7 papers
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…
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…
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…
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…
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…
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…