1,010 research outputs found
Quantum-inspired Machine Learning on high-energy physics data
Tensor Networks, a numerical tool originally designed for simulating quantum
many-body systems, have recently been applied to solve Machine Learning
problems. Exploiting a tree tensor network, we apply a quantum-inspired machine
learning technique to a very important and challenging big data problem in high
energy physics: the analysis and classification of data produced by the Large
Hadron Collider at CERN. In particular, we present how to effectively classify
so-called b-jets, jets originating from b-quarks from proton-proton collisions
in the LHCb experiment, and how to interpret the classification results. We
exploit the Tensor Network approach to select important features and adapt the
network geometry based on information acquired in the learning process.
Finally, we show how to adapt the tree tensor network to achieve optimal
precision or fast response in time without the need of repeating the learning
process. These results pave the way to the implementation of high-frequency
real-time applications, a key ingredient needed among others for current and
future LHCb event classification able to trigger events at the tens of MHz
scale.Comment: 13 pages, 4 figure
Preliminary Report on the Study of Beam-Induced Background Effects at a Muon Collider
Physics at a multi-TeV muon collider needs a change of perspective for the
detector design due to the large amount of background induced by muon beam
decays. Preliminary studies, based on simulated data, on the composition and
the characteristics of the particles originated from the muon decays and
reaching the detectors are presented here. The reconstruction performance of
the physics processes and has been investigated
for the time being without the effect of the machine induced background. A
preliminary study of the environment hazard due to the radiation induced by
neutrino interactions with the matter is presented using the FLUKA simulation
program
Quantum Machine Learning for -jet charge identification
Machine Learning algorithms have played an important role in hadronic jet
classification problems. The large variety of models applied to Large Hadron
Collider data has demonstrated that there is still room for improvement. In
this context Quantum Machine Learning is a new and almost unexplored
methodology, where the intrinsic properties of quantum computation could be
used to exploit particles correlations for improving the jet classification
performance. In this paper, we present a brand new approach to identify if a
jet contains a hadron formed by a or quark at the moment of
production, based on a Variational Quantum Classifier applied to simulated data
of the LHCb experiment. Quantum models are trained and evaluated using LHCb
simulation. The jet identification performance is compared with a Deep Neural
Network model to assess which method gives the better performance
CDF experience with monte carlo production using LCG grid
The upgrades of the Tevatron collider and CDF detector have considerably increased the demand on computing resources, in particular for Monte Carlo production. This has forced the collaboration to move beyond the usage of dedicated resources and start exploiting the Grid. The CDF Analysis Farm (CAF) model has been reimplemented into LcgCAF in order to access Grid resources by using the LCG/EGEE middleware. Many sites in Italy and in Europe are accessed through this portal by CDF users mainly to produce Monte Carlo data but also for other analysis jobs. We review here the setup used to submit jobs to Grid sites and retrieve the output, including CDF-specific configuration of some Grid components. We also describe the batch and interactive monitor tools developed to allow users to verify the jobs status during their lifetime in the Grid environment. Finally we analyze the efficiency and typical failure modes of the current Grid infrastructure reporting the performances of different parts of the system used
A new CDF model for data movement based on SRM
Being a large international collaboration established well before the full development of the Grid as the main computing tool for High Energy Physics, CDF has recently changed and improved its computing model, decentralizing some parts of it in order to be able to exploit the rising number of distributed resources available nowadays. Despite those efforts, while the large majority of CDF Monte Carlo production has moved to the Grid, data processing is still mainly performed in dedicated farms hosted at FNAL, requiring a centralized management of data and Monte Carlo samples needed for physics analysis. This rises the question on how to manage the transfer of produced Monte Carlo samples from remote Grid sites to FNAL in an efficient way; up to now CDF has relied on a non scalable centralized solution based on dedicated data servers accessed through rcp protocol, which has proven to be unsatisfactory. A new data transfer model has been designed that uses SRMs as local caches for remote Monte Carlo production sites, interfaces them with SAM, the experiment data catalog, and finally realizes the file movement exploiting the features provided by the data catalog transfer layer. We describe here the model and its integration within the current CDF computing architecture
Crilin: A Semi-Homogeneous Calorimeter for a Future Muon Collider
Calorimeters, as other detectors, have to face the increasing performance demands of the new energy frontier experiments. For a future Muon Collider the main challenge is given by the Beam Induced Background that may pose limitations to the physics performance. However, it is possible to reduce the BIB impact by exploiting some of its characteristics by ensuring high granularity, excellent timing, longitudinal segmentation and good energy resolution. The proposed design, the Crilin calorimeter, is an alternative semi-homogeneous ECAL barrel for the Muon Collider based on Lead Fluoride Crystals (PbF2) with a surface-mount UV-extended Silicon Photomultipliers (SiPMs) readout with an optimized design for a future Muon Collider
Observation of Hadronic W Decays in t-tbar Events with the Collider Detector at Fermilab
We observe hadronic W decays in t-tbar -> W (-> l nu) + >= 4 jet events using
a 109 pb-1 data sample of p-pbar collisions at sqrt{s} = 1.8 TeV collected with
the Collider Detector at Fermilab (CDF). A peak in the dijet invariant mass
distribution is obtained that is consistent with W decay and inconsistent with
the background prediction by 3.3 standard deviations. From this peak we measure
the W mass to be 77.2 +- 4.6 (stat+syst) GeV/c^2. This result demonstrates the
presence of two W bosons in t-tbar candidates in the W (-> l nu) + >= 4 jet
channel.Comment: 20 pages, 4 figures, submitted to PR
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