240 citations · 1.4k across the 35 of their papers we have counts for
37 papers
Spiking neural networks for streaming qubit readout
Barry M. Dillon, Aqib Javed, Jim Harkin +2
Fast and accurate qubit-state assignment is essential for feedback, calibration, and error correction in quantum processors. In superconducting platforms, frequency-multiplexed rea…
Spiking Neural Network inference on FPGAs with hls4ml
Barry M. Dillon
Spiking Neural Networks (SNNs) provide a naturally temporal machine-learning framework. Their neurons maintain an internal state and propagate information through discrete spikes,…
Anomaly detection with spiking neural networks for LHC physics
Barry M. Dillon, Jim Harkin, Aqib Javed
Anomaly detection offers a promising strategy for discovering new physics at the Large Hadron Collider (LHC). This paper investigates AutoEncoders built using neuromorphic Spiking…
Theory-informed neural networks for particle physics
Barry M. Dillon, Michael Spannowsky
We present a theory-informed reinforcement-learning framework that recasts the combinatorial assignment of final-state particles in hadron collider events as a Markov decision proc…
Anomalies, Representations, and Self-Supervision
Barry M. Dillon, Luigi Favaro, Friedrich Feiden +2
We develop a self-supervised method for density-based anomaly detection using contrastive learning, and test it using event-level anomaly data from CMS ADC2021. The AnomalyCLR tech…
Performance versus Resilience in Modern Quark-Gluon Tagging
Anja Butter, Barry M. Dillon, Tilman Plehn +1
Discriminating quark-like from gluon-like jets is, in many ways, a key challenge for many LHC analyses. First, we use a known difference in Pythia and Herwig simulations to show ho…
Modern Machine Learning for LHC Physicists
Tilman Plehn, Anja Butter, Barry Dillon +3
Depending on the point of view, modern machine learning is either providing an unprecedented boost to the numerical methods of particle physics, or it is transforming the way we do…
A Normalized Autoencoder for LHC Triggers
Barry M. Dillon, Luigi Favaro, Tilman Plehn +2
Autoencoders are an effective analysis tool for the LHC, as they represent one of its main goal of finding physics beyond the Standard Model. The key challenge is that out-of-distr…
Self-supervised Anomaly Detection for New Physics
Barry M. Dillon, Radha Mastandrea, Benjamin Nachman
We investigate a method of model-agnostic anomaly detection through studying jets, collimated sprays of particles produced in high-energy collisions. We train a transformer neural…
Jets and Jet Substructure at Future Colliders
Ben Nachman, Salvatore Rappoccio, Nhan Tran +24
Even though jet substructure was not an original design consideration for the Large Hadron Collider (LHC) experiments, it has emerged as an essential tool for the current physics p…
What's Anomalous in LHC Jets?
Thorsten Buss, Barry M. Dillon, Thorben Finke +5
Searches for anomalies are a significant motivation for the LHC and help define key analysis steps, including triggers. We discuss specific examples how LHC anomalies can be define…
Symmetries, Safety, and Self-Supervision
Barry M. Dillon, Gregor Kasieczka, Hans Olischlager +3
Collider searches face the challenge of defining a representation of high-dimensional data such that physical symmetries are manifest, the discriminating features are retained, and…
Bayesian Probabilistic Modelling for Four-Tops at the LHC
Ezequiel Alvarez, Barry M. Dillon, Darius A. Faroughy +3
Monte Carlo (MC) generators are crucial for analyzing data in particle collider experiments. However, often even a small mismatch between the MC simulations and the measurements ca…
Better Latent Spaces for Better Autoencoders
Barry M. Dillon, Tilman Plehn, Christof Sauer +1
Autoencoders as tools behind anomaly searches at the LHC have the structural problem that they only work in one direction, extracting jets with higher complexity but not the other…
Bump Hunting in Latent Space
Blaž Bortolato, Barry M. Dillon, Jernej F. Kamenik +1
Unsupervised anomaly detection could be crucial in future analyses searching for rare phenomena in large datasets, as for example collected at the LHC. To this end, we introduce a…
The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics
Gregor Kasieczka, Benjamin Nachman, David Shih +44
A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. I…
A comparison of optimisation algorithms for high-dimensional particle and astrophysics applications
The DarkMachines High Dimensional Sampling Group, Csaba Balázs, Melissa van Beekveld +18
Optimisation problems are ubiquitous in particle and astrophysics, and involve locating the optimum of a complicated function of many parameters that may be computationally expensi…
Learning the latent structure of collider events
Barry M. Dillon, Darius A. Faroughy, Jernej F. Kamenik +1
We describe a technique to learn the underlying structure of collider events directly from the data, without having a particular theoretical model in mind. It allows to infer aspec…
Reinterpretation of LHC Results for New Physics: Status and Recommendations after Run 2
Waleed Abdallah, Shehu AbdusSalam, Azar Ahmadov +139
We report on the status of efforts to improve the reinterpretation of searches and measurements at the LHC in terms of models for new physics, in the context of the LHC Reinterpret…
Dilaton portal in strongly interacting twin Higgs models
Aqeel Ahmed, Barry M. Dillon, Saereh Najjari
We consider a strongly interacting twin Higgs (SITH) model where an ultraviolet completion of twin Higgs mechanism is realized by a strongly coupled approximately scale invariant t…
Higgs boson potential at colliders: status and perspectives
B. Di Micco, M. Gouzevitch, J. Mazzitelli +104
This document summarises the current theoretical and experimental status of the di-Higgs boson production searches, and of the direct and indirect constraints on the Higgs boson se…
Axion-like-particle decay in strong electromagnetic backgrounds
B. King, B. M. Dillon, K. A. Beyer +1
The decay of a massive pseudoscalar, scalar and U(1) boson into an electron-positron pair in the presence of strong electromagnetic backgrounds is calculated. Of particular interes…
Composite Higgs at high transverse momentum
Andrea Banfi, Barry M. Dillon, Wissarut Ketaiam +1
In this paper we explore composite Higgs scenarios through the effects of light top-partners in Higgs+Jet production at the LHC. The pseudo-Goldstone boson nature of the Higgs fiel…
Uncovering latent jet substructure
Barry M. Dillon, Darius A. Faroughy, Jernej F. Kamenik
We apply techniques from Bayesian generative statistical modeling to uncover hidden features in jet substructure observables that discriminate between different a priori unknown un…