arXiv subjects

Daniel Czech

Publications and source records attributed to Daniel Czech.

21 records · Page 2Linked to original sources

arXiv2019

The Breakthrough Listen Search for Extraterrestrial Intelligence

The discovery of the ubiquity of habitable extrasolar planets, combined with revolutionary advances in instrumentation and observational capabilities, have ushered in a renaissance in the millenia-old quest to answer our most profound question about the Universe and our place within it - Are we alone? The Breakthrough Listen Initiative, announced in July 2015 as a 10-year 100M USD program, is the most comprehensive effort in history to quantify the distribution of advanced, technologically capable life in the universe. In this white paper, we outline the status of the on-going observing campaign with our primary observing facilities, as well as planned activities with these instruments over the next few years. We also list collaborative facilities which will conduct searches for technosignatures in either primary observing mode, or commensally. We highlight some of the novel analysis techniques we are bringing to bear on multi-petabyte data sets, including machine learning tools we are deploying to search for a broader range of technosignatures than was previously possible.

astro-ph.IM↗

arXiv2018

A CNN and LSTM-Based Approach to Classifying Transient Radio Frequency Interference

Transient radio frequency interference (RFI) is detrimental to radio astronomy. It is particularly difficult to identify the sources of transient RFI, which is broadband and intermittent. Such RFI is often generated by devices like mechanical relays, fluorescent lighting or AC machines, which may be present in the surrounding infrastructure of a radio telescope array. One mitigating approach is to deploy independent RFI monitoring stations at radio telescope arrays. Once the sources of RFI signals are identified, they may be removed or replaced where possible. For the first time in the open literature, we demonstrate an approach to classifying the sources of transient RFI (in time domain data) that makes use of deep learning techniques including CNNs and LSTMs. Applied to a previously obtained dataset of experimentally recorded transient RFI signals, our proposed approach offers good results. It shows potential for development into a tool for identifying the sources of transient RFI signals recorded by independent RFI monitoring stations.

eess.SP↗

arXiv2017

A Dictionary Approach to Identifying Transient RFI

As radio telescopes become more sensitive, the damaging effects of radio frequency interference (RFI) become more apparent. Near radio telescope arrays, RFI sources are often easily removed or replaced; the challenge lies in identifying them. Transient (impulsive) RFI is particularly difficult to identify. We propose a novel dictionary-based approach to transient RFI identification. RFI events are treated as sequences of sub-events, drawn from particular labelled classes. We demonstrate an automated method of extracting and labelling sub-events using a dataset of transient RFI. A dictionary of labels may be used in conjunction with hidden Markov models to identify the sources of RFI events reliably. We attain improved classification accuracy over traditional approaches such as SVMs or a naïve kNN classifier. Finally, we investigate why transient RFI is difficult to classify. We show that cluster separation in the principal components domain is influenced by the mains supply phase for certain sources.

astro-ph.IM↗