30 research outputs found
Equivariant Graph Neural Networks for Charged Particle Tracking
Graph neural networks (GNNs) have gained traction in high-energy physics
(HEP) for their potential to improve accuracy and scalability. However, their
resource-intensive nature and complex operations have motivated the development
of symmetry-equivariant architectures. In this work, we introduce EuclidNet, a
novel symmetry-equivariant GNN for charged particle tracking. EuclidNet
leverages the graph representation of collision events and enforces rotational
symmetry with respect to the detector's beamline axis, leading to a more
efficient model. We benchmark EuclidNet against the state-of-the-art
Interaction Network on the TrackML dataset, which simulates high-pileup
conditions expected at the High-Luminosity Large Hadron Collider (HL-LHC). Our
results show that EuclidNet achieves near-state-of-the-art performance at small
model scales (<1000 parameters), outperforming the non-equivariant benchmarks.
This study paves the way for future investigations into more resource-efficient
GNN models for particle tracking in HEP experiments.Comment: Proceedings submission to ACAT 2022. 7 page
Charged particle tracking via edge-classifying interaction networks
Recent work has demonstrated that geometric deep learning methods such as
graph neural networks (GNNs) are well suited to address a variety of
reconstruction problems in high energy particle physics. In particular,
particle tracking data is naturally represented as a graph by identifying
silicon tracker hits as nodes and particle trajectories as edges; given a set
of hypothesized edges, edge-classifying GNNs identify those corresponding to
real particle trajectories. In this work, we adapt the physics-motivated
interaction network (IN) GNN toward the problem of particle tracking in pileup
conditions similar to those expected at the high-luminosity Large Hadron
Collider. Assuming idealized hit filtering at various particle momenta
thresholds, we demonstrate the IN's excellent edge-classification accuracy and
tracking efficiency through a suite of measurements at each stage of GNN-based
tracking: graph construction, edge classification, and track building. The
proposed IN architecture is substantially smaller than previously studied GNN
tracking architectures; this is particularly promising as a reduction in size
is critical for enabling GNN-based tracking in constrained computing
environments. Furthermore, the IN may be represented as either a set of
explicit matrix operations or a message passing GNN. Efforts are underway to
accelerate each representation via heterogeneous computing resources towards
both high-level and low-latency triggering applications.Comment: This is a post-peer-review, pre-copyedit version of this article. The
final authenticated version is available online at:
https://doi.org/10.1007/s41781-021-00073-
Lifestyle and personal wellness in particle physics research activities
Finding a balance between professional responsibilities and personal
priorities is a great challenge of contemporary life and particularly within
the HEPAC community. Failure to achieve a proper balance often leads to
different degrees of mental and physical issues and affects work performance.
In this paper, we discuss some of the main causes that lead to the imbalance
between work and personal life in our academic field. We present some
recommendations in order to establish mechanisms to create a healthier and more
equitable work environment, for the different members of our community at the
different levels of their careers
Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs
We develop and study FPGA implementations of algorithms for charged particle
tracking based on graph neural networks. The two complementary FPGA designs are
based on OpenCL, a framework for writing programs that execute across
heterogeneous platforms, and hls4ml, a high-level-synthesis-based compiler for
neural network to firmware conversion. We evaluate and compare the resource
usage, latency, and tracking performance of our implementations based on a
benchmark dataset. We find a considerable speedup over CPU-based execution is
possible, potentially enabling such algorithms to be used effectively in future
computing workflows and the FPGA-based Level-1 trigger at the CERN Large Hadron
Collider.Comment: 8 pages, 4 figures, To appear in Third Workshop on Machine Learning
and the Physical Sciences (NeurIPS 2020
Physics and Computing Performance of the Exa.TrkX TrackML Pipeline
The Exa.TrkX project has applied geometric learning concepts such as metric
learning and graph neural networks to HEP particle tracking. The Exa.TrkX
tracking pipeline clusters detector measurements to form track candidates and
filters them. The pipeline, originally developed using the TrackML dataset (a
simulation of an LHC-like tracking detector), has been demonstrated on various
detectors, including the DUNE LArTPC and the 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
Data Science and Machine Learning in Education
The growing role of data science (DS) and machine learning (ML) in
high-energy physics (HEP) is well established and pertinent given the complex
detectors, large data, sets and sophisticated analyses at the heart of HEP
research. Moreover, exploiting symmetries inherent in physics data have
inspired physics-informed ML as a vibrant sub-field of computer science
research. HEP researchers benefit greatly from materials widely available
materials for use in education, training and workforce development. They are
also contributing to these materials and providing software to DS/ML-related
fields. Increasingly, physics departments are offering courses at the
intersection of DS, ML and physics, often using curricula developed by HEP
researchers and involving open software and data used in HEP. In this white
paper, we explore synergies between HEP research and DS/ML education, discuss
opportunities and challenges at this intersection, and propose community
activities that will be mutually beneficial.Comment: Contribution to Snowmass 202
Applications and Techniques for Fast Machine Learning in Science
In this community review report, we discuss applications and techniques for fast machine learning (ML) in science - the concept of integrating powerful ML methods into the real-time experimental data processing loop to accelerate scientific discovery. The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for training and implementing performant and resource-efficient ML algorithms; and computing architectures, platforms, and technologies for deploying these algorithms. We also present overlapping challenges across the multiple scientific domains where common solutions can be found. This community report is intended to give plenty of examples and inspiration for scientific discovery through integrated and accelerated ML solutions. This is followed by a high-level overview and organization of technical advances, including an abundance of pointers to source material, which can enable these breakthroughs
Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector
A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements