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Abstract When electrons with energies of O(100) MeV pass through a liquid argon time projection chamber (LArTPC), they deposit energy in the form of electromagnetic showers. Methods to reconstruct the energy of these showers in LArTPCs often rely on the combination of a clustering algorithm and a linear calibration between the shower energy and charge contained in the cluster. This reconstruction process could be improved through the use of a convolutional neural network (CNN). Here we discuss the performance of various CNN-based models on simulated LArTPC images, and then compare the best performing models to a typical linear calibration algorithm.more »We show that the CNN method is able to address inefficiencies caused by unresponsive wires in LArTPCs and reconstruct a larger fraction of imperfect events to within 5 % accuracy compared with the linear algorithm.« less
Free, publicly-accessible full text available February 1, 2023
Christy, C. T.; Jayasinghe, T.; Stanek, K. Z.; Kochanek, C. S.; Way, Z.; Prieto, J. L.; Shappee, B. J.; Holoien, T. W.-S.; Thompson, T. A.; Schneider, A.(
, Publications of the Astronomical Society of the Pacific)
Abstract We present the first results from Citizen ASAS-SN, a citizen science project for the All-Sky Automated Survey for Supernovae (ASAS-SN) hosted on the Zooniverse platform. Citizen ASAS-SN utilizes the newer, deeper, higher cadence ASAS-SN g -band data and tasks volunteers to classify periodic variable star candidates based on their phased light curves. We started from 40,640 new variable candidates from an input list of ∼7.4 million stars with δ < −60° and the volunteers identified 10,420 new discoveries which they classified as 4234 pulsating variables, 3132 rotational variables, 2923 eclipsing binaries, and 131 variables flagged as Unknown. They classifiedmore »known variable stars with an accuracy of 89% for pulsating variables, 81% for eclipsing binaries, and 49% for rotational variables. We examine user performance, agreement between users, and compare the citizen science classifications with our machine learning classifier updated for the g -band light curves. In general, user activity correlates with higher classification accuracy and higher user agreement. We used the user’s “Junk” classifications to develop an effective machine learning classifier to separate real from false variables, and there is a clear path for using this “Junk” training set to significantly improve our primary machine learning classifier. We also illustrate the value of Citizen ASAS-SN for identifying unusual variables with several examples.« less
Free, publicly-accessible full text available February 1, 2023
Aguilar-Arevalo, A. A.; Brown, B. C.; Conrad, J. M.; Dharmapalan, R.; Diaz, A.; Djurcic, Z.; Finley, D. A.; Ford, R.; Garvey, G. T.; Gollapinni, S.; et al(
, Physical Review D)
Abbasi, R.; Ackermann, M.; Adams, J.; Aguilar, J.A.; Ahlers, M.; Ahrens, M.; Alameddine, J.M.; Alves, A.A.; Amin, N.M.; Andeen, K.; et al(
, Journal of Instrumentation)
Abstract The Surface Enhancement of the IceTop air-shower array will include the addition of radio antennas and scintillator panels, co-located with the existing ice-Cherenkov tanks and covering an area of about 1 km 2 . Together, these will increase the sensitivity of the IceCube Neutrino Observatory to the electromagnetic and muonic components of cosmic-ray-induced air showers at the South Pole. The inclusion of the radio technique necessitates an expanded set of simulation and analysis tools to explore the radio-frequency emission from air showers in the 70 MHz to 350 MHz band. In this paper we describe the software modules thatmore »have been developed to work with time- and frequency-domain information within IceCube's existing software framework, IceTray, which is used by the entire IceCube collaboration. The software includes a method by which air-shower simulation, generated using CoREAS, can be reused via waveform interpolation, thus overcoming a significant computational hurdle in the field.« less
Free, publicly-accessible full text available June 1, 2023
Abbasi, R.; Ackermann, M.; Adams, J.; Aguilar, J. A.; Ahlers, M.; Ahrens, M.; Alameddine, J. M.; Alispach, C.; Alves, A. A.; Amin, N. M.; et al(
, Physical Review Letters)
Free, publicly-accessible full text available June 1, 2023
