2,968 research outputs found
JUNIPR: a Framework for Unsupervised Machine Learning in Particle Physics
In applications of machine learning to particle physics, a persistent
challenge is how to go beyond discrimination to learn about the underlying
physics. To this end, a powerful tool would be a framework for unsupervised
learning, where the machine learns the intricate high-dimensional contours of
the data upon which it is trained, without reference to pre-established labels.
In order to approach such a complex task, an unsupervised network must be
structured intelligently, based on a qualitative understanding of the data. In
this paper, we scaffold the neural network's architecture around a
leading-order model of the physics underlying the data. In addition to making
unsupervised learning tractable, this design actually alleviates existing
tensions between performance and interpretability. We call the framework
JUNIPR: "Jets from UNsupervised Interpretable PRobabilistic models". In this
approach, the set of particle momenta composing a jet are clustered into a
binary tree that the neural network examines sequentially. Training is
unsupervised and unrestricted: the network could decide that the data bears
little correspondence to the chosen tree structure. However, when there is a
correspondence, the network's output along the tree has a direct physical
interpretation. JUNIPR models can perform discrimination tasks, through the
statistically optimal likelihood-ratio test, and they permit visualizations of
discrimination power at each branching in a jet's tree. Additionally, JUNIPR
models provide a probability distribution from which events can be drawn,
providing a data-driven Monte Carlo generator. As a third application, JUNIPR
models can reweight events from one (e.g. simulated) data set to agree with
distributions from another (e.g. experimental) data set.Comment: 37 pages, 24 figure
An operational definition of quark and gluon jets
While "quark" and "gluon" jets are often treated as separate, well-defined
objects in both theoretical and experimental contexts, no precise, practical,
and hadron-level definition of jet flavor presently exists. To remedy this
issue, we develop and advocate for a data-driven, operational definition of
quark and gluon jets that is readily applicable at colliders. Rather than
specifying a per-jet flavor label, we aggregately define quark and gluon jets
at the distribution level in terms of measured hadronic cross sections.
Intuitively, quark and gluon jets emerge as the two maximally separable
categories within two jet samples in data. Benefiting from recent work on
data-driven classifiers and topic modeling for jets, we show that the practical
tools needed to implement our definition already exist for experimental
applications. As an informative example, we demonstrate the power of our
operational definition using Z+jet and dijet samples, illustrating that pure
quark and gluon distributions and fractions can be successfully extracted in a
fully well-defined manner.Comment: 38 pages, 10 figures, 1 table; v2: updated to match JHEP versio
Supervised deep learning in high energy phenomenology: a mini review
Deep learning, a branch of machine learning, have been recently applied to
high energy experimental and phenomenological studies. In this note we give a
brief review on those applications using supervised deep learning. We first
describe various learning models and then recapitulate their applications to
high energy phenomenological studies. Some detailed applications are delineated
in details, including the machine learning scan in the analysis of new physics
parameter space, the graph neural networks in the search of top-squark
production and in the measurement of the top-Higgs coupling at the LHC.Comment: Invited review, 72 pages, 24 figure
Interaction networks for the identification of boosted decays
We develop an algorithm based on an interaction network to identify
high-transverse-momentum Higgs bosons decaying to bottom quark-antiquark pairs
and distinguish them from ordinary jets that reflect the configurations of
quarks and gluons at short distances. The algorithm's inputs are features of
the reconstructed charged particles in a jet and the secondary vertices
associated with them. Describing the jet shower as a combination of
particle-to-particle and particle-to-vertex interactions, the model is trained
to learn a jet representation on which the classification problem is optimized.
The algorithm is trained on simulated samples of realistic LHC collisions,
released by the CMS Collaboration on the CERN Open Data Portal. The interaction
network achieves a drastic improvement in the identification performance with
respect to state-of-the-art algorithms.Comment: 20 pages, 8 figures, 6 tables, version published in PR
Modern Machine Learning for LHC Physicists
Modern machine learning is transforming particle physics, faster than we can
follow, and bullying its way into our numerical tool box. For young researchers
it is crucial to stay on top of this development, which means applying
cutting-edge methods and tools to the full range of LHC physics problems. These
lecture notes are meant to lead students with basic knowledge of particle
physics and significant enthusiasm for machine learning to relevant
applications as fast as possible. They start with an LHC-specific motivation
and a non-standard introduction to neural networks and then cover
classification, unsupervised classification, generative networks, and inverse
problems. Two themes defining much of the discussion are well-defined loss
functions reflecting the problem at hand and uncertainty-aware networks. As
part of the applications, the notes include some aspects of theoretical LHC
physics. All examples are chosen from particle physics publications of the last
few years. Given that these notes will be outdated already at the time of
submission, the week of ML4Jets 2022, they will be updated frequently.Comment: First version, we very much appreciate feedbac
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