UR: Acoustic Parameters as Discriminators of Wall Events in PICO Dark Matter Research Data
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University of Northeastern Illinois
This guest post was written by Lucie Volkova. She recently graduated from Northeastern Illinois University. She obtained her bachelor’s degree in physics and plans to complete her master’s and doctorate degrees in astrophysics thereafter. This research was carried out to meet the requirements of the honors and carried out under the supervision of Dr. Orin Harris.
Bubble chambers are one of the many types of detectors that particle physicists use to search for undetected dark matter. The PICO collaboration – a coat rack formed from the fusion of similar PICASSO (Project in Canada to research supersymmetric objects) and COUPP (Chicago Observatory for Subterranean Particle Physics) experiments – runs such a bubble detector using superheated liquid fluorocarbons (with and without iodine). Different measurement modes are used to collect data when a bubble is detected in the fluid: cameras at different angles take sequential images, the pressure of the liquid is monitored, and piezoelectric sensors around the chamber are used to capture the waves. sound and convert them into electric waves. currents.
The bubbles which form along the walls of the chamber exhibit a different behavior from those which nucleate (or form) in the mass of the target liquid due to the shape distortions of the wall boundary; they do not develop symmetrically and have characteristic “tails” unlike normal spherical bubbles. In previous analyzes when looking for dark matter, these wall events were excluded from the data set to control for this different behavior using information available from images or pressure rise data. Seventy-nine dark matter search data events (bubbles) acquired by the PICO-60 bubble chamber in 2016 were visually classified as wall or bulk events and acoustically analyzed. Using the piezoelectric sensors and filtering software, waveform profiles can be created for each bubble, exploring properties such as power at different frequencies and the overall waveform. The aim of this research was to find all the parameters that could reliably distinguish wall events from bulk events for a cleaner analysis.
Wall events were found to differ significantly from mass events using several acoustic parameters, including overall loudness. Acoustic data proved to be a promising indicator of the type of event and gave an efficient and reliable reference cut. In particle physics, the reference volume is the part of the target liquid from which the data will be included and is always smaller than the total volume of the detector. Using these sections, 100% specificity was achieved, meaning that zero wall events were incorrectly characterized as mass events (and, therefore, incorrectly included in dark matter research data) . Up to 92% efficiency has been achieved, meaning that no more than 8% of mass events were incorrectly characterized as wall events (and, therefore, wrongly left outside dark matter research data). These results suggest that a larger volume of target fluid may be considered in future analyzes. It is also essential to have several modes of observation to validate the existing methods used to determine the volume of target fluid. Preliminary results indicate that future analyzes would likely show several percent more efficient fiducialization by taking advantage of acoustic parameters similar to those studied here. This would allow for a larger dataset per detector runtime, possibly leading to earlier detection of dark matter or improved constraints on possible characteristics of dark matter particles such as mass.
Astrobite edited by: Ellis Avallone