The muon tagging is an essential tool to distinguish between gamma and hadron-induced showers in wide field-of-view gamma-ray observatories. In this work, it is shown that an efficient muon tagging (and counting) can be achieved using a water Cherenkov detector with a reduced water volume and multiple PMTs, provided that the PMT signal spatial and time patterns are interpreted by an analysis...
As the first step in a wide-ranging study to determine the capabilities of
fine-grained calorimeters to identify different hadrons within dense showers, we show how to extract all the information about all intermediate processes taking place within the development of complex hadron showers produced by simulation in GEANT4.
Future improvements in particle accelerator performance are predicated on increasingly accurate online modeling of accelerators. Hysteresis effects in magnetic, mechanical, and material components of accelerators are often neglected in online accelerator models used to inform control algorithms, even though reproducibility errors from systems exhibiting hysteresis are not negligible in high...
The recent MODE whitepaper*, proposes an end-to-end differential pipeline for the optimization of detector designs directly with respect to the end goal of the experiment, rather than intermediate proxy targets. The TomOpt python package is the first concrete step in attempting to realize such a pipeline, and aims to allow the optimisation of detectors for the purpose of muon tomography with...
We present a machine learning approach for real-time detector monitoring. The corresponding core algorithm is powered by recent large-scale implementations of kernel methods, nonparametric learning algorithms that can approximate any continuous function given enough data. The model evaluates the compatibility between incoming batches of experimental data and a reference data sample, by...
Derivatives, mainly in the form of gradients and Hessians, are ubiquitous in machine learning and Bayesian inference. Automatic differentiation (AD) techniques transform a program into a derivative (adjoint) program, which is run to compute the gradient.
Traditionally, most AD systems have been high level, and unable to extract good performance on scalar code or loops modifying memory....
The Compressed Baryonic Matter (CBM) experiment at FAIR will investigate the
QCD phase diagram at high net-baryon density (μB > 500 MeV) with heavy-ion
collisions in the energy range of √sNN = 2.7−4.9 GeV. Precise determination of dense
baryonic matter properties requires multi-differential measurements of strange
hadron yields, both for the most copiously produced K0s and Λ as well as for...
ACTAR is an active-target TPC optimized for the study of nuclear reactions produced by low-intensity beams, such as radioactive beams. In this detector, the gas used to track charged particles within the chamber is at the same time used as a target for the incoming beam. Reconstructing the tracks left by ions is a challenging task, and two different reconstruction algorithms are compared in...
