arXiv subjects

Noah Clarke Hall

Publications and source records attributed to Noah Clarke Hall.

5 recordsLinked to original sources

arXiv2026

Distilling Normalizing Flows for Real-Time Anomaly Detection at the LHC

Normalizing flows are principled anomaly detectors, selecting anomalies using a probabilistic per-event likelihood. Extreme latency and resource constraints have prevented the deployment of flow likelihoods within the hardware triggers at the Large Hadron Collider. We bypass these limitations by distilling the likelihood from a large normalizing flow into lightweight student estimators suitable for deployment on a field-programmable gate array. Our use of a conditional normalizing flow enables precise likelihood estimation in the presence of missing input features. Both decision tree and neural network students are considered, the latter optimized under advanced quantization techniques. By simultaneously improving likelihood quality and lowering inference cost, we demonstrate both state-of-the-art physics performance and latency compared with existing flow-based approaches.

hep-ex↗

arXiv2026

AMD Versal AI-Engines for fixed latency environments

Complex, high-throughput data acquisition and processing systems, such as those used in high-energy physics experiments, are increasingly moving sophisticated pattern recognition and data compression algorithms closer to the sensors themselves. To meet these needs, programmable device manufacturers offer multi-silicon die packages that commonly include dedicated co-processors within the same package. We present a technical study of a new family of such co-processors from AMD Xilinx, the Adaptive Intelligence (AI) Engine, or AIE, as part of the Versal architecture. Specifically, we focus on the deployment capabilities of AIEs in fixed latency environments such as those typically found in colliding beam experiments like those at the Large Hadron Collider. We evaluate the performance of a vectorised implementation of both a Boosted Decision Tree (BDT) and a Convolutional Neural Network (CNN), thereby demonstrating the feasibility of deploying AIEs for ML applications in such environments and their use as possible alternatives to traditional programmable logic-based implementations.

hep-ex↗

arXiv2026

End-to-end optimisation of HEP triggers

High-energy physics experiments face extreme data rates, requiring real-time trigger systems to reduce event throughput while preserving sensitivity to rare processes. Trigger systems are typically constructed as modular chains of sequentially optimised algorithms, including machine learning models. Each algorithm is optimised for a specific local objective with no guarantee of overall optimality. We instead formulate trigger design as a constrained end-to-end optimisation problem, treating all stages- including data encoding, denoising, clustering, and calibration- as components of a single differentiable system trained against a unified physics objective. The framework jointly optimises performance while incorporating physics and deployment constraints. We demonstrate this approach on a hardware multi-jet trigger inspired by the ATLAS High-Luminosity Large Hadron Collider design. Using Higgs boson pair production as a benchmark, we observe x2-4 improvement in true-positive rate at fixed false-positive rate, while preserving interpretable intermediate physics objects and monotonic calibration constraints. These results highlight end-to-end optimisation as a practical paradigm for next-generation real-time event selection systems.

hep-ex↗

arXiv2025

DECADE: Decorrelated anomaly detection triggers to enhance the low-mass discovery potential of the LHC

At the ATLAS and CMS experiments at CERN's Large Hadron Collider, the rate of proton-proton collisions far exceeds the rate at which data can be recorded. A real-time event selection process, or "trigger", is needed to ensure that the data recorded contains the highest possible discovery potential. In the absence of hoped-for anomalies that would lead to the discovery of new physics, there is increasing motivation to develop dedicated, model-agnostic, anomaly detection triggers. A common approach is to use unsupervised machine learning (ML) to predict an event-by-event anomaly score, based on the momenta and multiplicity of reconstructed objects. Such anomaly scores often exhibit high correlation with existing trigger observables and thus exhibit a selection bias towards high-momentum anomalies. In this article, we introduce DECorrelated Anomaly DEtection (DECADE), in which quantile regression is applied to the output of a pre-trained anomaly detection algorithm, guaranteeing the independence of the threshold on the anomaly score with respect to primary trigger observables. Thus, DECADE provides efficiency in low-momentum regions of phase space not captured by existing triggers, boosting the trigger efficiency for low-mass phenomena that are inaccessible via primary triggers and current anomaly detection triggers. Quantile regression is implemented using decision tree ensembles, making DECADE highly computationally efficient and therefore optimal for use both in software-based trigger systems and in FPGA-based hardware triggers. In both cases, we demonstrate that DECADE would add an insignificant additional latency and resource cost to the hardware anomaly detection triggers currently in operation at ATLAS and CMS, as well as to those proposed for the High-Luminosity era of the Large Hadron Collider.

hep-ex↗

arXiv2023

Machine-enhanced CP-asymmetries in the electroweak sector

The violation of charge conjugation (C) and parity (P) symmetries are a requirement for the observed dominance of matter over antimatter in the Universe. As an established effect of beyond the Standard Model physics, this could point towards additional CP violation in the Higgs-gauge sector. The phenomenological footprint of the associated anomalous couplings can be small, and designing measurement strategies with the highest sensitivity is therefore of the utmost importance in order to maximise the discovery potential of the Large Hadron Collider (LHC). There are, however, very few measurements of CP-sensitive observables in processes that probe the weak-boson self-interactions. In this article, we study the sensitivity to new sources of CP violation for a range of experimentally-accessible electroweak processes, including $Wγ$ production, $WW$ production via photon fusion, electroweak $Zjj$ production, electroweak $ZZjj$ production, and electroweak $W^\pm W^\pm jj$ production. We study simple angular observables as well CP-sensitive observables constructed using the outputs of machine-learning (ML) algorithms. We find that the ML-constructed CP-sensitive observables improve the sensitivity to CP-violating effects by up to a factor of five, depending on the process. We also find that inclusive $Wγ$ and electroweak $Zjj$ production have the potential to set the best possible constraints on certain CP-odd operators in the Higgs-gauge sector of dimension-six effective field theories.

hep-ph↗