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.
Particle identification at the Super Charm-Tau factory experiment will be provided by a Focusing Aerogel Ring Imaging CHerenkov detector (FARICH). Silicon photomultipliers used for the Cherenkov ...light detection generate a lot of noise hits that must be mitigated to reduce both the data flow and negative effects on particle velocity resolution. In this work we present our approach to filtering signal hits, inspired by object detection techniques for computer vision. Several ML-based approaches to the FARICH reconstruction problem in different settings are also discussed.
The optimization of big industrial setups and the accompanying detailed simulations of internal physical processes require complex and time-consuming simulation calculations. We propose a versatile ...approach that can alleviate difficulties in solving this problem and show this using an example of electromagnetic calorimeter optimization at a Large Hadron Collider experiment. Our approach consists of a block representation of the calorimeter optimization process from setting sensitive characteristics of modules and their locations to obtaining a quality metric and applying machine learning methods. The main blocks are signal and background particles generation and their propagation to the calorimeter, the generation of electromagnetic showers of signal and noise in modules with a given technology, the combination of signal and noise with the simulation of different luminosities, the energy and spatial reconstruction of the signal and obtaining the final metric. This approach allows us to evaluate the operational characteristics of the calorimeter and find a more suitable configuration with the necessary quality without extensive use of time-consuming resources.
Abstract
Detailed detector simulation models are vital for the successful operation of modern high-energy physics experiments. In most cases, running detailed models requires a significant amount of ...computing resources. It is desired to have approaches that are less resource-intensive. In this work, we demonstrate the applicability of Generative Adversarial Networks (GAN) as a 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 faster than the detailed simulation in an order of magnitude 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.
Daily operation of a large-scale experiment is a challenging task, particularly from perspectives of routine monitoring of quality for data being taken. We describe an approach that uses Machine ...Learning for the automated system to monitor data quality, which is based on partial use of data qualified manually by detector experts. The system automatically classifies marginal cases: both of good an bad data, and use human expert decision to classify remaining "grey area" cases. This study uses collision data collected by the CMS experiment at LHC in 2010. We demonstrate that proposed workflow is able to automatically process at least 20% of samples without noticeable degradation of the result.
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
(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.
The SHiP experiment is designed to search for very weakly interacting particles beyond the Standard Model which are produced in a 400 GeV/c proton beam dump at the CERN SPS. The critical challenge ...for this experiment is to keep the Standard Model background level negligible. In the beam dump, around 1011 muons will be produced per second. The muon rate in the spectrometer has to be reduced by at least four orders of magnitude to avoid muoninduced backgrounds. It is demonstrated that new improved active muon shield may be used to magnetically deflect the muons out of the acceptance of the spectrometer.
Where is SUSY? Beskidt, C.; de Boer, W.; Kazakov, D. I. ...
The journal of high energy physics,
05/2012, Volume:
2012, Issue:
5
Journal Article
Peer reviewed
Open access
A
bstract
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.
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