86,695 research outputs found
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
A population-based approach to background discrimination in particle physics
Background properties in experimental particle physics are typically
estimated using control samples corresponding to large numbers of events. This
can provide precise knowledge of average background distributions, but
typically does not consider the effect of fluctuations in a data set of
interest. A novel approach based on mixture model decomposition is presented as
a way to estimate the effect of fluctuations on the shapes of probability
distributions in a given data set, with a view to improving on the knowledge of
background distributions obtained from control samples. Events are treated as
heterogeneous populations comprising particles originating from different
processes, and individual particles are mapped to a process of interest on a
probabilistic basis. The proposed approach makes it possible to extract from
the data information about the effect of fluctuations that would otherwise be
lost using traditional methods based on high-statistics control samples. A
feasibility study on Monte Carlo is presented, together with a comparison with
existing techniques. Finally, the prospects for the development of tools for
intensive offline analysis of individual events at the Large Hadron Collider
are discussed.Comment: Updated according to the version published in J. Phys.: Conf. Ser.
Minor changes have been made to the text with respect to the published
article with a view to improving readabilit
PhysicsGP: A Genetic Programming Approach to Event Selection
We present a novel multivariate classification technique based on Genetic
Programming. The technique is distinct from Genetic Algorithms and offers
several advantages compared to Neural Networks and Support Vector Machines. The
technique optimizes a set of human-readable classifiers with respect to some
user-defined performance measure. We calculate the Vapnik-Chervonenkis
dimension of this class of learning machines and consider a practical example:
the search for the Standard Model Higgs Boson at the LHC. The resulting
classifier is very fast to evaluate, human-readable, and easily portable. The
software may be downloaded at: http://cern.ch/~cranmer/PhysicsGP.htmlComment: 16 pages 9 figures, 1 table. Submitted to Comput. Phys. Commu
Quantum-inspired Machine Learning on high-energy physics data
Tensor Networks, a numerical tool originally designed for simulating quantum
many-body systems, have recently been applied to solve Machine Learning
problems. Exploiting a tree tensor network, we apply a quantum-inspired machine
learning technique to a very important and challenging big data problem in high
energy physics: the analysis and classification of data produced by the Large
Hadron Collider at CERN. In particular, we present how to effectively classify
so-called b-jets, jets originating from b-quarks from proton-proton collisions
in the LHCb experiment, and how to interpret the classification results. We
exploit the Tensor Network approach to select important features and adapt the
network geometry based on information acquired in the learning process.
Finally, we show how to adapt the tree tensor network to achieve optimal
precision or fast response in time without the need of repeating the learning
process. These results pave the way to the implementation of high-frequency
real-time applications, a key ingredient needed among others for current and
future LHCb event classification able to trigger events at the tens of MHz
scale.Comment: 13 pages, 4 figure
The Inverse Bagging Algorithm: Anomaly Detection by Inverse Bootstrap Aggregating
For data sets populated by a very well modeled process and by another process
of unknown probability density function (PDF), a desired feature when
manipulating the fraction of the unknown process (either for enhancing it or
suppressing it) consists in avoiding to modify the kinematic distributions of
the well modeled one. A bootstrap technique is used to identify sub-samples
rich in the well modeled process, and classify each event according to the
frequency of it being part of such sub-samples. Comparisons with general MVA
algorithms will be shown, as well as a study of the asymptotic properties of
the method, making use of a public domain data set that models a typical search
for new physics as performed at hadronic colliders such as the Large Hadron
Collider (LHC).Comment: 8 pages, 5 figures. Proceedings of the XIIth Quark Confinement and
Hadron Spectrum conference, 28/8-2/9 2016, Thessaloniki, Greec
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