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.Comment: talk presented at the LHCP 2013 Conference in Barcelona, Spain, May 13-18th, 201

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.Comment: Talk given at the SUSY'09 conference, Boston, USA, June 5-10, 2009. 4 pages, 2 figure

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.Comment: Proceedings for 24th International Conference on Computing in High Energy and Nuclear Physic

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.Comment: 4 pages, to appear in the proceedings for the XXX Physics in Collision International Symposium, Karlsruhe, Germany, September 1-4, 201

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.Comment: In proceedings of 10th International Workshop on Ring Imaging Cherenkov Detector

Controlling Quality for a Physics-Driven Generative Models and Auxiliary Regression Approach

High energy physics experiments heavily rely on the results of MC simulation of data used to extract physics results. However, the detailed simulation often requires tremendous amount of computation resources. Using Generative Adversarial Networks and other deep learning generative techniques can drastically speed up the computationally heavy simulations like a simulation of the calorimeter response. To be useful, such models are required to satisfy quality metrics which are driven by a specific physics properties of generated objects rather than by a regular ML image-like quality metrics. The auxiliary regression extension to the GAN-based fast simulation demonstrated improvements of the physics quality for generated objects. This approach introduces physics metrics to a Discriminator path of the model thus allows direct discriminating of objects with poorly reproduced properties. In this paper we discuss the auxiliary regression GAN approach to physicsbased fast simulation and concentrate on requirements to the quality of the auxiliary regressor to provide a necessary precision of the generative models built on top of this regressor

What Machine Learning Can Do for Focusing Aerogel Detectors

Particle identification at the Super Charm-Tau factory experiment will be provided by a Focusing Aerogel Ring Imaging CHerenkov detector (FARICH). The specifics of detector location make proper cooling difficult, therefore a significant number of ambient background hits are captured. They must be mitigated to reduce the data flow and improve particle velocity resolution. In this work we present several approaches to filtering signal hits, inspired by machine learning techniques from computer vision.Comment: 5 pages, 4 figures, to be published in 26th International Conference on Computing in High Energy & Nuclear Physics (CHEP2023) proceeding