1,012 research outputs found
Topology classification with deep learning to improve real-time event selection at the LHC
We show how event topology classification based on deep learning could be used to improve the purity of data samples selected in real time at at the Large Hadron Collider. We consider different data representations, on which different kinds of multi-class classifiers are trained. Both raw data and high-level features are utilized. In the considered examples, a filter based on the classifier's score can be trained to retain ~99% of the interesting events and reduce the false-positive rate by as much as one order of magnitude for certain background processes. By operating such a filter as part of the online event selection infrastructure of the LHC experiments, one could benefit from a more flexible and inclusive selection strategy while reducing the amount of downstream resources wasted in processing false positives. The saved resources could be translated into a reduction of the detector operation cost or into an effective increase of storage and processing capabilities, which could be reinvested to extend the physics reach of the LHC experiments
Variational Autoencoders for New Physics Mining at the Large Hadron Collider
Using variational autoencoders trained on known physics processes, we develop
a one-sided threshold test to isolate previously unseen processes as outlier
events. Since the autoencoder training does not depend on any specific new
physics signature, the proposed procedure doesn't make specific assumptions on
the nature of new physics. An event selection based on this algorithm would be
complementary to classic LHC searches, typically based on model-dependent
hypothesis testing. Such an algorithm would deliver a list of anomalous events,
that the experimental collaborations could further scrutinize and even release
as a catalog, similarly to what is typically done in other scientific domains.
Event topologies repeating in this dataset could inspire new-physics model
building and new experimental searches. Running in the trigger system of the
LHC experiments, such an application could identify anomalous events that would
be otherwise lost, extending the scientific reach of the LHC.Comment: 29 pages, 12 figures, 5 table
Boosting with Machine Learning
High Higgs production at hadron colliders provides a direct probe of
the internal structure of the loop with the decay
offering the most statistics due to the large branching ratio. Despite the
overwhelming QCD background, recent advances in jet substructure have put the
observation of the channel at the LHC within the realm
of possibility. In order to enhance the sensitivity to this process, we develop
a two stream convolutional neural network, with one stream acting on jet
information and one using global event properties. The neural network
significantly increases the discovery potential of a Higgs signal, both for
high Standard Model production as well for possible beyond the Standard
Model contributions. Unlike most studies for boosted hadronically decaying
massive particles, the boosted Higgs search is unique because double
-tagging rejects nearly all background processes that do not have two hard
prongs. In this context --- which goes beyond state-of-the-art two-prong
tagging --- the network is studied to identify the origin of the additional
information leading to the increased significance. The procedures described
here are also applicable to related final states where they can be used to
identify additional sources of discrimination power that are not being
exploited by current techniques.Comment: 26 pages, 12 figures. v3: Updated to journal versio
Reducing model bias in a deep learning classifier using domain adversarial neural networks in the MINERvA experiment
We present a simulation-based study using deep convolutional neural networks
(DCNNs) to identify neutrino interaction vertices in the MINERvA passive
targets region, and illustrate the application of domain adversarial neural
networks (DANNs) in this context. DANNs are designed to be trained in one
domain (simulated data) but tested in a second domain (physics data) and
utilize unlabeled data from the second domain so that during training only
features which are unable to discriminate between the domains are promoted.
MINERvA is a neutrino-nucleus scattering experiment using the NuMI beamline at
Fermilab. -dependent cross sections are an important part of the physics
program, and these measurements require vertex finding in complicated events.
To illustrate the impact of the DANN we used a modified set of simulation in
place of physics data during the training of the DANN and then used the label
of the modified simulation during the evaluation of the DANN. We find that deep
learning based methods offer significant advantages over our prior track-based
reconstruction for the task of vertex finding, and that DANNs are able to
improve the performance of deep networks by leveraging available unlabeled data
and by mitigating network performance degradation rooted in biases in the
physics models used for training.Comment: 41 page
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