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Implicit Neural Representation as a Differentiable Surrogate for Photon Propagation in a Monolithic Neutrino Detector

May 9, 2023, 11:45 AM
Hampton Roads VII (Norfolk Waterside Marriott)

Hampton Roads VII

Norfolk Waterside Marriott

235 East Main Street Norfolk, VA 23510
Oral Track 9 - Artificial Intelligence and Machine Learning Track 9 - Artificial Intelligence and Machine Learning


Tsang, Patrick (SLAC)


Modern neutrino experiments employ hundreds to tens of thousands of photon detectors to detect scintillation photons produced from the energy deposition of charged particles. A traditional approach of modeling individual photon propagation as a look-up table requires high computational resources, and therefore it is not scalable for future experiments with multi-kiloton target volume.

We propose a new approach using SIREN, an implicit neural representation with periodic activation functions, to model the look-up table as a 3D scene. It reproduces the acceptance map with high accuracy using orders of magnitude less number of parameters than the look-up table. As a continuous and differentiable parameterization, SIREN also represents a smooth gradient surface. As such, it allows downstream applications such as inverse problem-solving and gradient-based optimizations. We demonstrate a data-driven method to optimize the SIREN model and an application of reconstruction using data collected from the Deep Underground Neutrino Experiment's (DUNE) near detector prototype.

Consider for long presentation Yes

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