7,195 research outputs found
Machine learning challenges in theoretical HEP
In these proceedings we perform a brief review of machine learning (ML)
applications in theoretical High Energy Physics (HEP-TH). We start the
discussion by defining and then classifying machine learning tasks in
theoretical HEP. We then discuss some of the most popular and recent published
approaches with focus on a relevant case study topic: the determination of
parton distribution functions (PDFs) and related tools. Finally, we provide an
outlook about future applications and developments due to the synergy between
ML and HEP-TH.Comment: 7 pages, 3 figures, in proceedings of the 18th International Workshop
on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2017
Learning to Classify from Impure Samples with High-Dimensional Data
A persistent challenge in practical classification tasks is that labeled
training sets are not always available. In particle physics, this challenge is
surmounted by the use of simulations. These simulations accurately reproduce
most features of data, but cannot be trusted to capture all of the complex
correlations exploitable by modern machine learning methods. Recent work in
weakly supervised learning has shown that simple, low-dimensional classifiers
can be trained using only the impure mixtures present in data. Here, we
demonstrate that complex, high-dimensional classifiers can also be trained on
impure mixtures using weak supervision techniques, with performance comparable
to what could be achieved with pure samples. Using weak supervision will
therefore allow us to avoid relying exclusively on simulations for
high-dimensional classification. This work opens the door to a new regime
whereby complex models are trained directly on data, providing direct access to
probe the underlying physics.Comment: 6 pages, 2 tables, 2 figures. v2: updated to match PRD versio
The Machine Learning Landscape of Top Taggers
Based on the established task of identifying boosted, hadronically decaying
top quarks, we compare a wide range of modern machine learning approaches.
Unlike most established methods they rely on low-level input, for instance
calorimeter output. While their network architectures are vastly different,
their performance is comparatively similar. In general, we find that these new
approaches are extremely powerful and great fun.Comment: Yet another tagger included
Direct optimisation of the discovery significance when training neural networks to search for new physics in particle colliders
We introduce two new loss functions designed to directly optimise the
statistical significance of the expected number of signal events when training
neural networks to classify events as signal or background in the scenario of a
search for new physics at a particle collider. The loss functions are designed
to directly maximise commonly used estimates of the statistical significance,
, and the Asimov estimate, . We consider their use in a toy
SUSY search with 30~fb of 14~TeV data collected at the LHC. In the case
that the search for the SUSY model is dominated by systematic uncertainties, it
is found that the loss function based on can outperform the binary cross
entropy in defining an optimal search region
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