79 citations · 514 across the 32 of their papers we have counts for
33 papers
Another Fit Bites the Dust: Conformal Prediction as a Calibration Standard for Machine Learning in High-Energy Physics
Jack Y. Araz, Michael Spannowsky
Machine-learning techniques are essential in modern collider research, yet their probabilistic outputs often lack calibrated uncertainty estimates and finite-sample guarantees, lim…
Deciphering compressed electroweakino excesses with MadAnalysis 5
Jack Y. Araz, Benjamin Fuks, Mark D. Goodsell +1
We present version 1.11 of MadAnalysis 5, which extends the software package in several major ways to improve the handling of efficiency tables, the computation of observables in d…
Graph theory inspired anomaly detection at the LHC
Jack Y. Araz, Dimitrios Athanasakos, Mateusz Ploskon +1
Designing model-independent anomaly detection algorithms for analyzing LHC data remains a central challenge in the search for new physics, due to the high dimensionality of collide…
Point cloud-based diffusion models for the Electron-Ion Collider
Jack Y. Araz, Vinicius Mikuni, Felix Ringer +3
At high-energy collider experiments, generative models can be used for a wide range of tasks, including fast detector simulations, unfolding, searches of physics beyond the Standar…
Toward hybrid quantum simulations with qubits and qumodes on trapped-ion platforms
Jack Y. Araz, Matt Grau, Jake Montgomery +1
We explore the feasibility of gate-based hybrid quantum computing using both discrete (qubit) and continuous (qumode) variables on trapped-ion platforms. Trapped-ion systems have d…
The role of data embedding in quantum autoencoders for improved anomaly detection
Jack Y. Araz, Michael Spannowsky
The performance of Quantum Autoencoders (QAEs) in anomaly detection tasks is critically dependent on the choice of data embedding and ansatz design. This study explores the effects…
State preparation of lattice field theories using quantum optimal control
Jack Y. Araz, Siddhanth Bhowmick, Matt Grau +2
We explore the application of quantum optimal control (QOC) techniques to state preparation of lattice field theories on quantum computers. As a first example, we focus on the Schw…
Thermal state preparation of the SYK model using a variational quantum algorithm
Jack Y. Araz, Raghav G. Jha, Felix Ringer +1
We study the preparation of thermal states of the dense and sparse Sachdev-Ye-Kitaev (SYK) model using a variational quantum algorithm for Majorana fermions over a…
Les Houches guide to reusable ML models in LHC analyses
Jack Y. Araz, Andy Buckley, Gregor Kasieczka +10
With the increasing usage of machine-learning in high-energy physics analyses, the publication of the trained models in a reusable form has become a crucial question for analysis p…
Exploring thermal equilibria of the Fermi-Hubbard model with variational quantum algorithms
Jack Y. Araz, Michael Spannowsky, Matthew Wingate
This study investigates the thermal properties of the repulsive Fermi-Hubbard model with chemical potential using variational quantum algorithms, crucial in comprehending particle…
Spey: smooth inference for reinterpretation studies
Jack Y. Araz
Statistical models serve as the cornerstone for hypothesis testing in empirical studies. This paper introduces a new cross-platform Python-based package designed to utilise differe…
Searches for new physics with boosted top quarks in the MadAnalysis 5 and Rivet frameworks
Jack Y. Araz, Andy Buckley, Benjamin Fuks
High-momentum top quarks are a natural physical system in collider experiments for testing models of new physics, and jet substructure methods are key both to exploiting their larg…
Quantum-probabilistic Hamiltonian learning for generative modelling & anomaly detection
Jack Y. Araz, Michael Spannowsky
The Hamiltonian of an isolated quantum mechanical system determines its dynamics and physical behaviour. This study investigates the possibility of learning and utilising a system'…
Towards a Quantum Simulation of Nonlinear Sigma Models with a Topological Term
Jack Y. Araz, Sebastian Schenk, Michael Spannowsky
We determine the mass gap of a two-dimensional nonlinear sigma model augmented with a topological -term using tensor network and digital quantum algorithms. As proof of p…
Strength in numbers: optimal and scalable combination of LHC new-physics searches
Jack Y. Araz, Andy Buckley, Benjamin Fuks +4
To gain a comprehensive view of what the LHC tells us about physics beyond the Standard Model (BSM), it is crucial that different BSM-sensitive analyses can be combined. But in gen…
Signal region combination with full and simplified likelihoods in MadAnalysis 5
Gaël Alguero, Jack Y. Araz, Benjamin Fuks +1
The statistical combination of disjoint signal regions in reinterpretation studies uses more of the data of an analysis and gives more robust results than the single signal region…
Identifying magnetic antiskyrmions while they form with convolutional neural networks
Jack Y. Araz, Juan Carlos Criado, Michael Spannowsky
Chiral magnets have attracted a large amount of research interest in recent years because they support a variety of topological defects, such as skyrmions and bimerons, and allow f…
Classical versus Quantum: comparing Tensor Network-based Quantum Circuits on LHC data
Jack Y. Araz, Michael Spannowsky
Tensor Networks (TN) are approximations of high-dimensional tensors designed to represent locally entangled quantum many-body systems efficiently. This study provides a comprehensi…
Recasting LHC searches for long-lived particles with MadAnalysis 5
Jack Y. Araz, Benjamin Fuks, Mark D. Goodsell +1
We present an extension of the simplified fast detector simulator of MadAnalysis 5 - the SFS framework - with methods making it suitable for the treatment of long-lived particles o…
Cross-fertilising extra gauge boson searches at the LHC
Jack Y. Araz, Mariana Frank, Benjamin Fuks +2
For the purpose of cross-fertilising currently separate experimental approaches, we connect results of LHC analyses attempting to access the properties of additional and…
Quantum-inspired event reconstruction with Tensor Networks: Matrix Product States
Jack Y. Araz, Michael Spannowsky
Tensor Networks are non-trivial representations of high-dimensional tensors, originally designed to describe quantum many-body systems. We show that Tensor Networks are ideal vehic…
Elvet -- a neural network-based differential equation and variational problem solver
Jack Y. Araz, Juan Carlos Criado, Michael Spannowsky
We present Elvet, a Python package for solving differential equations and variational problems using machine learning methods. Elvet can deal with any system of coupled ordinary or…
Combine and Conquer: Event Reconstruction with Bayesian Ensemble Neural Networks
Jack Y. Araz, Michael Spannowsky
Ensemble learning is a technique where multiple component learners are combined through a protocol. We propose an Ensemble Neural Network (ENN) that uses the combined latent-featur…
Proceedings of the second MadAnalysis 5 workshop on LHC recasting in Korea
Benjamin Fuks, Pyungwon Ko, Seung J. Lee +32
We document the activities performed during the second MadAnalysis 5 workshop on LHC recasting, that was organised in KIAS (Seoul, Korea) on February 12-20, 2020. We detail the imp…