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

Blog Article

Abstract The Valves Pipe 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 setup, the inference engine used to make inference from the experimental data can be trained using simulated data.

In many scenarios, experimental data is corrupted by noise during the data acquisition process, which is either unaccounted for in the simulator or whose strength might not match with the one set in the simulator.This deteriorates the accuracy of the inference.While advances in denoising can be leveraged to address this challenge, many denoising techniques ignore the fact that experimental data in many scientific domains tends to be irregular/graph-structured.

This paper addresses these two challenges by developing a kernel-based learnable graph (KBLG) denoiser, which can be used to denoise experimental graph-structured UPS data.In order to exhibit the efficacy of the developed denoiser in simulation-driven inference problems, this paper considers the problem of inference of the position of an interaction in an astroparticle detector.The available simulated data in this case is the snapshots of the luminous responses of the photomultiplier tube sensors used within the dark matter detection experiment.

In experimental situations, these measurements are corrupted by noise generated by secondary optical and electronic processes.The proposed KBLG denoiser is used to denoise the multiple snapshots of the experimental measurements, which are then used for position reconstruction using a multilayer perceptron trained using noiseless simulated data.Numerical results exhibit that the proposed KBLG denoiser outperforms a graph-agnostic denoiser in terms of MLP-based position reconstruction performances for different levels of noise (noise variance).