Description
New tools enabled by machine learning techniques
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Patrick Tsang (SLAC)6/28/24, 9:00 AM
Modeling the light propagation in LArTPC with sinusoidal representation networks (SIREN) is scalable and capable of being calibrated using data.
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In this talk, I will demonstrate a few applications of the SIREN in position reconstruction and charge-light signal correlation. -
Pierre Granger (APC/CNRS)6/28/24, 9:35 AM
Liquid argon time projection chambers (LArTPCs) are highly attractive for particle detection because of their tracking resolution and calorimetric reconstruction capabilities. Developing high-quality simulators for such detectors is very challenging because conventional approaches to describe different detector parameters or processes ignore their entanglement (ie, calibrations are done one at...
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Yifan Chen (SLAC)6/28/24, 10:10 AM
The fidelity of detector simulation is crucial for precision experiments, such as DUNE which uses liquid argon time projection chambers (LArTPCs). Conventional calibration approaches usually tackle individual detector processes and require careful tuning of the calibration procedure to mitigate the impact from elsewhere. We have previously shown a successful demonstration of differentiable...
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Junjie Xia (IPMU)6/28/24, 11:15 AM
The water Cherenkov detector stands as a cornerstone in numerous physics programs such as nucleon decay search and precise neutrino measurements. Over recent decades, many such detectors have achieved groundbreaking discoveries, with preparations underway for the next generation of advancements. However, like in all other experiments, accurately quantifying detector systematic uncertainties...
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César Jesús-Valls (Kavli IPMU, University of Tokyo)6/28/24, 11:40 AM
Drawing statistical conclusions out of experimental data entails to compare it to the physics model predictions of interest. In modern particle physics experiments, producing predictions often requires of three subtasks: 1) Simulating the particles interactions and propagation within the detector, 2) Describing the detector response and tuning its description to calibration data and 3)...
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Jiri Franc (Czech Technical University in Prague)6/28/24, 12:05 PM
In high energy physics, the detection of rare events and the computation of their properties require precise and reliable statistical methods, with uncertainty quantification playing a crucial role. Nowadays, most research relies on machine learning methods, where the calibration of output probabilities is not always straightforward. How can we then draw conclusions with the required five...
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Beata Kowal (University of Wroclaw)6/28/24, 12:30 PM
We shall review the results of our recent work on the developments of the NuWro Monte Carlo generator of events. We are working on applying deep learning techniques to optimize the NuWro generator. In the first step, we work on the neural network model that generates the lepton-nucleus cross-sections. We obtained a deep neural network model that predicts the electron-carbon cross-section over...
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Daniel Douglas (SLAC National Accelerator Laboratory)6/28/24, 12:55 PM
With the rising importance of large sequential models in neutrino imaging and reconstruction tasks, a robust estimation of uncertainty at each stage of reconstruction is essential. These chained models produce valuable physical representations of the evolution of a neutrino interaction, which are of interest to many disparate fields of study. This talk will discuss methods available for...
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Kazuhiro Terao (SLAC)6/28/24, 1:20 PM