Using multiple engines in the Virtual Monte Carlo package

The Virtual Monte Carlo (VMC) package provides a unified interface to different detector simulation transport engines such as GEANT3 and GEANT4. It has been in production use in various experiments but so far the simulation of one event was restricted to the usage of a single chosen engine. We introduce here the possibility to mix multiple engines within the simulation of a single event. Depending on user conditions the simulation is partitioned among the chosen engines, for instance to profit from each of their advantages or specific capabilities. Such conditions can depend on phase space, geometry, particle type or an arbitrary combination. As a main achievement, this development allows for the implementation of fast simulation kernels at the VMC level which can be used stand-alone or together with full simulation engines. This capability is crucial to cope with largely increasing data expected in future LHC runs.

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3D convolutional GAN for fast simulation

EPJ Web of Conferences ◽

10.1051/epjconf/201921402010 ◽

2019 ◽

Vol 214 ◽

pp. 02010 ◽

Cited By ~ 4

Author(s):

Sofia Vallecorsa ◽

Federico Carminati ◽

Gulrukh Khattak

Keyword(s):

Machine Learning ◽

Monte Carlo Simulation ◽

Monte Carlo ◽

Three Dimensional ◽

Machine Learning Techniques ◽

Generative Adversarial Networks ◽

Detector Response ◽

Fast Simulation ◽

Geant4 Monte Carlo Simulation ◽

Detector Simulation

Machine Learning techniques have been used in different applications by the HEP community: in this talk, we discuss the case of detector simulation. The need for simulated events, expected in the future for LHC experiments and their High Luminosity upgrades, is increasing dramatically and requires new fast simulation solutions. We describe an R&D activity aimed at providing a configurable tool capable of training a neural network to reproduce the detector response and speed-up standard Monte Carlo simulation. This represents a generic approach in the sense that such a network could be designed and trained to simulate any kind of detector and, eventually, the whole data processing chain in order to get, directly in one step, the final reconstructed quantities, in just a small fraction of time. We present the first application of three-dimensional convolutional Generative Adversarial Networks to the simulation of high granularity electromagnetic calorimeters. We describe detailed validation studies comparing our results to Geant4 Monte Carlo simulation. Finally we show how this tool could be generalized to describe a whole class of calorimeters, opening the way to a generic machine learning based fast simulation approach.

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New Developments in the VMC Project

EPJ Web of Conferences ◽

10.1051/epjconf/202024502005 ◽

2020 ◽

Vol 245 ◽

pp. 02005

Author(s):

Ivana Hřivnáčová ◽

Benedikt Volkel

Keyword(s):

Monte Carlo ◽

Magnetic Fields ◽

Transition Radiation ◽

Sensitive Detector ◽

Fast Simulation ◽

New Developments ◽

Geometry Model ◽

Detector Simulation ◽

Code Quality

Virtual Monte Carlo (VMC) provides a unified interface to different detector simulation transport engines such as GEANT3 and GEANT4. Since recently all the VMC packages (the VMC core library, also included in ROOT, and the GEANT3 and GEANT4 VMC) are distributed via the VMC Project GitHub organization. In addition to these VMC related packages, the VMC project also includes the Virtual Geometry Model (VGM), which is optionally used in GEANT4 VMC for conversion between GEANT4 and ROOT TGeo geometry models. In this contribution we will present the new organization of the VMC project at GitHub and new developments in the VMC interfaces and the VMC packages. We will cover the introduction of the sensitive detector interface in the VMC core and both GEANT3 and GEANT4 VMC and the new GEANT4-related developments. GEANT4 VMC 3.0 with the integration of multithreading processing was presented at CHEP in 2015. In this presentation we will report on new features included since this version: the improved support for magnetic fields, the integration of fast simulation, Garfield physics, GEANT4 transition radiation and monopole physics. Five new VMC examples demonstrating these new features, and serving also for tests, will be also discussed. Finally we will mention the work towards the code quality and improvements in testing, documentation and automated code formatting.

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Fast simulation of detector effects in Rivet

SciPost Physics ◽

10.21468/scipostphys.8.2.025 ◽

2020 ◽

Vol 8 (2) ◽

Cited By ~ 1

Author(s):

Andy Buckley ◽

Deepak Kar ◽

Karl Nordström

Keyword(s):

Phase Space ◽

Event Analysis ◽

Important Class ◽

Jet Substructure ◽

Fast Simulation ◽

Design And Implementation ◽

Analysis System ◽

Standard Detector ◽

Detector Simulation ◽

Jet Structure

We describe the design and implementation of detector-bias emulation in the Rivet MC event analysis system. Implemented using C++ efficiency and kinematic smearing functors, it allows detector effects to be specified within an analysis routine, customised to the exact phase-space and reconstruction working points of the analysis. A set of standard detector functions for the physics objects of Runs 1 and 2 of the ATLAS and CMS experiments is also provided. Finally, as jet substructure is an important class of physics observable usually considered to require an explicit detector simulation, we demonstrate that a smearing approach, tuned to available substructure data and implemented in Rivet, can accurately reproduce jet-structure biases observed by ATLAS.

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Upgrade of CMS Full Simulation for Run 2

EPJ Web of Conferences ◽

10.1051/epjconf/201921402012 ◽

2019 ◽

Vol 214 ◽

pp. 02012

Author(s):

Vladimir Ivanchenko ◽

Sunanda Banerjee

Keyword(s):

Monte Carlo ◽

Simulation Software ◽

Pixel Detector ◽

Test Beam ◽

Hadronic Calorimeter ◽

Cms Detector ◽

The Status ◽

Full Simulation

We report on the status of the CMS full simulation software for Run 2 operations of the LHC. Initially, Geant4 10.0p02 was used and about 16 billion events were produced for analysis of 2015-2016 data. In 2017, the CMS detector was updated with a new tracking pixel detector, a modified hadronic calorimeter electronics, and extra muon detectors added. Corresponding modifications were introduced in the full simulation and Geant4 10.2p02 was adopted for 2017 simulation productions; that includes an improved Geant4 for multi-threaded mode, which became the default for 2017. For the 2018 Monte Carlo productions, the full simulation has been updated further. The new Geant4 version 10.4 is used, adopted for the production after detailed validations using test-beam and collision data. The results of validations will be described in details. Several aspects of the migration to Geant4 10.4 and modifications in CMSSW simulation software will be also discussed.

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