2,387 research outputs found
Convolutional Neural Networks Applied to Neutrino Events in a Liquid Argon Time Projection Chamber
We present several studies of convolutional neural networks applied to data
coming from the MicroBooNE detector, a liquid argon time projection chamber
(LArTPC). The algorithms studied include the classification of single particle
images, the localization of single particle and neutrino interactions in an
image, and the detection of a simulated neutrino event overlaid with cosmic ray
backgrounds taken from real detector data. These studies demonstrate the
potential of convolutional neural networks for particle identification or event
detection on simulated neutrino interactions. We also address technical issues
that arise when applying this technique to data from a large LArTPC at or near
ground level
Classifiers for centrality determination in proton-nucleus and nucleus-nucleus collisions
Centrality, as a geometrical property of the collision, is crucial for the
physical interpretation of nucleus-nucleus and proton-nucleus experimental
data. However, it cannot be directly accessed in event-by-event data analysis.
Common methods for centrality estimation in A-A and p-A collisions usually rely
on a single detector (either on the signal in zero-degree calorimeters or on
the multiplicity in some semi-central rapidity range). In the present work, we
made an attempt to develop an approach for centrality determination that is
based on machine-learning techniques and utilizes information from several
detector subsystems simultaneously. Different event classifiers are suggested
and evaluated for their selectivity power in terms of the number of
nucleons-participants and the impact parameter of the collision. Finer
centrality resolution may allow to reduce impact from so-called volume
fluctuations on physical observables being studied in heavy-ion experiments
like ALICE at the LHC and fixed target experiment NA61/SHINE on SPS.Comment: To be published in proceedings of the "XIIth Quark Confinement and
the Hadron Spectrum" conference (Thessaloniki, 2016
Identifying Heavy-Flavor Jets Using Vectors of Locally Aggregated Descriptors
Jets of collimated particles serve a multitude of purposes in high energy
collisions. Recently, studies of jet interaction with the quark-gluon plasma
(QGP) created in high energy heavy ion collisions are of growing interest,
particularly towards understanding partonic energy loss in the QGP medium and
its related modifications of the jet shower and fragmentation. Since the QGP is
a colored medium, the extent of jet quenching and consequently, the transport
properties of the medium are expected to be sensitive to fundamental properties
of the jets such as the flavor of the parton that initiates the jet.
Identifying the jet flavor enables an extraction of the mass dependence in
jet-QGP interactions. We present a novel approach to tagging heavy-flavor jets
at collider experiments utilizing the information contained within jet
constituents via the \texttt{JetVLAD} model architecture. We show the
performance of this model in proton-proton collisions at center of mass energy
GeV as characterized by common metrics and showcase its
ability to extract high purity heavy-flavor jet sample at various jet momenta
and realistic production cross-sections including a brief discussion on the
impact of out-of-time pile-up. Such studies open new opportunities for future
high purity heavy-flavor measurements at jet energies accessible at current and
future collider experiments.Comment: 18 pages, 6 figures and 3 tables. Accepted by JINS
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