Bulletin of the American Physical Society
APS April Meeting 2022
Volume 67, Number 6
Saturday–Tuesday, April 9–12, 2022; New York
Session Y08: New Techniques in Neutrino Physics IRecordings Available
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Sponsoring Units: DPF Chair: Guang Yang, University of California, Berkeley Room: Juilliard |
Tuesday, April 12, 2022 1:30PM - 1:42PM Withdrawn |
Y08.00001: Dimuons in Neutrino Telescopes: New Predictions and First Search in IceCube Bei Zhou, John F Beacom Neutrino telescopes are powerful probes of high-energy astrophysics and particle physics. Their power is increased when they can isolate different event classes, e.g., by flavor, though that is not the only possibility. Here we focus on a new event class for neutrino telescopes: dimuons, two energetic muons from one neutrino interaction. We make new theoretical and observational contributions. For the theoretical part, we calculate dimuon production cross sections and detection prospects via deep-inelastic scattering (DIS; where we greatly improve upon prior work) and $W$-boson production (WBP; where we present first results). We show that IceCube should have $\simeq 400$ dimuons ($\simeq 8$ from WBP) in its current data and that IceCube-Gen2, with a higher threshold but a larger exposure, can detect $\simeq 1200$ dimuons ($\simeq 30$ from WBP) in 10 years. These dimuons are almost all produced by atmospheric neutrinos. For the observational part, we perform a simple but conservative analysis of IceCube public data, finding the first candidate dimuon events (19 events). Though some IceCube experts we consulted argue these events cannot be real dimuons, (A) these events match well all aspects of our predictions and (B) no other compelling hypotheses have been raised. Whether these 19 events are real dimuons or some new background (or signal!), it is important to understand them. Here we share full details to help IceCube and to attract scrutiny from the broader community. Together, these theoretical and observational contributions help open a valuable new direction for neutrino telescopes, one especially important for probing high-energy QCD and new physics. |
Tuesday, April 12, 2022 1:42PM - 1:54PM |
Y08.00002: Reactor Antineutrino Spectrum Unfolding Techniques for a Final Analysis of PROSPECT-I and Future Experiments Christian Roca Catala The Precision Reactor Oscillation and SPECTrum (PROSPECT) experiment measures the spectrum of antineutrinos from the High Flux Isotope Reactor and searches for potential short-baseline oscillations. The analyses performed to produce the last published results from the current dataset (PROSPECT-I) excluded initially unusable data obtained from some detector segments containing non-operating PMTs. Recent efforts from the collaboration have resulted in a more sophisticated analysis that includes the Single Ended Event Reconstruction (SEER) of the previously unused segments, and the careful data splitting (DS) of the different time periods to maximize the available statistics. |
Tuesday, April 12, 2022 1:54PM - 2:06PM |
Y08.00003: Precise Measurement of Reactor Antineutrino Spectra from Joint Analyses of PROSPECT, STEREO, and Daya Bay Benjamin T Foust The PROSPECT, Daya Bay, and STEREO reactor antineutrino experiments have made world-leading measurements of the $^{235}$U antineutrino spectra from fission reactors using high-performance liquid scintillator detectors. |
Tuesday, April 12, 2022 2:06PM - 2:18PM |
Y08.00004: Transformer Networks for NOvA Event Classification Alejandro J Yankelevich, Alexander Konstantinovich Shmakov NOvA is a long-baseline neutrino experiment studying neutrino oscillations with Fermilab's NuMI beam. A convolutional neural network (CNN) that analyzes topological features is used to determine neutrino flavor in both the near and far detectors and observe the disappearance of muon neutrinos and the appearance of electron neutrinos. Alternative approaches to flavor identification using machine learning are being investigated with the goal of developing a network trained with both event-level and particle-level images in addition to reconstructed physical variables while maintaining the performance of the CNN. Such a network could be used to analyze the individual prediction importances of these inputs. An original network that uses a combination of transformer and MobileNet CNN blocks will be discussed. |
Tuesday, April 12, 2022 2:18PM - 2:30PM |
Y08.00005: Strategies for cosmogenic background suppression at the ICARUS Biswaranjan Behera The ICARUS detector will search for LSND like neutrino oscillations exposed at shallow depth to the FNAL BNB beam in the context of the Short Baseline (SBN) program. Cosmic backgrounds rejection is particularly relevant for the ICARUS detector due to its larger size and distance from target compared to short baseline near detector (SBND). In ICARUS the neutrino signal/cosmic background ratio is 40 times more unfavorable with in addition a greater than 3 times larger out-of-spill comics rate. In this talk, I will discuss the techniques for reducing cosmogenic backgrounds in the ICARUS detector. |
Tuesday, April 12, 2022 2:30PM - 2:42PM |
Y08.00006: Bayesian methods for three flavour oscillation analyses in NOvA Artur A Sztuc NOvA is a long-baseline, accelerator neutrino oscillation experiment that uses two functionally identical detectors with an 810 km baseline to study neutrino oscillation and interaction physics. NOvA measures the muon neutrino deficit and electron neutrino excess with neutrino and anitineutrino beams to probe neutrino oscillation parameters, including the large neutrino mixing angle, the mass ordering and the CP-violating phase. New Bayesian frameworks are being developed for the NOvA neutrino oscillation analyses, using the Markov Chain Monte Carlo technique. This technique will also be used for the joint data analysis of the NOvA and T2K experiments. The motivation and status of the new Bayesian analyses will be presented with the description of the new framework. Comparisons to the NOvA Frequentist analysis and the Bayesian interpretation will be presented and discussed. |
Tuesday, April 12, 2022 2:42PM - 2:54PM |
Y08.00007: Deep Neural Networks for New Physics Searches for Dilepton Objects at the Fermilab Short Baseline Neutrino Program Joshua Berger, Brian Batell, Jamie Dyer, Ahmed Ismail I discuss the prospects for studying dark sectors beyond the standard model at the Fermilab short-baseline neutrino experiments by applying deep neural networks. Dilepton dark sector signals can be challenging to disntinguish from photon backgrounds. We apply machine learning techniques to simulated signal and backgorund events to determine the viability of these tools for BSM searches in this channel. We compare our results to traditional search strategies and find improved sensitivity. |
Tuesday, April 12, 2022 2:54PM - 3:06PM Withdrawn |
Y08.00008: New tests for neutrino self-interactions using astrophysical neutrinos Po-Wen Chang, Ivan Esteban, Todd A Thompson, John F Beacom Neutrino self-interactions (nuSI) have been proposed to explain the muon g-2 anomaly, the short baseline anomalies, and the Hubble tension. Furthermore, nuSI can affect the evolution of neutrino-dense environments in our Universe. However, laboratory probes remain weak. We open new windows to test nuSI using astrophysical neutrino signals. Through simple and conservative arguments, we find that current data allow us to place world-leading constraints and that great improvements can be expected using forthcoming experiments. |
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