199 research outputs found
Semi-supervised Graph Neural Networks for Pileup Noise Removal
The high instantaneous luminosity of the CERN Large Hadron Collider leads to
multiple proton-proton interactions in the same or nearby bunch crossings
(pileup). Advanced pileup mitigation algorithms are designed to remove this
noise from pileup particles and improve the performance of crucial physics
observables. This study implements a semi-supervised graph neural network for
particle-level pileup noise removal, by identifying individual particles
produced from pileup. The graph neural network is firstly trained on charged
particles with known labels, which can be obtained from detector measurements
on data or simulation, and then inferred on neutral particles for which such
labels are missing. This semi-supervised approach does not depend on the ground
truth information from simulation and thus allows us to perform training
directly on experimental data. The performance of this approach is found to be
consistently better than widely-used domain algorithms and comparable to the
fully-supervised training using simulation truth information. The study serves
as the first attempt at applying semi-supervised learning techniques to pileup
mitigation, and opens up a new direction of fully data-driven machine learning
pileup mitigation studies
ABCNet: An attention-based method for particle tagging
In high energy physics, graph-based implementations have the advantage of
treating the input data sets in a similar way as they are collected by collider
experiments. To expand on this concept, we propose a graph neural network
enhanced by attention mechanisms called ABCNet. To exemplify the advantages and
flexibility of treating collider data as a point cloud, two physically
motivated problems are investigated: quark-gluon discrimination and pileup
reduction. The former is an event-by-event classification while the latter
requires each reconstructed particle to receive a classification score. For
both tasks ABCNet shows an improved performance compared to other algorithms
available.Comment: 13 pages, 5 figure
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
Pile-Up Mitigation using Attention
Particle production from secondary proton-proton collisions, commonly
referred to as pile-up, impair the sensitivity of both new physics searches and
precision measurements at LHC experiments. We propose a novel algorithm, PUMA,
for identifying pile-up objects with the help of deep neural networks based on
sparse transformers. These attention mechanisms were developed for natural
language processing but have become popular in other applications. In a
realistic detector simulation, our method outperforms classical benchmark
algorithms for pile-up mitigation in key observables. It provides a perspective
for mitigating the effects of pile-up in the high luminosity era of the LHC,
where up to 200 proton-proton collisions are expected to occur simultaneously
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