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Proceedings Paper

Detecting anomalies in astronomical signals using machine learning algorithms embedded in an FPGA
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Paper Abstract

Taking a large interferometer for radio astronomy, such as the ALMA1 telescope, where the amount of stations (50 in the case of ALMA’s main array, which can extend to 64 antennas) produces an enormous amount of data in a short period of time – visibilities can be produced every 16msec or total power information every 1msec (this means up to 2016 baselines). With the aforementioned into account it is becoming more difficult to detect problems in the signal produced by each antenna in a timely manner (one antenna produces 4 x 2GHz spectral windows x 2 polarizations, which means a 16 GHz bandwidth signal which is later digitized using 3-bits samplers). This work will present an approach based on machine learning algorithms for detecting problems in the already digitized signal produced by the active antennas (the set of antennas which is being used in an observation). The aim of this work is to detect unsuitable, or totally corrupted, signals. In addition, this development also provides an almost real time warning which finally helps stop and investigate the problem in order to avoid collecting useless information.

Paper Details

Date Published: 19 July 2016
PDF: 17 pages
Proc. SPIE 9914, Millimeter, Submillimeter, and Far-Infrared Detectors and Instrumentation for Astronomy VIII, 99143B (19 July 2016); doi: 10.1117/12.2231491
Show Author Affiliations
Alejandro F. Saez, Joint ALMA Observatory (Chile)
Daniel E. Herrera, Joint ALMA Observatory (Chile)

Published in SPIE Proceedings Vol. 9914:
Millimeter, Submillimeter, and Far-Infrared Detectors and Instrumentation for Astronomy VIII
Wayne S. Holland; Jonas Zmuidzinas, Editor(s)

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