arXiv subjects

Fedor Ratnikov

Publications and source records attributed to Fedor Ratnikov.

30 records · Page 2Linked to original sources

arXiv2020

Using Machine Learning to Speed Up and Improve Calorimeter R&D

Design of new experiments, as well as upgrade of ongoing ones, is a continuous process in the experimental high energy physics. 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 Geant 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.

physics.ins-det↗

arXiv2020

Generative Adversarial Networks for LHCb Fast Simulation

LHCb is one of the major experiments operating at the Large Hadron Collider at CERN. The richness of the physics program and the increasing precision of the measurements in LHCb lead to the need of ever larger simulated samples. This need will increase further when the upgraded LHCb detector will start collecting data in the LHC Run 3. Given the computing resources pledged for the production of Monte Carlo simulated events in the next years, the use of fast simulation techniques will be mandatory to cope with the expected dataset size. In LHCb generative models, which are nowadays widely used for computer vision and image processing are being investigated in order to accelerate the generation of showers in the calorimeter and high-level responses of Cherenkov detector. We demonstrate that this approach provides high-fidelity results along with a significant speed increase and discuss possible implication of these results. We also present an implementation of this algorithm into LHCb simulation software and validation tests.

physics.ins-det↗

arXiv2019

Machine Learning approach to boosting neutral particles identification in the LHCb calorimeter

We present a new approach to identification of boosted neutral particles using Electromagnetic Calorimeter (ECAL) of the LHCb detector. The identification of photons and neutral pions is currently based on the geometric parameters which characterise the expected shape of energy deposition in the calorimeter. This allows to distinguish single photons in the electromagnetic calorimeter from overlapping photons produced from high momentum $π^0$ decays. The novel approach proposed here is based on applying machine learning techniques to primary calorimeter information, that are energies collected in individual cells around the energy cluster. This method allows to improve separation performance of photons and neutral pions and has no significant energy dependence.

physics.ins-det↗

arXiv2019

$(1 + \varepsilon)$-class Classification: an Anomaly Detection Method for Highly Imbalanced or Incomplete Data Sets

Anomaly detection is not an easy problem since distribution of anomalous samples is unknown a priori. We explore a novel method that gives a trade-off possibility between one-class and two-class approaches, and leads to a better performance on anomaly detection problems with small or non-representative anomalous samples. The method is evaluated using several data sets and compared to a set of conventional one-class and two-class approaches.

stat.ML↗

arXiv2019

Generative Models for Fast Calorimeter Simulation.LHCb case

Simulation is one of the key components in high energy physics. Historically it relies on the Monte Carlo methods which require a tremendous amount of computation resources. These methods may have difficulties with the expected High Luminosity Large Hadron Collider (HL LHC) need, so the experiment is in urgent need of new fast simulation techniques. We introduce a new Deep Learning framework based on Generative Adversarial Networks which can be faster than traditional simulation methods by 5 order of magnitude with reasonable simulation accuracy. This approach will allow physicists to produce a big enough amount of simulated data needed by the next HL LHC experiments using limited computing resources.

physics.data-an↗

arXiv2019

Cherenkov Detectors Fast Simulation Using Neural Networks

We propose a way to simulate Cherenkov detector response using a generative adversarial neural network to bypass low-level details. This network is trained to reproduce high level features of the simulated detector events based on input observables of incident particles. This allows the dramatic increase of simulation speed. We demonstrate that this approach provides simulation precision which is consistent with the baseline and discuss possible implications of these results.

hep-ex↗

arXiv2018

HEP Community White Paper on Software trigger and event reconstruction

Realizing the physics programs of the planned and upgraded high-energy physics (HEP) experiments over the next 10 years will require the HEP community to address a number of challenges in the area of software and computing. For this reason, the HEP software community has engaged in a planning process over the past two years, with the objective of identifying and prioritizing the research and development required to enable the next generation of HEP detectors to fulfill their full physics potential. The aim is to produce a Community White Paper which will describe the community strategy and a roadmap for software and computing research and development in HEP for the 2020s. The topics of event reconstruction and software triggers were considered by a joint working group and are summarized together in this document.

physics.comp-ph↗

arXiv2018

HEP Community White Paper on Software trigger and event reconstruction: Executive Summary

physics.comp-ph↗

arXiv2017

Towards automation of data quality system for CERN CMS experiment

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.

physics.data-an↗

arXiv2013

Search for R-parity violating Supersymmetry using the CMS detector

In this talk, the latest results from CMS on R-parity violating Supersymmetry are reviewed. We present results using up to 20/fb of data from the 8 TeV LHC run of 2012. Interpretations of the experimental results in terms of production of squarks, gluinos, charginos, neutralinos, and sleptons within R-parity violating susy models are presented.

hep-ex↗

arXiv2011

Search for Stopped Gluinos in pp collisions at sqrt(s)=7 TeV at CMS

The results of the first search for long-lived gluinos produced in 7 TeV pp collisions at the CERN Large Hadron Collider are presented. The search looks for evidence of long-lived particles that stop in the CMS detector and decay in the quiescent periods between beam crossings. In a dataset with a peak instantaneous luminosity of 10^-32 /cm^2/s, an integrated luminosity of 10/pb, and a search interval corresponding to 62 hours of LHC operation, no significant excess above background was observed. Limits at the 95% confidence level on gluino pair production over 13 orders of magnitude of gluino lifetime are set. For a mass difference (m_gluino-m_neutralino)>100 GeV/c^2, and assuming BR(gluino-> g neutralino)=100%, m_gluino < 370 GeV/c^2 are excluded for lifetimes from 10^-6 s to 1000 s.

hep-ex↗

arXiv2009

Searching for Stopped Gluinos at CMS

We describe plans for a search for long-lived particles which will become stopped by the CMS detector. We will look for the subsequent decay of these particles during time intervals where there are no $pp$ collisions in CMS: during gaps between crossings in the LHC beam structure, and during inter-fill periods between the beam being dumped and re-injection. Such long living particles decays will be recorded with dedicated calorimeter triggers. For models predicting these particles, such as split-susy gluinos, the large cross-section combined with good stopping power of CMS, yields a significant number of triggerable decays. If LHC instantaneous luminosity approaches 10^32 cm^-2 s^-1 in 2009-10, 5-sigma significance can be established in a matter of days, since these decays occur on top of a negligible background. Due to limited size, this paper concentrates on main idea and expected results. More details are available in https://twiki.cern.ch/twiki/bin/view/CMS/PhysicsResults.

hep-ex↗