Kavli Affiliate: Kiyoshi W. Masui
| First 5 Authors: Antonio Herrera-Martin, Radu V. Craiu, Gwendolyn M. Eadie, David C. Stenning, Derek Bingham
| Summary:
An important task in the study of fast radio bursts (FRBs) remains the
automatic classification of repeating and non-repeating sources based on their
morphological properties. We propose a statistical model that considers a
modified logistic regression to classify FRB sources. The classical logistic
regression model is modified to accommodate the small proportion of repeaters
in the data, a feature that is likely due to the sampling procedure and
duration and is not a characteristic of the population of FRB sources. The
weighted logistic regression hinges on the choice of a tuning parameter that
represents the true proportion $tau$ of repeating FRB sources in the entire
population. The proposed method has a sound statistical foundation, direct
interpretability, and operates with only 5 parameters, enabling quicker
retraining with added data. Using the CHIME/FRB Collaboration sample of
repeating and non-repeating FRBs and numerical experiments, we achieve a
classification accuracy for repeaters of nearly 75% or higher when $tau$ is
set in the range of $50$ to $60$%. This implies a tentative high proportion of
repeaters, which is surprising, but is also in agreement with recent estimates
of $tau$ that are obtained using other methods.
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