18,214 research outputs found
An efficient behavior classifier based on distributions of relevant events
Proceeding of: European Conference on Artificial Intelligence (ECAI 2008). Patras, Greece, july, 21st-25th, 2008.This work has been supported by the Spanish Ministry of Education and Science under project TRA-2007-67374-C02-02.Publicad
(Machine) Learning to Do More with Less
Determining the best method for training a machine learning algorithm is
critical to maximizing its ability to classify data. In this paper, we compare
the standard "fully supervised" approach (that relies on knowledge of
event-by-event truth-level labels) with a recent proposal that instead utilizes
class ratios as the only discriminating information provided during training.
This so-called "weakly supervised" technique has access to less information
than the fully supervised method and yet is still able to yield impressive
discriminating power. In addition, weak supervision seems particularly well
suited to particle physics since quantum mechanics is incompatible with the
notion of mapping an individual event onto any single Feynman diagram. We
examine the technique in detail -- both analytically and numerically -- with a
focus on the robustness to issues of mischaracterizing the training samples.
Weakly supervised networks turn out to be remarkably insensitive to systematic
mismodeling. Furthermore, we demonstrate that the event level outputs for
weakly versus fully supervised networks are probing different kinematics, even
though the numerical quality metrics are essentially identical. This implies
that it should be possible to improve the overall classification ability by
combining the output from the two types of networks. For concreteness, we apply
this technology to a signature of beyond the Standard Model physics to
demonstrate that all these impressive features continue to hold in a scenario
of relevance to the LHC.Comment: 32 pages, 12 figures. Example code is provided at
https://github.com/bostdiek/PublicWeaklySupervised . v3: Version published in
JHEP, discussion adde
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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