine Learning for Stellar Spectra : Anomaly Detection in stellar spectra using Unsupervised Random ForestSpectral Analysis using Variational Autoencoders

Paranjape, Mihir January 2021 (has links)
This thesis was carried out in two parts. The stellar spectral data was used from the Gaia-ESO survey. The data used was fromthe public archive as well as data received from Dr. Recio-Blanco at Observatoire Cote D'Azure. 1) I performed anomaly detection using unsupervised random forests, by applying the concept of weirdness scores to identify outliers. 2) Using spectral data along with physical parameters of objects in the galactic bulge of the Gaia-ESO survey, I built a variational autoencoder neural network to reconstruct stellar spectra and explore latent features learning physical parameters by themselves.
Stellar spectra
outlier
VAE
neural network
machine learning
Gaia-ESO
Astronomy, Astrophysics and Cosmology
Astronomi, astrofysik och kosmologi

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