Bulletin of the American Physical Society
APS April Meeting 2021
Volume 66, Number 5
Saturday–Tuesday, April 17–20, 2021; Virtual; Time Zone: Central Daylight Time, USA
Session L17: Data Science IILive

Hide Abstracts 
Sponsoring Units: GDS Chair: Dimitri Bourilkov, University of Florida 
Sunday, April 18, 2021 3:45PM  3:57PM Live 
L17.00001: Inference of neutrino flavor evolution through data assimilation and neural differential equations Ermal Rrapaj, Amol Patwardhan, Eve Armstrong, George Fuller The evolution of neutrino flavor in dense environments such as corecollapse supernovae and binary compact object mergers constitutes an important and unsolved problem. Its solution has potential implications for the dynamics and heavyelement nucleosynthesis in these environments. In this paper, we build upon recent work to explore inferencebased techniques for the estimation of model parameters and neutrino flavor evolution histories. We combine data assimilation, ordinary differential equation solvers, and neural networks to craft an inference approach tailored for nonlinear dynamical systems. Using this architecture, and a simple twoneutrinobeam, twoflavor model, we compare the performances of nine different optimization algorithms and expand upon previous assessments of the efficacy of inference for tackling problems in flavor evolution. We find that employing this new architecture, together with evolutionary optimization algorithms, accurately captures flavor histories in the smallscale model and allows us to quickly explore both model parameters and initial flavor content. In future work we plan to extend these inference techniques to large numbers of neutrinos. [Preview Abstract] 
Sunday, April 18, 2021 3:57PM  4:09PM Live 
L17.00002: Improving IceCube Event Reconstruction Using A Graph Convolutional Network And Semantic Segmentation Rui An Machine learning is a candidate for the nextgeneration reconstruction for neutrino experiments such as IceCube. IceCube is an iceCherenkov neutrino detector embedded in a cubic kilometer of glacial ice in Antarctica. The detector observes astrophysical and atmospheric neutrinos via the light emitted by charged particles produced in neutrino interactions with 5160 digital optical modules~(DOM). A typical $\nu_\mu$ interaction~(1TeV$\sim$100TeV) originating inside the detector, namely the ``starting track'', is dominated by deep inelastic scattering which produces a hadronic cascade near the interaction vertex and a muon track. Conventional reconstruction assumes continuous energy loss for ``starting track'' events in the hadronic cascades, leading to a generally underestimated energy reconstruction. Correct clustering the hadronic cascade and track is crucial for improving the energy reconstruction. This study presents a graph convolutional network for semantic segmentation to distinguish the DOM charges as cascadelike and tracklike charges. [Preview Abstract] 
Sunday, April 18, 2021 4:09PM  4:21PM Live 
L17.00003: Fast Energy Reconstruction using CNNs for GeV Scale Neutrinos in IceCube Jessie Micallef The IceCube Neutrino Observatory, located deep under the Antarctic ice, detects astrophysical and atmospheric neutrinos. It uses 5160 optical modules spanning a cubic kilometer of ice to detect Cherenkov radiation originating from neutrino interactions. Atmospheric neutrinos at the scale of 10GeV can be used to measure important fundamental properties of neutrinos such as the oscillation parameters and to search for nonstandard interactions. Current likelihoodbased reconstructions take seconds to minutes to reconstruct the properties (energy, direction, etc.) of a neutrino event, which makes them computationally challenging for large data sets. In this talk, I will present work showing the optimization of a convolutional neural network (CNN) to reconstruct the energy of 10GeV scale events in IceCube. This method takes submilliseconds per neutrino event, and also offers improvements in the energy resolution. [Preview Abstract] 
Sunday, April 18, 2021 4:21PM  4:33PM Live 
L17.00004: Evolving Antennas for UltraHigh Energy Neutrino Detection Alexander Patton Evolutionary algorithms borrow from biology the concepts of mutation and selection to approach complex problems more efficiently than traditional techniques. The GENETIS project uses genetic algorithms to develop antenna designs with higher sensitivity to radio impulses from ultrahigh energy neutrino interactions than current designs. We attempt to improve on antenna designs used in current experiments, using geometric constraints imposed by a narrow hole in deep ice. By integrating the XFdtd finitedifference time domain modeling program with simulations of neutrino experiments, we are able to assign a fitness score that is based on neutrino sensitivities. We will report on advancements to the algorithm, steps taken to improve the software we use, the latest results from our evolutions, as well as our roadmap for manufacturing. [Preview Abstract] 
Sunday, April 18, 2021 4:33PM  4:45PM Live 
L17.00005: 1D to 2D CYLEX Transformation to Save Time in JWL Parameterization Reid Ginoza A JonesWilkinsLee (JWL) equation of state (EOS) can be parameterized by simulating a cylinder expansion experiment (CYLEX) and selecting those parameters that lead to the closest match of the experimental velocity history data. This may be computationally expensive as it may take many simulations to find an acceptable parameter set. In this study, we investigate the possibility of using a hypothetical onedimensional sphere expansion simulation as a substitute for the twodimensional CYLEX simulation. The goal was to find a transform function that maps the velocity history of the sphere simulation to the velocity history of the CYLEX simulation. JWLs from several sources, some randomly generated, were used in both the onedimensional sphere expansion and the twodimensional CYLEX simulations. Three regression models were then constructed and evaluated: a threeterm polynomial regression, a nineterm polynomial regression, and a multivariate adaptive regression spline model. These regression models resulted in close agreement between the CYLEX simulation velocity history and the transformed onedimensional sphere expansion simulation velocity history. [Preview Abstract] 
Sunday, April 18, 2021 4:45PM  4:57PM On Demand 
L17.00006: Combined research approach for determining the ground state of spin glass Alena Korol, Dmitrii Kapitan, Alexey Rybin, Egor Vasiliev, Konstantin Soldatov, Yuriy Shevchenko, Vitalii Kapitan, Konstantin Nefedev Monte Carlo simulation is one of the most powerful approaches in statistical physics. However, the ability of modern machine learning techniques to classify, identify, or interpret massive data sets provides a complementary paradigm to the above approach. In our research, we studied the EdwardsAnderson (EA) spin glass (SG) model in a square twodimensional (2D) lattice of Ising spins with a bimodal distribution of bonds. In present work, for simulation we used a combination of our Hybrid Multispin Method (HMM) and the Restricted Boltzmann Machine (RBM) to predict the ground states for the EdwardsAnderson spin glass model. To predict the GS, we used the data of the HMM to train our neural network and to predict spin glass state with a lower energy level than in the training data sets. Our research has shown that the ground states of spin glass systems can be predicted using a neural network. However, given the absence of an exact solution to determine the number of degenerations at the ground energy level for big systems, it is impossible to check whether our algorithms reach the global energy minimum. Nonetheless, machinelearning algorithms can make a significant contribution to solving NPcomplete problems in the future. [Preview Abstract] 
Follow Us 
Engage
Become an APS Member 
My APS
Renew Membership 
Information for 
About APSThe American Physical Society (APS) is a nonprofit membership organization working to advance the knowledge of physics. 
© 2022 American Physical Society
 All rights reserved  Terms of Use
 Contact Us
Headquarters
1 Physics Ellipse, College Park, MD 207403844
(301) 2093200
Editorial Office
1 Research Road, Ridge, NY 119612701
(631) 5914000
Office of Public Affairs
529 14th St NW, Suite 1050, Washington, D.C. 200452001
(202) 6628700