beta$-Annealed Variational Autoencoder for glitches
Gravitational wave detectors such as LIGO and Virgo are susceptible to various types of instrumental and environmental disturbances known as glitches which can mask and mimic gravitational waves. While there are 22 classes of non-Gaussian noise gradients currently identified, the number of classes i...
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Zusammenfassung: | Gravitational wave detectors such as LIGO and Virgo are susceptible to
various types of instrumental and environmental disturbances known as glitches
which can mask and mimic gravitational waves. While there are 22 classes of
non-Gaussian noise gradients currently identified, the number of classes is
likely to increase as these detectors go through commissioning between
observation runs. Since identification and labelling new noise gradients can be
arduous and time-consuming, we propose $\beta$-Annelead VAEs to learn
representations from spectograms in an unsupervised way. Using the same
formulation as \cite{alemi2017fixing}, we view
Bottleneck-VAEs~cite{burgess2018understanding} through the lens of information
theory and connect them to $\beta$-VAEs~cite{higgins2017beta}. Motivated by
this connection, we propose an annealing schedule for the hyperparameter
$\beta$ in $\beta$-VAEs which has advantages of: 1) One fewer hyperparameter to
tune, 2) Better reconstruction quality, while producing similar levels of
disentanglement. |
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DOI: | 10.48550/arxiv.2107.10667 |