Design of new experiments, as well as upgrade of ongoing ones, is a continuous process in the experimental high energy physics. Frontier R&Ds are used to squeeze the maximum physics performance using ...cutting edge detector technologies. The evaluation of physics performance for particular configuration includes sketching this configuration in Geant, simulating typical signals and backgrounds, applying reasonable reconstruction procedures, combining results in physics performance metrics. Since the best solution is a trade-off between different kinds of limitations, a quick turn over is necessary to evaluate physics performance for different techniques in different configurations. Two typical problems which slow down evaluation of physics performance for particular approaches to calorimeter detector technologies and configurations are: Emulating particular detector properties including raw detector response together with a signal processing chain to adequately simulate a calorimeter response for different signal and background conditions. This includes combining detector properties obtained from the general simulation with properties obtained from different kinds of bench and beam tests of detector and electronics prototypes. Building an adequate reconstruction algorithm for physics reconstruction of the detector response which is reasonably tuned to extract the most of the performance provided by the given detector configuration. Being approached from the first principles, both problems require significant development efforts. Fortunately, both problems may be addressed by using modern machine learning approaches, that allow a combination of available details of the detector techniques into corresponding higher level physics performance in a semi-automated way. In this paper, we discuss the use of advanced machine learning techniques to speed up and improve the precision of the detector development and optimisation cycle, with an emphasis on the experience and practical results obtained by applying this approach to epitomising the electromagnetic calorimeter design as a part of the upgrade project for the LHCb detector at LHC.
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.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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.
Machine Learning in Calorimeter optimization Boldyrev, A.; Derkach, D.; Ratnikov, F. ...
Journal of physics. Conference series,
01/2021, Letnik:
1740, Številka:
1
Journal Article
Recenzirano
Odprti dostop
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.
Advanced detector R&D for both new and ongoing experiments in HEP requires performing computationally intensive and detailed simulations as part of the detector-design optimisation process. We ...propose a versatile approach to this task that is based on machine learning and can substitute the most computationally intensive steps of the process while retaining the GEANT4 accuracy to details. The approach covers entire detector representation from the event generation to the evaluation of the physics performance. The approach allows the use of arbitrary modules arrangement, different signal and background conditions, tunable reconstruction algorithms, and desired physics performance metrics. While combined with properties of detector and electronics prototypes obtained from beam tests, the approach becomes even more versatile. We focus on the Phase II Upgrade of the LHCb Calorimeter under the requirements on operation at high luminosity. We discuss the general design of the approach and particular estimations, including spatial and energy resolution for the future LHCb Calorimeter setup at different pile-up conditions.
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.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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, Letnik:
2012, Številka:
5
Journal Article
Recenzirano
Odprti dostop
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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