Speaker
Description
The current and future programs for accelerator-based neutrino imaging detectors feature the use of Liquid Argon Time Projection Chambers (LArTPC) as the fundamental detection technology. These detectors combine high-resolution imaging and precision calorimetry to enable the study of neutrino interactions with unparalleled capabilities. However, the volume of data from LArTPCs will exceed 25 Petabytes each year for DUNE (Deep Underground Neutrino Experiment) and event reconstruction techniques are complex, requiring significant computational resources. These aspects of LArTPC data make utilization of real-time event triggering and event filtering algorithms that can distinguish signal from background important, but still challenging to accomplish with reasonable efficiency especially for low energy neutrino interactions. At Fermilab, we are developing a machine-learning-based trigger and filtering algorithm for the lab's flagship experiment DUNE, to extend the sensitivity of the detector, particularly for low energy neutrinos that do not come from an accelerator beam. Building off of recent research in machine learning to improve artificial intelligence, this new trigger algorithm will employ software to optimize data collection, pre-processing, and to make a final event selection decision. Development and testing of the trigger decision system will leverage data from MicroBooNE, ProtoDUNE, and Short Baseline Neutrino (SBN) LArTPC detectors, and will also provide benefits to the physics programs of those experiments.
This talk will focus on application of a Convolutional Neural Network (CNN) to MicroBooNE data and will study performance metrices such as memory usage and latency. We will also discuss progress towards applying a Semantic Segmentation with Sparse Convolutional Network (SparseCNN) on the same data and compare the performance of the two algorithms.
| Consider for long presentation | Yes |
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