Conveners
3.3 Hardware acceleration, FPGAs & Uncertainty quantification
- Anastasios Belias (GSI Helmholtzzentrum für Schwerionenforschung GmbH)
Presentation materials
The matrix element method is the LHC inference method of choice for limited statistics. We present a dedicated machine learning framework, based on efficient phase-space integration, a learned acceptance and transfer function. It is based on a choice of INN and diffusion networks, and a transformer to solve jet combinatorics. We showcase this setup for the CP-phase of the top Yukawa coupling...
Uncertainty quantification (UQ) is crucial for reliable predictions in inverse problems, where the model parameters are inferred from limited and noisy data. Monte Carlo methods offer a powerful approach to quantifying uncertainty in inverse problems, but their effectiveness hinges on the accuracy of the input data. This talk explores the robustness of an inverse problem methodology that...
The problem of comparing two high-dimensional samples to test the null hypothesis that they are drawn from the same distribution is a fundamental question in statistical hypothesis testing. This study presents a comprehensive comparison of various non-parametric two-sample tests, specifically focusing on their statistical power in high-dimensional settings. The tests are built from univariate...
Particle physics experiments entail the collection of large data samples of complex information. In order to produce and detect low probability processes of interest (signal), a huge number of particle collisions must be carried out. This type of experiments produces huge sets of observations where most of them are of no interest (background). For this reason, a mechanism able to differentiate...
Adversarial deep learning techniques are based on changing input distributions (adversaries), with the goal of causing false classifications when input to a deep neural network classifier. Adversaries aim to maximize the output error while only exerting minimal perturbations to the input data. Moreover, various techniques to defend against such attacks have been developed in the past. While...
The quantum-chromodynamic substructure of hadrons at the smallest scales relies critically on the accurate interpretation of abundant experimental data generated by large-scale infrastructures such as the Large Hadron Collider. Comparing a multitude of measured cross sections with the latest higher-order theory predictions, we probe the validity of the standard model of particles with...
In recent years, disparities have emerged within the context of the concordance model regarding the estimated value of the Hubble constant H0 [1907.10625] using Cosmic Microwave Background (CMB) and Supernovae data (commonly referred to as the Hubble tension), the clustering σ8 [1610.04606] using CMB and weak lensing, and the curvature ΩK [1908.09139, 1911.02087] using CMB and lensing/BAO, and...
One of the most important challenges in High Energy Physics today is to find rare new physics signals among an abundance of Standard Model proton-proton collisions, also known as anomaly detection. Deep Learning (DL) based techniques for this anomaly detection problem are increasing in popularity [1]. One such DL technique is the Deep SVDD model [2], which shows great results when applied to...
The Compressed Baryonic Matter (CBM) experiment, located at the Facility for Antiproton and Ion Research (FAIR) accelerator complex in Darmstadt, Germany, aims to study the phase diagram of strongly interacting matter in the realm of high net baryon densities and moderate temperatures. The SIS-100 accelerator ring at FAIR produces accelerated beams up to the energies of about 30 GeV for...
Machine Learning (ML) techniques have been employed for the high energy physics (HEP) community since the early 80s to deal with a broad spectrum of problems. This work explores the prospects of using Deep Learning techniques to estimate elliptic flow (v2) in heavy-ion collisions at the RHIC and LHC energies. A novel method is developed to process the input
observables from track-level...
