arXiv subjects

F. Ratnikov

Publications and source records attributed to F. Ratnikov.

10 recordsLinked to original sources

arXiv2022

Generative Adversarial Networks for the fast simulation of the Time Projection Chamber responses at the MPD detector

The detailed detector simulation models are vital for the successful operation of modern high-energy physics experiments. In most cases, such detailed models require a significant amount of computing resources to run. Often this may not be afforded and less resource-intensive approaches are desired. In this work, we demonstrate the applicability of Generative Adversarial Networks (GAN) as the basis for such fast-simulation models for the case of the Time Projection Chamber (TPC) at the MPD detector at the NICA accelerator complex. Our prototype GAN-based model of TPC works more than an order of magnitude faster compared to the detailed simulation without any noticeable drop in the quality of the high-level reconstruction characteristics for the generated data. Approaches with direct and indirect quality metrics optimization are compared.

physics.ins-det↗

arXiv2020

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.

physics.ins-det↗

arXiv2020

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.

physics.ins-det↗

arXiv2017

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.

physics.data-an↗

arXiv2012

Constraints on Supersymmetry from LHC data on SUSY searches and Higgs bosons combined with cosmology and direct dark matter searches

The ATLAS and CMS experiments did not find evidence for Supersymmetry using close to 5/fb of published LHC data at a center-of-mass energy of 7 TeV. We combine these LHC data with data on B_s -> mu mu (LHCb experiment), the relic density (WMAP and other cosmological data) and upper limits on the dark matter scattering cross sections on nuclei (XENON100 data). The excluded regions in the constrained Minimal Supersymmetric SM (CMSSM) lead to gluinos excluded below 1270 GeV and dark matter candidates below 220 GeV for values of the scalar masses (m_0) below 1500 GeV. For large m_0 values the limits of the gluinos and the dark matter candidate are reduced to 970 GeV and 130 GeV, respectively. If a Higgs mass of 125 GeV is imposed in the fit, the preferred SUSY region is above this excluded region, but the size of the preferred region is strongly dependent on the assumed theoretical error.

hep-ph↗

arXiv2012

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.

hep-ph↗

arXiv2011

Constraints from the decay B_s -> mu mu and LHC limits on Supersymmetry

The pure leptonic decay B_s -> mu mu is strongly suppressed in the Standard Model (SM), but can have large enhancements in Supersymmetry, especially at large values of tanbe. New limits on this decay channel from recent LHC data have been used to claim that these limits restrict the SUSY parameter space even more than the direct searches. However, direct searches are hardly dependent on tanbe, while BR(B_s -> mu mu) is proportional to tanbe^6. The relic density constraint requires large tanbe in a large region of the parameter space, which can lead to large values of B_s -> mu mu. Nevertheless, the experimental upper limit on BR(B_s -> mu mu) is not constraining the parameter space of the CMSSM more than the direct searches and the present Higgs limits, if combined with the relic density. We also observe SUSY parameter regions with negative interferences, where the B_s -> mu mu value is up to a factor three below the SM expectation, even at large values of tanbe.

hep-ex↗

arXiv2003

Selection of Tau Leptons with the CDF II Trigger System

In Run II of the CDF experiment, traditional dilepton triggers are enriched by lepton (electrons or muons) plus track, di-tau and tau plus missing transverse energy triggers at Level-3 dedicated to physical processes including tau leptons. We describe these triggers, along with their physics motivations, implementation and cross-sections and report on their initial performance.

hep-ex↗

arXiv2003

Management of Grid Jobs and Information within SAMGrid

We describe some of the key aspects of the SAMGrid system, used by the D0 and CDF experiments at Fermilab. Having sustained success of the data handling part of SAMGrid, we have developed new services for job and information services. Our job management is rooted in \CondorG and uses enhancements that are general applicability for HEP grids. Our information system is based on a uniform framework for configuration management based on XML data representation and processing.

cs.DC↗

arXiv2003

Grid Based Monitoring on the Rutgers CDF Analysis Farm

Run II at the Fermilab Tevatron Collider started in March 2001, and it will continue probing the high energy frontier in particle physics until the start of the LHC at CERN. The CDF collaboration at Fermilab has already stored 260 TB of data and expects to store 1PB of data in the next two years. The HEXCAF computing farm is being set up at Rutgers University to provide the software environment, computing resources, and access to data for physicists participating in the Collaboration. Some job submission, detector data access and storage of the output results are based on the SAM-GRID tools. To extend monitoring for these jobs running on the farm a bridge was developed between the SAM-GRID monitoring tools and the internal farm monitoring. This presentation will describe the configuration and functionality of the HEXCAF farm with the emphasis on the monitoring tools. Finally we summarize our experience of installing and operating a GRID environment on a remote cluster that is being used for real physics studies in the big running experiment.

physics.comp-ph↗