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An Injection System for the CHIME/FRB Experiment

Authors :
Merryfield, Marcus
Tendulkar, S. P.
Shin, Kaitlyn
Andersen, Bridget C.
Josephy, Alexander
Good, Deborah C.
Dong, Fengqiu Adam
Masui, Kiyoshi W.
Lang, Dustin
Münchmeyer, Moritz
Brar, Charanjot
Cassanelli, Tomas
Dobbs, Matt
Fonseca, Emmanuel
Kaspi, Victoria M.
Mena-Parra, Juan
Pleunis, Ziggy
Rafiei-Ravandi, Masoud
Sand, Ketan R.
Scholz, Paul
Smith, Kendrick
Stairs, Ingrid H.
Publication Year :
2022

Abstract

Dedicated surveys searching for Fast Radio Bursts (FRBs) are subject to selection effects which bias the observed population of events. Software injection systems are one method of correcting for these biases by injecting a mock population of synthetic FRBs directly into the realtime search pipeline. The injected population may then be used to map intrinsic burst properties onto an expected signal-to-noise ratio (SNR), so long as telescope characteristics such as the beam model and calibration factors are properly accounted for. This paper presents an injection system developed for the Canadian Hydrogen Intensity Mapping Experiment Fast Radio Burst project (CHIME/FRB). The system was tested to ensure high detection efficiency, and the pulse calibration method was verified. Using an injection population of ~85,000 synthetic FRBs, we found that the correlation between fluence and SNR for injected FRBs was consistent with that of CHIME/FRB detections in the first CHIME/FRB catalog. We also noted that the sensitivity of the telescope varied strongly as a function of the broadened burst width, but not as a function of the dispersion measure. We conclude that some of the machine-learning based Radio Frequency Interference (RFI) mitigation methods used by CHIME/FRB can be re-trained using injection data to increase sensitivity to wide events, and that planned upgrades to the presented injection system will allow for determining a more accurate CHIME/FRB selection function in the near future.<br />Comment: 13 pages, 8 figures. Submitted to AJ

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2206.14079
Document Type :
Working Paper
Full Text :
https://doi.org/10.3847/1538-3881/ac9ab5