scholarly journals Performance of a geometric deep learning pipeline for HL-LHC particle tracking

2021 ◽  
Vol 81 (10) ◽  
Author(s):  
Xiangyang Ju ◽  
Daniel Murnane ◽  
Paolo Calafiura ◽  
Nicholas Choma ◽  
Sean Conlon ◽  
...  
Keyword(s):  
Neural Networks ◽  
Particle Tracking ◽  
Metric Learning ◽  
Liquid Argon ◽  
Gpu Acceleration ◽  
New Developments ◽  
Tracking Detector ◽  
Computing Performance ◽  
Graph Neural Networks ◽  
Number Of Particles

AbstractThe Exa.TrkX project has applied geometric learning concepts such as metric learning and graph neural networks to HEP particle tracking. Exa.TrkX’s tracking pipeline groups detector measurements to form track candidates and filters them. The pipeline, originally developed using the TrackML dataset (a simulation of an LHC-inspired tracking detector), has been demonstrated on other detectors, including DUNE Liquid Argon TPC and CMS High-Granularity Calorimeter. This paper documents new developments needed to study the physics and computing performance of the Exa.TrkX pipeline on the full TrackML dataset, a first step towards validating the pipeline using ATLAS and CMS data. The pipeline achieves tracking efficiency and purity similar to production tracking algorithms. Crucially for future HEP applications, the pipeline benefits significantly from GPU acceleration, and its computational requirements scale close to linearly with the number of particles in the event.

scholarly journals Hybrid quantum classical graph neural networks for particle track reconstruction

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Cenk Tüysüz ◽  
Carla Rieger ◽  
Kristiane Novotny ◽  
Bilge Demirköz ◽  
Daniel Dobos ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Complex Geometry ◽  
Hadron Collider ◽  
Nuclear Research ◽  
Reconstruction Algorithms ◽  
Track Reconstruction ◽  
Instantaneous Rate ◽  
Tracking Detector ◽  
Graph Neural Networks

AbstractThe Large Hadron Collider (LHC) at the European Organisation for Nuclear Research (CERN) will be upgraded to further increase the instantaneous rate of particle collisions (luminosity) and become the High Luminosity LHC (HL-LHC). This increase in luminosity will significantly increase the number of particles interacting with the detector. The interaction of particles with a detector is referred to as “hit”. The HL-LHC will yield many more detector hits, which will pose a combinatorial challenge by using reconstruction algorithms to determine particle trajectories from those hits. This work explores the possibility of converting a novel graph neural network model, that can optimally take into account the sparse nature of the tracking detector data and their complex geometry, to a hybrid quantum-classical graph neural network that benefits from using variational quantum layers. We show that this hybrid model can perform similar to the classical approach. Also, we explore parametrized quantum circuits (PQC) with different expressibility and entangling capacities, and compare their training performance in order to quantify the expected benefits. These results can be used to build a future road map to further develop circuit-based hybrid quantum-classical graph neural networks.

scholarly journals Application of deep metric learning to molecular  similarity

2021 ◽  
Author(s):  
Damien Coupry ◽  
Peter Pogany
Keyword(s):  
Neural Networks ◽  
Drug Discovery ◽  
Molecular Similarity ◽  
Metric Learning ◽  
Zinc Database ◽  
Deep Metric Learning ◽  
Loss Method ◽  
Standard Practices ◽  
Graph Neural Networks ◽  
Definition Of

Abstract Graph based methods are increasingly important in chemistry and drug discovery, with applications ranging from QSAR to molecular generation. Combining graph neural networks and deep metric learning concepts, we expose a framework for quantifying molecular similarity based on learned embeddings separate from any endpoint. Using a minimal definition of similarity, and data from the ZINC database of public compounds, this work demonstrate the properties of the embedding and its suitability for a range of applications, among them a novel reconstruction loss method for training deep molecular auto-encoders. We also compare the performance of the embedding to standard practices, with a focus on known failure points and edge cases.

Clustering of electromagnetic showers and particle interactions with graph neural networks in liquid argon time projection chambers

2021 ◽  
Vol 104 (7) ◽  
Author(s):  
François Drielsma ◽  
Qing Lin ◽  
Pierre Côte de Soux ◽  
Laura Dominé ◽  
Ran Itay ◽  
...  
Keyword(s):  
Neural Networks ◽  
Liquid Argon ◽  
Particle Interactions ◽  
Electromagnetic Showers ◽  
Time Projection Chambers ◽  
Graph Neural Networks ◽  
Time Projection

Graph Neural Networks for Prediction of Fuel Ignition Quality

2020 ◽  
Author(s):  
Artur Schweidtmann ◽  
Jan Rittig ◽  
Andrea König ◽  
Martin Grohe ◽  
Alexander Mitsos ◽  
...  
Keyword(s):  
Neural Networks ◽  
Octane Number ◽  
Molecular Graph ◽  
Chemical Properties ◽  
Graph Representation ◽  
Structure Property ◽  
Oxygenated Hydrocarbons ◽  
Physico Chemical ◽  
Ignition Quality ◽  
Graph Neural Networks

<div>Prediction of combustion-related properties of (oxygenated) hydrocarbons is an important and challenging task for which quantitative structure-property relationship (QSPR) models are frequently employed. Recently, a machine learning method, graph neural networks (GNNs), has shown promising results for the prediction of structure-property relationships. GNNs utilize a graph representation of molecules, where atoms correspond to nodes and bonds to edges containing information about the molecular structure. More specifically, GNNs learn physico-chemical properties as a function of the molecular graph in a supervised learning setup using a backpropagation algorithm. This end-to-end learning approach eliminates the need for selection of molecular descriptors or structural groups, as it learns optimal fingerprints through graph convolutions and maps the fingerprints to the physico-chemical properties by deep learning. We develop GNN models for predicting three fuel ignition quality indicators, i.e., the derived cetane number (DCN), the research octane number (RON), and the motor octane number (MON), of oxygenated and non-oxygenated hydrocarbons. In light of limited experimental data in the order of hundreds, we propose a combination of multi-task learning, transfer learning, and ensemble learning. The results show competitive performance of the proposed GNN approach compared to state-of-the-art QSPR models making it a promising field for future research. The prediction tool is available via a web front-end at www.avt.rwth-aachen.de/gnn.</div>

Conversational Emotion Recognition Using Self-Attention Mechanisms and Graph Neural Networks

2020 ◽  
Author(s):  
Zheng Lian ◽  
Jianhua Tao ◽  
Bin Liu ◽  
Jian Huang ◽  
Zhanlei Yang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Emotion Recognition ◽  
Graph Neural Networks

MutualRec: Joint friend and item recommendations with mutualistic attentional graph neural networks

2020 ◽  
pp. 102954
Author(s):  
Yang Xiao ◽  
Qingqi Pei ◽  
Tingting Xiao ◽  
Lina Yao ◽  
Huan Liu
Keyword(s):  
Neural Networks ◽  
Graph Neural Networks
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