Related papers: Data Augmentation at the LHC through Analysis-spec…

This work presents a quantum convolutional neural network (QCNN) for the classification of high energy physics events. The proposed model is tested using a simulated dataset from the Deep Underground Neutrino Experiment. The proposed…

Deep convolutional neural networks have dominated the pattern recognition scene by providing much more accurate solutions in computer vision problems such as object recognition and object detection. Most of these solutions come at a huge…

The development of an LHC physics analysis involves numerous investigations that require the repeated processing of terabytes of data. Thus, a rapid completion of each of these analysis cycles is central to mastering the science project. We…

We demonstrate transfer learning capabilities in a machine-learned algorithm trained for particle-flow reconstruction in high energy particle colliders. This paper presents a cross-detector fine-tuning study, where we initially pretrain the…

As the particle physics community needs higher and higher precisions in order to test our current model of the subatomic world, larger and larger datasets are necessary. With upgrades scheduled for the detectors of colliding-beam…

We evaluate the performance of an LHCb-like detector using a fast simulation of proton-proton collisions at center-of-mass energies of 50 and 100 TeV. The study shows that detector acceptances and resolutions could be similar to those at…

A novel method is proposed here to precisely model the multi-dimensional features of QCD multi-jet events in hadron collisions. The method relies on the schematization of high-pT QCD processes as 2->2 reactions made complex by sub-leading…

We propose a new strategy to probe heavy neutrinos with non-universal fermion couplings at the Large Hadron Collider (LHC) using a novel production mechanism and machine-learning algorithms. Focusing on proton--proton collisions at…

This document describes the novel techniques used to simulate the common Snowmass 2013 Energy Frontier Standard Model backgrounds for future hadron colliders. The purpose of many Energy Frontier studies is to explore the reach of high…

Detecting extreme events in large datasets is a major challenge in climate science research. Current algorithms for extreme event detection are build upon human expertise in defining events based on subjective thresholds of relevant…

Reliable data quality monitoring is a key asset in delivering collision data suitable for physics analysis in any modern large-scale High Energy Physics experiment. This paper focuses on the use of artificial neural networks for supervised…

Data loading can dominate deep neural network training time on large-scale systems. We present a comprehensive study on accelerating data loading performance in large-scale distributed training. We first identify performance and scalability…

We introduce the Fastron, a configuration space (C-space) model to be used as a proxy to kinematic-based collision detection. The Fastron allows iterative updates to account for a changing environment through a combination of a novel…

The present work proposes an inflow turbulence generation strategy using deep learning methods. This is achieved with the help of an autoencoder architecture with two different types of operational layers in the latent-space: a fully…

The high cost of high-resolution computational fluid/flame dynamics (CFD) has hindered its application in combustion related design, research and optimization. In this study, we propose a new framework for turbulent combustion simulation…

Data analysis in high-energy physics (HEP) begins with data reduction, where vast datasets are filtered to extract relevant events. At the Large Hadron Collider (LHC), this process is bottlenecked by slow data transfers between storage and…

Deep-learning is a cutting edge theory that is being applied to many fields. For vision applications the Convolutional Neural Networks (CNN) are demanding significant accuracy for classification tasks. Numerous hardware accelerators have…

Data scouting, introduced by CMS in 2011, is the use of specialized data streams based on reduced event content, enabling LHC experiments to record unprecedented numbers of proton-proton collision events that would otherwise be rejected by…

Experimental High-Energy Physics (HEP), especially the Large Hadron Collider (LHC) programme at the European Organization for Nuclear Research (CERN), is one of the most computationally intensive activities in the world. This demand is set…

This paper presents a novel method for the reconstruction of interaction vertices in particle collision data. The algorithm is an agglomerative clustering technique designed for high-luminosity environments in current and future…