525 research outputs found
On the Topic of Jets: Disentangling Quarks and Gluons at Colliders
We introduce jet topics: a framework to identify underlying classes of jets
from collider data. Because of a close mathematical relationship between
distributions of observables in jets and emergent themes in sets of documents,
we can apply recent techniques in "topic modeling" to extract jet topics from
data with minimal or no input from simulation or theory. As a proof of concept
with parton shower samples, we apply jet topics to determine separate quark and
gluon jet distributions for constituent multiplicity. We also determine
separate quark and gluon rapidity spectra from a mixed Z-plus-jet sample. While
jet topics are defined directly from hadron-level multi-differential cross
sections, one can also predict jet topics from first-principles theoretical
calculations, with potential implications for how to define quark and gluon
jets beyond leading-logarithmic accuracy. These investigations suggest that jet
topics will be useful for extracting underlying jet distributions and fractions
in a wide range of contexts at the Large Hadron Collider.Comment: 8 pages, 4 figures, 1 table. v2: Improved discussion to match PRL
versio
Energy flow polynomials: A complete linear basis for jet substructure
We introduce the energy flow polynomials: a complete set of jet substructure
observables which form a discrete linear basis for all infrared- and
collinear-safe observables. Energy flow polynomials are multiparticle energy
correlators with specific angular structures that are a direct consequence of
infrared and collinear safety. We establish a powerful graph-theoretic
representation of the energy flow polynomials which allows us to design
efficient algorithms for their computation. Many common jet observables are
exact linear combinations of energy flow polynomials, and we demonstrate the
linear spanning nature of the energy flow basis by performing regression for
several common jet observables. Using linear classification with energy flow
polynomials, we achieve excellent performance on three representative jet
tagging problems: quark/gluon discrimination, boosted W tagging, and boosted
top tagging. The energy flow basis provides a systematic framework for complete
investigations of jet substructure using linear methods.Comment: 41+15 pages, 13 figures, 5 tables; v2: updated to match JHEP versio
Classification without labels: Learning from mixed samples in high energy physics
Modern machine learning techniques can be used to construct powerful models
for difficult collider physics problems. In many applications, however, these
models are trained on imperfect simulations due to a lack of truth-level
information in the data, which risks the model learning artifacts of the
simulation. In this paper, we introduce the paradigm of classification without
labels (CWoLa) in which a classifier is trained to distinguish statistical
mixtures of classes, which are common in collider physics. Crucially, neither
individual labels nor class proportions are required, yet we prove that the
optimal classifier in the CWoLa paradigm is also the optimal classifier in the
traditional fully-supervised case where all label information is available.
After demonstrating the power of this method in an analytical toy example, we
consider a realistic benchmark for collider physics: distinguishing quark-
versus gluon-initiated jets using mixed quark/gluon training samples. More
generally, CWoLa can be applied to any classification problem where labels or
class proportions are unknown or simulations are unreliable, but statistical
mixtures of the classes are available.Comment: 18 pages, 5 figures; v2: intro extended and references added; v3:
additional discussion to match JHEP versio
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
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