Paris workshop on Bayesian Deep Learning for Cosmology and Time Domain Astrophysics

Search for ultra-fast radio bursts

poster_s1_3

Non programmé

2m

Buffon Amphitheater (APC laboratory, Université Paris Cité)

Poster + lightning talk Time Domain Astrophysics Lightning talks

Orateur

Kristen Lackeos (Max Planck Institute for Radio Astronomy)

Description

Gaussian phase noise of radio intensity time series is reduced by a factor of n when the raw voltage data are digitally filtered through n orthornormal bandpass eigen-filters, sharing the same intensity bandwidth, and the resulting intensity series are co-added. (Lieu et al. 2020) The algorithm is designed to enhance the sensitivity of detecting ultra-fast radio bursts that would otherwise be smoothed out by time averaging and too faint to be visible in a noisy unaveraged time series. We define ultra-fast to be a timescale on the order of the coherence time of the filtered radiation. We propose to use FETCH, a deep-learning based fast transient classifier, created by Agarwal et al. (2020). Here we present our progress on this front.

References
Lieu, R., et al., CQG, 37, 165001, 2020
Agarwal, D., et al. MNRAS, 497, 1661, 2020