Role of denoisers in simulation-based inference from graph-structured data: a case study for position inference in an astroparticle detector

Role of denoisers in simulation-based inference from graph-structured data: a case study for position inference in an astroparticle detector

Abstract The focus of this paper is to improve simulation-based inference through improved denoising of experimental data. Statistical inference of parameters governing a complex physical process from observed data is an important task for several scientific domains. In a likelihood-free inference s...

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Bibliographic Details
Main Authors: Venkat Roy, Aaron Higuera, Shixiao Liang, Christina Peters, Waheed U. Bajwa, Christopher D. Tunnell
Format: Article
Language:English
Published: SpringerOpen 2025-01-01
Series:EURASIP Journal on Advances in Signal Processing
Subjects:
Astroparticle detector
Graph learning
Kernel trick
Multilayer perceptron
Simulation-based inference
Online Access:https://doi.org/10.1186/s13634-024-01201-8
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https://doi.org/10.1186/s13634-024-01201-8

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