Deep learning for inferring cause of data anomalies

Daily operation of a large-scale experiment is a resource consuming task, particularly from perspectives of routine data quality monitoring. Typically, data comes from different sub-detectors and the global quality of data depends on the combinatorial performance of each of them. In this paper, the problem of identifying channels in which anomalies occurred is considered. We introduce a generic deep learning model and prove that, under reasonable assumptions, the model learns to identify 'channels' which are affected by an anomaly. Such model could be used for data quality manager cross-check and assistance and identifying good channels in anomalous data samples. The main novelty of the method is that the model does not require ground truth labels for each channel, only global flag is used. This effectively distinguishes the model from classical classification methods. Being applied to CMS data collected in the year 2010, this approach proves its ability to decompose anomaly by separate channels.Comment: Presented at ACAT 2017 conference, Seattle, US

Simulating the Time Projection Chamber responses at the MPD detector using Generative Adversarial Networks

High energy physics experiments rely heavily on the detailed detector simulation models in many tasks. Running these detailed models typically requires a notable amount of the computing time available to the experiments. In this work, we demonstrate a new approach to speed up the simulation of the Time Projection Chamber tracker of the MPD experiment at the NICA accelerator complex. Our method is based on a Generative Adversarial Network - a deep learning technique allowing for implicit estimation of the population distribution for a given set of objects. This approach lets us learn and then sample from the distribution of raw detector responses, conditioned on the parameters of the charged particle tracks. To evaluate the quality of the proposed model, we integrate a prototype into the MPD software stack and demonstrate that it produces high-quality events similar to the detailed simulator, with a speed-up of at least an order of magnitude. The prototype is trained on the responses from the inner part of the detector and, once expanded to the full detector, should be ready for use in physics tasks.Comment: This is a post-peer-review, pre-copyedit version of an article published in Eur. Phys. J. C. The final authenticated version is available online at: http://dx.doi.org/10.1140/epjc/s10052-021-09366-

Using machine learning to speed up new and upgrade detector studies: a calorimeter case

In this paper, we discuss the way advanced machine learning techniques allow physicists to perform in-depth studies of the realistic operating modes of the detectors during the stage of their design. Proposed approach can be applied to both design concept (CDR) and technical design (TDR) phases of future detectors and existing detectors if upgraded. The machine learning approaches may speed up the verification of the possible detector configurations and will automate the entire detector R\&D, which is often accompanied by a large number of scattered studies. We present the approach of using machine learning for detector R\&D and its optimisation cycle with an emphasis on the project of the electromagnetic calorimeter upgrade for the LHCb detector\cite{lhcls3}. The spatial reconstruction and time of arrival properties for the electromagnetic calorimeter were demonstrated.Comment: Talk presented on CHEP 2019 conferenc

Where is SUSY?

The direct searches for Superymmetry at colliders can be complemented by direct searches for dark matter (DM) in underground experiments, if one assumes the Lightest Supersymmetric Particle (LSP) provides the dark matter of the universe. It will be shown that within the Constrained minimal Supersymmetric Model (CMSSM) the direct searches for DM are complementary to direct LHC searches for SUSY and Higgs particles using analytical formulae. A combined excluded region from LHC, WMAP and XENON100 will be provided, showing that within the CMSSM gluinos below 1 TeV and LSP masses below 160 GeV are excluded (m_{1/2} > 400 GeV) independent of the squark masses.Comment: 16 pages, 10 figure

Event generator tunes obtained from underlying event and multiparton scattering measurements

New sets of parameters (“tunes”) for the underlying-event (UE) modelling of the PYTHIA8, PYTHIA6 and HERWIG++ MonteCarlo event generators are constructed using different parton distribution functions. Combined fits to CMS UE proton–proton (pp) data at √s = 7 TeV and to UE proton–antiproton (pp) data from the CDF experiment at lower √s, are used to study the UE models and constrain their parameters, providing thereby improved predictions for proton–proton collisions at 13 TeV. In addition, it is investigated whether the values of the parameters obtained from fits to UE observables are consistent with the values determined from fitting observables sensitive to double-parton scattering processes. Finally, comparisons are presented of the UE tunes to “minimum bias” (MB) events, multijet, and Drell– Yan (qq → Z/γ*→lepton-antilepton+jets) observables at 7 and 8 TeV, as well as predictions for MB and UE observables at 13 TeV

A transverse current rectification in graphene superlattice

A model for energy spectrum of superlattice on the base of graphene placed on the striped dielectric substrate is proposed. A direct current component which appears in that structure perpendicularly to pulling electric field under the influence of elliptically polarized electromagnetic wave was derived. A transverse current density dependence on pulling field magnitude and on magnitude of component of elliptically polarized wave directed along the axis of a superlattice is analyzed.Comment: 12 pages, 6 figure

Measurement of the differential cross section and charge asymmetry for inclusive pp → W\u3csup\u3e±\u3c/sup\u3e + \u3ci\u3eX\u3c/i\u3e production at √\u3ci\u3es\u3c/i\u3e = 8 TeV

The differential cross section and charge asymmetry for inclusive pp → W± + X → μ±ν + X production at √s = 8 TeV are measured as a function of muon pseudorapidity. The data sample corresponds to an integrated luminosity of 18.8 fb−1 recorded with the CMS detector at the LHC. These results provide important constraints on the parton distribution functions of the proton in the range of the Bjorken scaling variable x from 10−3 to 10−1

Measurement of Charged Pion Production Yields off the NuMI Target

The fixed-target MIPP experiment, Fermilab E907, was designed to measure the production of hadrons from the collisions of hadrons of momenta ranging from 5 to 120 GeV/c on a variety of nuclei. These data will generally improve the simulation of particle detectors and predictions of particle beam fluxes at accelerators. The spectrometer momentum resolution is between 3 and 4%, and particle identification is performed for particles ranging between 0.3 and 80 GeV/c using $dE/dx$, time-of-flight and Cherenkov radiation measurements. MIPP collected $1.42 \times10^6$ events of 120 GeV Main Injector protons striking a target used in the NuMI facility at Fermilab. The data have been analyzed and we present here charged pion yields per proton-on-target determined in bins of longitudinal and transverse momentum between 0.5 and 80 GeV/c, with combined statistical and systematic relative uncertainties between 5 and 10%.Comment: 15 pages, 13 figure