arXiv subjects

Kate A. Richardson

Publications and source records attributed to Kate A. Richardson.

4 recordsLinked to original sources

arXiv2026

Real-time lepton identification at LHCb in Run 3 using Lipschitz neural networks

The LHCb physics program in Run 3 relies critically on the efficient real-time selection of events containing muons and electrons, which are key signatures in a wide range of heavy-flavor and exotic decay processes. The LHCb Run 3 detector now operates with a fully software-based trigger that processes the complete detector readout at the LHC bunch-crossing rate, with the first trigger stage executed on GPUs. In this environment, particle-identification algorithms must achieve high efficiency and background rejection while satisfying stringent constraints on throughput and memory footprint. We present algorithms for muon and electron identification in the LHCb Run 3 GPU trigger based on Lipschitz-constrained neural networks. Separate networks are developed for muons and electrons and are trained using simulated events. Their performance is evaluated relative to the previous baseline algorithms, demonstrating improved discrimination across a wide range of kinematic regions while remaining compatible with the requirements of real-time GPU execution.

hep-ex↗

arXiv2026

A converged architecture for processing 32 Tbps of physics data in real-time at the LHCb experiment

The LHCb detector at the Large Hadron Collider has been upgraded to acquire an unprecedented 32 Tbps of particle-collision data to provide new insights in the High Energy Physics domain. The data produced by the detector is filtered in real-time to select interesting collisions. As part of the upgrade, a pre-filtering stage has been removed leading to a factor 40 increase in data rate. To deal with the high throughput demands of LHCb real-time data processing, we present an off-the-shelf network architecture using zero-copy techniques in conjunction with an efficient, fully-GPU-based filter. Our converged architecture is able to process the full 32 Tbps of particle-collision data in real-time, the highest in any physics experiment to date. Our result extends the reach of the LHCb physics programme and sets a new standard for real-time data processing at particle physics experiments.

hep-ex↗

arXiv2025

The DNA of nuclear models: How AI predicts nuclear masses

Obtaining high-precision predictions of nuclear masses, or equivalently nuclear binding energies, $E_b$, remains an important goal in nuclear-physics research. Recently, many AI-based tools have shown promising results on this task, some achieving precision that surpasses the best physics models. However, the utility of these AI models remains in question given that predictions are only useful where measurements do not exist, which inherently requires extrapolation away from the training (and testing) samples. Since AI models are largely black boxes, the reliability of such an extrapolation is difficult to assess. We present an AI model that not only achieves cutting-edge precision for $E_b$, but does so in an interpretable manner. For example, we find that (and explain why) the most important dimensions of its internal representation form a double helix, where the analog of the hydrogen bonds in DNA here link the number of protons and neutrons found in the most stable nucleus of each isotopic chain. Furthermore, we show that the AI prediction of $E_b$ can be factorized and ordered hierarchically, with the most important terms corresponding to well-known symbolic models (such as the famous liquid drop). Remarkably, the improvement of the AI model over symbolic ones can almost entirely be attributed to an observation made by Jaffe in 1969 based on the structure of most known nuclear ground states. The end result is a fully interpretable data-driven model of nuclear masses based on physics deduced by AI.

nucl-th↗

arXiv2021

The search for low-mass axion dark matter with ABRACADABRA-10cm

Two of the most pressing questions in physics are the microscopic nature of the dark matter that comprises 84% of the mass in the universe and the absence of a neutron electric dipole moment. These questions would be resolved by the existence of a hypothetical particle known as the quantum chromodynamics (QCD) axion. In this work, we probe the hypothesis that axions constitute dark matter, using the ABRACADABRA-10cm experiment in a broadband configuration, with world-leading sensitivity. We find no significant evidence for axions, and we present 95% upper limits on the axion-photon coupling down to the world-leading level $g_{aγγ}<3.2 \times10^{-11}$ GeV$^{-1}$, representing one of the most sensitive searches for axions in the 0.41 - 8.27 neV mass range. Our work paves a direct path for future experiments capable of confirming or excluding the hypothesis that dark matter is a QCD axion in the mass range motivated by String Theory and Grand Unified Theories.

hep-ex↗