53 citations · 129 across the 11 of their papers we have counts for
12 papers
RAISE: LLM-based Automated Heuristic Design with Robust Adversary Instance Search
Fei Liu, Alessio Figalli, Patrick Owen +1
Automated Heuristic Design (AHD) with Large Language Models (LLMs) has shown remarkable progress in discovering high-quality heuristics. However, existing LLM-based AHD methods opt…
On the Codesign of Scientific Experiments and Industrial Systems
Tommaso Dorigo, Pietro Vischia, Shahzaib Abbas +84
The optimization of large experiments in fundamental science, such as detectors for subnuclear physics at particle colliders, shares with the optimization of complex systems for in…
Large Language Models for Physics Instrument Design
Sara Zoccheddu, Shah Rukh Qasim, Patrick Owen +1
We study the use of large language models (LLMs) for physics instrument design and compare their performance to reinforcement learning (RL). Using only prompting, LLMs are given ta…
Ultra-Fast Muon Transport via Histogram Sampling on GPUs
Luis Felipe P. Cattelan, Shah Rukh Qasim, Patrick H. Owen +1
We present a GPU-accelerated method for muon transport based on histogram sampling that delivers orders of magnitude faster performance than CPU-based Geant4 simulation. Our method…
Towards replacing detector simulation with heterogeneous GNNs in flavour physics analyses
Guillermo Hijano, Davide Lancierini, Alexander Mclean Marshall +8
Driven by the increasing volume of recorded data, the demand for simulation from experiments based at the Large Hadron Collider will rise sharply in the coming years. Addressing th…
Physics Instrument Design with Reinforcement Learning
Shah Rukh Qasim, Patrick Owen, Nicola Serra
We present a case for the use of Reinforcement Learning (RL) for the design of physics instrument as an alternative to gradient-based instrument-optimization methods. It's applicab…
FunTuple: A new N-tuple component for offline data processing at the LHCb experiment
Abhijit Mathad, Martina Ferrillo, Sacha Barré +5
The offline software framework of the LHCb experiment has undergone a significant overhaul to tackle the data processing challenges that will arise in the upcoming Run 3 and Run 4…
The DL Advocate: Playing the devil's advocate with hidden systematic uncertainties
Andrei Golutvin, Aleksandr Iniukhin, Andrea Mauri +3
We propose a new method based on machine learning to \emph{play the devil's advocate} and investigate the impact of unknown systematic effects in a quantitative way. This method pr…
A general effective field theory description of lepton universality ratios
Gino Isidori, Davide Lancierini, Abhijit Mathad +3
We construct an expression for a general lepton flavour universality (LFU) ratio, , in decays in terms of a series of hadronic quantities which can be treat…
On the significance of new physics in decays
Gino Isidori, Davide Lancierini, Patrick Owen +1
Motivated by deviations with respect to Standard Model predictions in decays, we evaluate the global significance of the new physics hypothesis in this system…
Study of the and states in decays
LHCb collaboration, R. Aaij, C. Abellán Beteta +956
The decays are studied using a data set corresponding to an integrated luminosity of 9fb collected with the LHCb detector in proton-proton co…
Probing effects of new physics in decays
Martina Ferrillo, Abhijit Mathad, Patrick Owen +1
We present, for the first time, the six-fold differential decay density expression for , taking into account the polarisation of the baryon a…