Estimating fast transient detection pipeline efficiencies at UTMOST via real-time injection of mock FRBs
| Autor: | Andrew Jameson, Chris Flynn, Adam Deller, T. Bateman, V. Venkatraman Krishnan, A. Sutherland, Wael Farah, Matthew Bailes, A. Mandlik, V. Gupta |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Physics
High Energy Astrophysical Phenomena (astro-ph.HE) education.field_of_study 010308 nuclear & particles physics Population FOS: Physical sciences Astronomy and Astrophysics Unbiased Estimation 01 natural sciences Electromagnetic interference Space and Planetary Science 0103 physical sciences education Molonglo Observatory Synthesis Telescope Astrophysics - Instrumentation and Methods for Astrophysics Astrophysics - High Energy Astrophysical Phenomena 010303 astronomy & astrophysics Algorithm Instrumentation and Methods for Astrophysics (astro-ph.IM) Pulse-width modulation |
| Popis: | Dedicated surveys using different detection pipelines are being carried out at multiple observatories to find more Fast Radio Bursts (FRBs). Understanding the efficiency of detection algorithms and the survey completeness function is important to enable unbiased estimation of the underlying FRB population properties. One method to achieve end-to-end testing of the system is by injecting mock FRBs in the live data-stream and searching for them blindly. Mock FRB injection is particularly effective for machine-learning-based classifiers, for which analytic characterisation is impractical. We describe a first-of-its-kind implementation of a real-time mock FRB injection system at the upgraded Molonglo Observatory Synthesis Telescope (UTMOST) and present our results for a set of 20,000 mock FRB injections. The injections have yielded clear insight into the detection efficiencies and have provided a survey completeness function for pulse width, fluence and DM. Mock FRBs are recovered with uniform efficiency over the full range of injected DMs, however the recovery fraction is found to be a strong function of the width and Signal-to-Noise (SNR). For low widths ($\lesssim 20$ ms) and high SNR ($\gtrsim$ 9) the recovery is highly effective with recovery fractions exceeding 90%. We find that the presence of radio frequency interference causes the recovered SNR values to be systematically lower by up to 20% compared to the injected values. We find that wider FRBs become increasingly hard to recover for the machine-learning-based classifier employed at UTMOST. We encourage other observatories to implement live injection set-ups for similar testing of their surveys. 12 pages, 9 figures. Accepted for publication in MNRAS |
| Databáze: | OpenAIRE |
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