19 research outputs found
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-
Knowledge Distillation for Anomaly Detection
Unsupervised deep learning techniques are widely used to identify anomalous
behaviour. The performance of such methods is a product of the amount of
training data and the model size. However, the size is often a limiting factor
for the deployment on resource-constrained devices. We present a novel
procedure based on knowledge distillation for compressing an unsupervised
anomaly detection model into a supervised deployable one and we suggest a set
of techniques to improve the detection sensitivity. Compressed models perform
comparably to their larger counterparts while significantly reducing the size
and memory footprint
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
Report of the Topical Group on Higgs Physics for Snowmass 2021: The Case for Precision Higgs Physics
A future Higgs Factory will provide improved precision on measurements of
Higgs couplings beyond those obtained by the LHC, and will enable a broad range
of investigations across the fields of fundamental physics, including the
mechanism of electroweak symmetry breaking, the origin of the masses and mixing
of fundamental particles, the predominance of matter over antimatter, and the
nature of dark matter. Future colliders will measure Higgs couplings to a few
per cent, giving a window to beyond the Standard Model (BSM) physics in the
1-10 TeV range. In addition, they will make precise measurements of the Higgs
width, and characterize the Higgs self-coupling. This report details the work
of the EF01 and EF02 working groups for the Snowmass 2021 study.Comment: 44 pages, 40 figures, Report of the Topical Group on Higgs Physics
for Snowmass 2021. The first four authors are the Conveners, with
Contributions from the other author
W+Heavy Flavour Jet Measurements at CMS
The production of W bosons in association with b quarks is studied using proton-proton collisions at p s = 7 TeV in a data sample collected with the CMS experiment at the LHC corresponding to an integrated luminosity of 5 : 0 fb 1 . The W + b Ě„ b events are selected in the W ! mn decay mode by requiring a muon with transverse momentum p T > 25 GeV and pseudorapidity j h j 25 GeV and j h j < 2 : 4. The measured W + b Ě„ b production cross section in the fiducial volume, calculated at the level of final-state particles, is 0 : 53 0 : 05 ( stat. ) 0 : 09 ( syst. ) 0 : 06 ( th. ) 0 : 01 ( lum. ) pb, in agreement with the standard model prediction. In addition, kinematic distributions of the W + b Ě„ b system are measured and found to be in agreement with the predictions of a simulation using MADGRAPH and PYTHI
Elements: RAD discoveries for fundamental physics
Searches for ``unknown physics'' are made possible by observing data through a new ``lens'': Traditionally, discoveries of rare processes through particle and astro-particle experiments have relied on our ability to accurately predict new physics phenomena, and to subsequently selectively look for them within the data by using algorithms trained on \textit{predicted} unknowns. In a new era of scientific discovery, driven by unprecedented data statistics and Artificial Intelligence (AI) advances, we instead may now use AI-powered tools that let the data guide our expectation to selectively identify rare and unpredictable signatures that may lie within the data itself, and which may be signatures of new fundamental physics phenomena in nature.</p
Knowledge Distillation for Anomaly Detection
Unsupervised deep learning techniques are widely used to identify anomalous behaviour. The performance of such methods is a product of the amount of training data and the model size. However, the size is often a limiting factor for the deployment on resource-constrained devices. We present a novel procedure based on knowledge distillation for compressing an unsupervised anomaly detection model into a supervised deployable one and we suggest a set of techniques to improve the detection sensitivity. Compressed models perform comparably to their larger counterparts while significantly reducing the size and memory footprint
Symbolic Regression on FPGAs for Fast Machine Learning Inference
The high-energy physics community is investigating the feasibility of deploying machine-learning-based solutions on Field-Programmable Gate Arrays (FPGAs) to improve physics sensitivity while meeting data processing latency limitations. In this contribution, we introduce a novel end-to-end procedure that utilizes a machine learning technique called symbolic regression (SR). It searches equation space to discover algebraic relations approximating a dataset. We use PySR (software for uncovering these expressions based on evolutionary algorithm) and extend the functionality of hls4ml (a package for machine learning inference in FPGAs) to support PySR-generated expressions for resource-constrained production environments. Deep learning models often optimise the top metric by pinning the network size because vast hyperparameter space prevents extensive neural architecture search. Conversely, SR selects a set of models on the Pareto front, which allows for optimising the performance-resource tradeoff directly. By embedding symbolic forms, our implementation can dramatically reduce the computational resources needed to perform critical tasks. We validate our procedure on a physics benchmark: multiclass classification of jets produced in simulated proton-proton collisions at the CERN Large Hadron Collider, and show that we approximate a 3-layer neural network with an inference model that has as low as 5 ns execution time (a reduction by a factor of 13) and over 90% approximation accuracy
Graph Neural Networks for Charged Particle Tracking on FPGAs.
The determination of charged particle trajectories in collisions at the CERN Large Hadron Collider (LHC) is an important but challenging problem, especially in the high interaction density conditions expected during the future high-luminosity phase of the LHC (HL-LHC). Graph neural networks (GNNs) are a type of geometric deep learning algorithm that has successfully been applied to this task by embedding tracker data as a graph-nodes represent hits, while edges represent possible track segments-and classifying the edges as true or fake track segments. However, their study in hardware- or software-based trigger applications has been limited due to their large computational cost. In this paper, we introduce an automated translation workflow, integrated into a broader tool called hls4ml, for converting GNNs into firmware for field-programmable gate arrays (FPGAs). We use this translation tool to implement GNNs for charged particle tracking, trained using the TrackML challenge dataset, on FPGAs with designs targeting different graph sizes, task complexites, and latency/throughput requirements. This work could enable the inclusion of charged particle tracking GNNs at the trigger level for HL-LHC experiments