arXiv subjects

Justin Clancy

Publications and source records attributed to Justin Clancy.

3 recordsLinked to original sources

arXiv2025

The Simons Observatory: Development of a Pipeline to Detect Rapid Transients in Time-Ordered Data

We introduce a method for detecting astrophysical transients evolving on timescales of milliseconds to minutes using cosmic microwave background (CMB) survey telescopes. While previous transient searches in CMB data operate in map space, our pipeline directly processes the raw time-ordered data, enabling sensitivity to fast, dynamic signals. We integrate our detection approach into the Simons Observatory time-domain pipeline and assess the performance on simulated observations with injected stellar flare-like light curves. For events flaring with a timescale of 0.5 s, the pipeline detects $\gtrsim90$% of events at flux densities of 800, 1150, 1650, and 4250 mJy when measured in the 93, 145, 225, and 280 GHz bands respectively. For longer $\ge5$ second flares, the 90% detection thresholds are reduced by a factor of four. We are able to determine the position of detected events in each observing band, with a positional uncertainty at the detection threshold comparable to the telescope resolution at that band. These results demonstrate the readiness of this pipeline for incorporation into upcoming Simons Observatory data analyses.

astro-ph.IM↗

arXiv2025

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

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.

astro-ph.IM↗

arXiv2022

Snowmass 2021 CMB-S4 White Paper

This Snowmass 2021 White Paper describes the Cosmic Microwave Background Stage 4 project CMB-S4, which is designed to cross critical thresholds in our understanding of the origin and evolution of the Universe, from the highest energies at the dawn of time through the growth of structure to the present day. We provide an overview of the science case, the technical design, and project plan.

astro-ph.CO↗