Bulletin of the American Physical Society
Mid-Atlantic Section 2022 Meeting
Volume 67, Number 20
Friday–Sunday, December 2–4, 2022; University Park, PA, Pennsylvania State University
Session E02: High Energy Particle Astrophysics |
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Chair: Abaz Kryemadhi, Messiah University Room: Pennsylvania State University Osmond 104 |
Saturday, December 3, 2022 2:00PM - 2:35PM |
E02.00001: Title: Mysterious PeVatrons – where are our Galaxy's most powerful accelerators hiding? Invited Speaker: Henrike Fleischhack
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Saturday, December 3, 2022 2:35PM - 3:10PM |
E02.00002: Particle and Astrophysics of Ultra-High-Energy Cosmic Rays Invited Speaker: Frank G Schroeder Ultra-High-Energy Cosmic Rays (UHECR) are at the intersection of the cosmic and energy frontiers of physics. This talk will summarize a whitepaper that has been prepared with input for the UHECR community for the Snowmass decadal survey [arXiv:2205.05845]. It provides an overview over the state-of-the-art, open questions regarding the astrophysics and particle physics of cosmic rays, and future experiments targeting these questions. |
Saturday, December 3, 2022 3:10PM - 3:22PM |
E02.00003: Cosmic Ray Propagation Studies With GALPROP Yuca Chen, Eun-Suk Seo, Hongyi Wu Various space-based and balloon-borne experiments have reported significant spectral hardening of cosmic ray data at around 200 GV. This spectral feature is seen in many different cosmic ray spectra, including primary and secondary elements. The numerical cosmic-ray propagation code GALPROP was used to investigate this spectral hardening. The Plain Diffusion model with reacceleration effects serves as a base model. Using rigidity-dependent parameters, three cases were studied: one with a diffusion coefficient break, one with source spectrum breaks, and one with a combination of both effects. An additional positron source was also investigated to explain the positron excess above ~27 GeV. Comparisons of elemental spectra and ratios for the three cases will be presented. The limitations of these three cases and plans for improvements will also be discussed. |
Saturday, December 3, 2022 3:22PM - 3:34PM |
E02.00004: Trigger Correlation Schemes and Projected Sensitivity of the Low Frequency Instrument of the PUEO Experiment Dylan Monteiro, Stephanie A Wissel, Yuchieh Ku The Payload for Ultrahigh Energy Observations (PUEO) is a long duration balloon experiment designed to observe ultra-high energy neutrinos (UHEN) with energies above EeV. PUEO's primary mechanism of detection will be the use radio antennas to receive signals emitted by Askaryan radiation that UHEN produce while passing through the ice shelf of Antarctica, in addition to signals emitted by Exentsive Air Shower (EAS) events of a UHEN causing a particle shower in the Earth's atmosphere. PUEO will have two detectors, the Main Instrument (MI) which consists of 256 horn atennas operating at 300-1200 MHz, and the Low Frequency (LF) Instrument which consists of 8 antennas operating at 50-300 MHz. Simulation studies have been ongoing to estimate the expected performance of the LF Instrument in its sensitivity to EAS and Askaryan signals. Computational efforts have made use of monte-carlo event generators in conjunction with detailed models of the various components of the LF antenna array to predict the sensitivity it may add to PUEO. In addition to the individual LF sensitivity, great consideration has been made to the various trigger schemes possible to correlate the LF array trigger with the MI array trigger. Use of AND schemes (Both detectors triggered), OR schemes, and others have been evaluated to decide which may be most significant in increasing overall UHEN sensitivity, in addition to providing the most information to aid in event reconstruction efforts. |
Saturday, December 3, 2022 3:34PM - 3:46PM |
E02.00005: Machine Learning for Classification and Denoising of Cosmic-Ray Radio Signals Dana Kullgren, Abdul Rehman, Alan Coleman, Frank G Schroeder Geomagnetic deflection of oppositely charged particles in cosmic-ray air showers produces radio signals that can be detected by antennas of a prototype surface station at the IceCube Neutrino Observatory. However, these antennas also measure thermal noise, human-made radio frequency interferences, and the continuous Galactic background which makes it challenging to separate cosmic-ray radio signals from this background. In this work, we employ convolutional neural networks (CNNs) with two goals in mind: (1) the identification of waveforms that contain cosmic-ray radio signals as opposed to waveforms that contain only background and (2) removing backgrounds from the waveform to reproduce the pure signal. The datasets required to train these models include signal simulations from the CoREAS Monte Carlo code as well as noise waveforms measured with the antennas at the IceCube Neutrino Observatory. Both signal and background traces are filtered to a frequency band of 100-350 MHz before training and analysis. We aim to use these machine-learning models to improve the detection threshold of radio experiments and to improve the accuracy of the arrival time and amplitude of detected cosmic-ray radio pulses. |
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