An Injection System for the CHIME/FRB Experiment

Dedicated surveys searching for fast radio bursts (FRBs) are subject to selection effects that 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 real-time search pipeline. The injected population may then be used to map intrinsic burst properties onto an expected signal-to-noise ratio (S/N), 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 similar to 85,000 synthetic FRBs, we found that the correlation between fluence and S/N for injected FRBs was consistent with that of CHIME/FRB detections in the first CHIME/FRB catalog. We 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 mitigation methods used by CHIME/FRB can be retrained 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. We also provide the full injection data sets along with usage tutorials.

Automated summary

This paper introduces an injection system that can correct for selection effects in dedicated surveys searching for fast radio bursts (FRBs). The system injects a synthetic population of FRBs into the real-time search pipeline to map burst properties onto signal-to-noise ratio (S/N). The study found that the correlation between fluence and S/N for the injected FRBs was consistent with the detections in the CHIME/FRB catalog, and suggested retraining machine-learning based Radio Frequency Interference mitigation methods to increase sensitivity to wide events.