Deep learning detection of transients. (arXiv:1902.03620v1 [astro-ph.HE])

Deep learning detection of transients. (arXiv:1902.03620v1 [astro-ph.HE])
<a href="http://arxiv.org/find/astro-ph/1/au:+Sadeh_I/0/1/0/all/0/1">Iftach Sadeh</a>

The next generation of observatories will facilitate the discovery of new
types of astrophysical transients. The detection of such phenomena, whose
characteristics are presently poorly constrained, will hinge on the ability to
perform blind searches. We present a new algorithm for this purpose, based on
deep learning. We incorporate two approaches, utilising anomaly detection and
classification techniques. The first is model-independent, avoiding the use of
background modelling and instrument simulations. The second method enables
targeted searches, relying on generic spectral and temporal patterns as input.
We compare our methodology with the existing approach to serendipitous
detection of gamma-ray transients. The algorithm is shown to be more robust,
especially for non-trivial spectral features. We use our framework to derive
the detection prospects of low-luminosity gamma-ray bursts with the upcoming
Cherenkov Telescope Array. Our method is the first unbiased, completely
data-driven approach for multiwavelength and multi-messenger transient
detection.

The next generation of observatories will facilitate the discovery of new
types of astrophysical transients. The detection of such phenomena, whose
characteristics are presently poorly constrained, will hinge on the ability to
perform blind searches. We present a new algorithm for this purpose, based on
deep learning. We incorporate two approaches, utilising anomaly detection and
classification techniques. The first is model-independent, avoiding the use of
background modelling and instrument simulations. The second method enables
targeted searches, relying on generic spectral and temporal patterns as input.
We compare our methodology with the existing approach to serendipitous
detection of gamma-ray transients. The algorithm is shown to be more robust,
especially for non-trivial spectral features. We use our framework to derive
the detection prospects of low-luminosity gamma-ray bursts with the upcoming
Cherenkov Telescope Array. Our method is the first unbiased, completely
data-driven approach for multiwavelength and multi-messenger transient
detection.

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