Reconstruction procedure of the Fluorescence detector Array of Single-pixel Telescopes (FAST)

TL;DR

The paper presents a cost-effective, simplified fluorescence telescope design for cosmic-ray detection, utilizing neural networks and simulation-based fitting to reconstruct air shower parameters with minimal photomultiplier tubes.

Contribution

It introduces a novel reconstruction method combining neural networks and simulation fitting for a low-cost, minimal-PMT fluorescence detector.

Findings

Demonstrates effective shower parameter reconstruction with FAST prototypes.

Shows potential for scalable, cost-efficient cosmic-ray detection.

Validates the approach using Telescope Array experiment data.

Abstract

The Fluorescence detector Array of Single-pixel Telescopes (FAST) is one of several proposed designs for a next-generation cosmic-ray detector. Such detectors will require enormous collecting areas whilst also needing to remain cost-efficient. To meet these demands, the FAST collaboration has designed a simplified, low-cost fluorescence telescope consisting of only four photomultiplier tubes (PMTs). Since standard air shower reconstruction techniques cannot be used with so few PMTs, FAST utilises an alternative two-step approach. In the first step, a neural network is used to provide a first estimate of the true shower parameters. This estimate is then used as the initial guess in a minimisation procedure where the measured PMT traces are compared to simulated ones, and the best-fit shower parameters are found. A detailed explanation of these steps is given, with the expected performance of FAST prototypes at the Telescope Array experiment acting as a demonstration of the technique.