The Atacama Cosmology Telescope: Machine Learning Driven Tools for Detecting Millimeter Sources in Timestream Pre-processing

Abstract

We present a new pipeline utilizing machine learning for classifying short-duration features in raw time-ordered data (TOD) of cosmic microwave background survey observations. The pipeline, specifically designed for the Atacama Cosmology Telescope, works in conjunction with the previous TOD preprocessing techniques that employ statistical thresholding to indiscriminately remove all large spikes in the data, whether they are due to noise features, cosmic rays, or true astrophysical sources, in a process called ``data cuts". This has the undesirable effect of excising real astrophysical sources, including transients, from the data. The classification pipeline demonstrated in this work uses the output from these data cuts and is able to differentiate between electronic noise, cosmic rays, and point sources, enabling the removal of undesired signals while retaining true astrophysical signals during TOD preprocessing. We achieve an overall accuracy of 90\% in categorizing data spikes of different origin and, importantly, 94\% for identifying those caused by astrophysical sources. Our pipeline also measures the amplitude of any detected source seen more than once and produces a subminute-to-minute light curve, providing information on its short timescale variability. This automated pipeline for source detection and amplitude estimation will be particularly useful for upcoming surveys with large data volumes, such as the Simons Observatory.