212 research outputs found
Quantum Associative Memory in HEP Track Pattern Recognition
We have entered the Noisy Intermediate-Scale Quantum Era. A plethora of
quantum processor prototypes allow evaluation of potential of the Quantum
Computing paradigm in applications to pressing computational problems of the
future. Growing data input rates and detector resolution foreseen in
High-Energy LHC (2030s) experiments expose the often high time and/or space
complexity of classical algorithms. Quantum algorithms can potentially become
the lower-complexity alternatives in such cases. In this work we discuss the
potential of Quantum Associative Memory (QuAM) in the context of LHC data
triggering. We examine the practical limits of storage capacity, as well as
store and recall errorless efficiency, from the viewpoints of the
state-of-the-art IBM quantum processors and LHC real-time charged track pattern
recognition requirements. We present a software prototype implementation of the
QuAM protocols and analyze the topological limitations for porting the simplest
QuAM instances to the public IBM 5Q and 14Q cloud-based superconducting chips.Comment: 8 pages, 6 figures, 1 table. Talk presented at CHEP2018. (v2) minor
stylistic revisions and typos correcte
Novel deep learning methods for track reconstruction
For the past year, the HEP.TrkX project has been investigating machine
learning solutions to LHC particle track reconstruction problems. A variety of
models were studied that drew inspiration from computer vision applications and
operated on an image-like representation of tracking detector data. While these
approaches have shown some promise, image-based methods face challenges in
scaling up to realistic HL-LHC data due to high dimensionality and sparsity. In
contrast, models that can operate on the spacepoint representation of track
measurements ("hits") can exploit the structure of the data to solve tasks
efficiently. In this paper we will show two sets of new deep learning models
for reconstructing tracks using space-point data arranged as sequences or
connected graphs. In the first set of models, Recurrent Neural Networks (RNNs)
are used to extrapolate, build, and evaluate track candidates akin to Kalman
Filter algorithms. Such models can express their own uncertainty when trained
with an appropriate likelihood loss function. The second set of models use
Graph Neural Networks (GNNs) for the tasks of hit classification and segment
classification. These models read a graph of connected hits and compute
features on the nodes and edges. They adaptively learn which hit connections
are important and which are spurious. The models are scaleable with simple
architecture and relatively few parameters. Results for all models will be
presented on ACTS generic detector simulated data.Comment: CTD 2018 proceeding
Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors
Pattern recognition problems in high energy physics are notably different
from traditional machine learning applications in computer vision.
Reconstruction algorithms identify and measure the kinematic properties of
particles produced in high energy collisions and recorded with complex detector
systems. Two critical applications are the reconstruction of charged particle
trajectories in tracking detectors and the reconstruction of particle showers
in calorimeters. These two problems have unique challenges and characteristics,
but both have high dimensionality, high degree of sparsity, and complex
geometric layouts. Graph Neural Networks (GNNs) are a relatively new class of
deep learning architectures which can deal with such data effectively, allowing
scientists to incorporate domain knowledge in a graph structure and learn
powerful representations leveraging that structure to identify patterns of
interest. In this work we demonstrate the applicability of GNNs to these two
diverse particle reconstruction problems.Comment: Presented at NeurIPS 2019 Workshop "Machine Learning and the Physical
Sciences
Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors
Pattern recognition problems in high energy physics are notably different
from traditional machine learning applications in computer vision.
Reconstruction algorithms identify and measure the kinematic properties of
particles produced in high energy collisions and recorded with complex detector
systems. Two critical applications are the reconstruction of charged particle
trajectories in tracking detectors and the reconstruction of particle showers
in calorimeters. These two problems have unique challenges and characteristics,
but both have high dimensionality, high degree of sparsity, and complex
geometric layouts. Graph Neural Networks (GNNs) are a relatively new class of
deep learning architectures which can deal with such data effectively, allowing
scientists to incorporate domain knowledge in a graph structure and learn
powerful representations leveraging that structure to identify patterns of
interest. In this work we demonstrate the applicability of GNNs to these two
diverse particle reconstruction problems
The HEP.TrkX Project: deep neural networks for HL-LHC online and offline tracking
Particle track reconstruction in dense environments such as the detectors of the High Luminosity Large Hadron Collider (HL-LHC) is a challenging pattern recognition problem. Traditional tracking algorithms such as the combinatorial Kalman Filter have been used with great success in LHC experiments for years. However, these state-of-the-art techniques are inherently sequential and scale poorly with the expected increases in detector occupancy in the HL-LHC conditions. The HEP.TrkX project is a pilot project with the aim to identify and develop cross-experiment solutions based on machine learning algorithms for track reconstruction. Machine learning algorithms bring a lot of potential to this problem thanks to their capability to model complex non-linear data dependencies, to learn effective representations of high-dimensional data through training, and to parallelize easily on high-throughput architectures such as GPUs. This contribution will describe our initial explorations into this relatively unexplored idea space. We will discuss the use of recurrent (LSTM) and convolutional neural networks to find and fit tracks in toy detector data
Graph Neural Network for Object Reconstruction in Liquid Argon Time Projection Chambers
This paper presents a graph neural network (GNN) technique for low-level
reconstruction of neutrino interactions in a Liquid Argon Time Projection
Chamber (LArTPC). GNNs are still a relatively novel technique, and have shown
great promise for similar reconstruction tasks in the LHC. In this paper, a
multihead attention message passing network is used to classify the
relationship between detector hits by labelling graph edges, determining
whether hits were produced by the same underlying particle, and if so, the
particle type. The trained model is 84% accurate overall, and performs best on
the EM shower and muon track classes. The model's strengths and weaknesses are
discussed, and plans for developing this technique further are summarised.Comment: 7 pages, 3 figures, submitted to the 25th International Conference on
Computing in High-Energy and Nuclear Physic
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