arXiv subjects

Maria Domenica Galati

Publications and source records attributed to Maria Domenica Galati.

2 recordsLinked to original sources

arXiv2026

Looking forward to $B^+\to τ^+ ν_τ$ and $B_c^+\to τ^+ ν_τ$

These proceedings present the outcome of a feasibility study using RapidSim simulation software that demonstrates that the LHCb experiment will be capable of observing the decays $B^+\to τ^+ ν_τ$ and $B_c^+\to τ^+ ν_τ$ using the data that is being collecting during Run 3 of the LHC. The proposed analysis exploits the small distance of only 5.1 millimetres between the sensing elements of LHCb's innermost silicon pixel detector, the VELO, and the LHC's proton beams to identify direct pixel hits in the VELO that can be associated with the charged $B^+$, $B_c^+$ or $τ^+$ particles. By using this extra information, the limitations due to the missing momentum and vertex information will be significantly reduced. This provides enough statistical power to pursue the measurements of these two decay channels at the LHC. In particular for the decay $B_c^+\to τ^+ ν_τ$, which has been identified by the high energy physics community as a key objective for experiments at the planned next-generation particle accelerators, this means we do not need to wait for the 2030s or beyond to get first experimental constraints.

hep-ex↗

arXiv2021

Applications and Techniques for Fast Machine Learning in Science

In this community review report, we discuss applications and techniques for fast machine learning (ML) in science -- the concept of integrating power ML methods into the real-time experimental data processing loop to accelerate scientific discovery. The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for training and implementing performant and resource-efficient ML algorithms; and computing architectures, platforms, and technologies for deploying these algorithms. We also present overlapping challenges across the multiple scientific domains where common solutions can be found. This community report is intended to give plenty of examples and inspiration for scientific discovery through integrated and accelerated ML solutions. This is followed by a high-level overview and organization of technical advances, including an abundance of pointers to source material, which can enable these breakthroughs.

cs.LG↗