Battaglieri, Marco (ORCID:0000000150028771)

DOE OSTI2025

A Beamdump facility at Jefferson Lab

The potential of the intense secondary muon, neutrino, and (hypothetical) light dark matter beams at the Thomas Jefferson National Accelerator Facility (Jefferson Lab) is explored. These are produced in the high-power dumps with high-current electron beams. Light dark matter searches with the approved Beam Dump eXperiment (BDX) are driving the realization of a new underground vault behind Hall A that could be extended to a Beamdump Facility with little additional installations. High-energy muons created via the Bethe–Heitler process uniquely do not proceed through the more common pion production and decay channels. Several possible muon physics applications are highlighted. Neutrino detector technologies and experiments suitable for a beamdump facility are outlined.

Accelerator Physics↗

DOE OSTI2025

Real Time implementation of Artificial Intelligence compression algorithm for High-Speed Streaming Readout signals

The new generation of high-energy physics experiments plans to acquire data in streaming mode. With this approach, it is possible to access the information of the whole detector (organized in time slices) for optimal and lossless triggering of data acquisitions. With this approach, data rates, especially in large detectors, are often very high, and the network is likely to be the bottleneck for the entire Streaming Read Out system. The aim of this work is to study the implementation of a lossy compression algorithm based on Artificial Intelligence: an Autoencoder. With Machine Learning it is possible to achieve a high compression ratio and fast inference time with only a small degradation of the signals, almost negligible for the specific application. This work explores different configurations of the Autoencoder and the implementation on different hardware. Different Autoencoder configurations are explored to find the best trade-off between compression ratio and reconstruction loss, both for signals and energy spectrum. Different hardware implementations are also explored to find the best platform to achieve real-time performance for the specific application.

Rossi, Fabio (ORCID:0009000385713885)↗