The BABAR Collaboration BABAR, the detector for the SLAC PEP-II asymmetric e + e − B Factory operating at the Υ (4S) resonance, was designed to allow comprehensive studies of CP -violation in B-meson decays. Charged particle tracks are measured in a multi-layer silicon vertex tracker surrounded by a cylindrical wire drift chamber. Electromagnetic showers from electrons and photons are detected in an array of CsI crystals located just inside the solenoidal coil of a superconducting magnet. Muons and neutral hadrons are identified by arrays of resistive plate chambers inserted into gaps in the steel flux return of the magnet. Charged hadrons are identified by dE/dx measurements in the tracking detectors and in a ring-imaging Cherenkov detector surrounding the drift chamber. The trigger, data acquisition and data-monitoring systems , VME-and network-based, are controlled by custom-designed online software. Details of the layout and performance of the detector components and their associated electronics and software are presented.
Particle physics has an ambitious and broad experimental programme for the coming decades. This programme requires large investments in detector hardware, either to build new facilities and experiments, or to upgrade existing ones. Similarly, it requires commensurate investment in the R&D of software to acquire, manage, process, and analyse the shear amounts of data to be recorded. In planning for the HL-LHC in particular, it is critical that all of the collaborating stakeholders agree on the software goals and priorities, and that the efforts complement each other. In this spirit, this white paper describes the R&D activities required to prepare for this software upgrade.
Accurate simulation of physical processes is crucial for the success of modern particle physics. However, simulating the development and interaction of particle showers with calorimeter detectors is a time consuming process and drives the computing needs of large experiments at the LHC and future colliders. Recently, generative machine learning models based on deep neural networks have shown promise in speeding up this task by several orders of magnitude. We investigate the use of a new architecture—the Bounded Information Bottleneck Autoencoder—for modelling electromagnetic showers in the central region of the Silicon-Tungsten calorimeter of the proposed International Large Detector. Combined with a novel second post-processing network, this approach achieves an accurate simulation of differential distributions including for the first time the shape of the minimum-ionizing-particle peak compared to a full Geant4 simulation for a high-granularity calorimeter with 27k simulated channels. The results are validated by comparing to established architectures. Our results further strengthen the case of using generative networks for fast simulation and demonstrate that physically relevant differential distributions can be described with high accuracy.
Given the increasing data collection capabilities and limited computing resources of future collider experiments, interest in using generative neural networks for the fast simulation of collider events is growing. In our previous study, the Bounded Information Bottleneck Autoencoder (BIB-AE) architecture for generating photon showers in a high-granularity calorimeter showed a high accuracy modeling of various global differential shower distributions. In this work, we investigate how the BIB-AE encodes this physics information in its latent space. Our understanding of this encoding allows us to propose methods to optimize the generation performance further, for example, by altering latent space sampling or by suggesting specific changes to hyperparameters. In particular, we improve the modeling of the shower shape along the particle incident axis.
PODIO is a C++ toolkit for the creation of event data models (EDMs) with a fast and efficient I/O layer. It employs plain-old-data (POD) data structures wherever possible, while avoiding deep object-hierarchies and virtual inheritance. A lightweight layer of handle classes provides the necessary highlevel interface for the physicist. PODIO creates all EDM code from simple instructive YAML files, describing the actual EDM entities. Since its original development PODIO has been very actively used for Future Circular Collider (FCC) studies. In its original version, the underlying I/O was entirely based on the automatic streaming code generated with ROOT dictionaries. Recently two additional I/O implementations have been added. One is based on HDF5 and the other uses SIO, a simple binary I/O library provided by LCIO. We briefly introduce the main features of PODIO and then report on recent developments with a focus on performance comparisons between the available I/O implementations. We conclude with presenting recent activities on porting the well-established LCIO EDM to PODIO and the recent EDM4hep project.
The EDM4hep project aims to design the common event data model for the Key4hep project and is generated via the podio toolkit. We present the first version of EDM4hep and discuss some of its use cases in the Key4hep project. Additionally, we discuss recent developments in podio, like the updates of the automatic code generation and also the addition of a second I/O backend based on SIO. We compare the available backends using benchmarks based on physics use cases, before we conclude with a discussion of currently ongoing work and future developments.
Detector description is an essential component in simulation, reconstruction and analysis of data resulting from particle collisions in high energy physics experiments and for the detector development studies for future experiments. Current detector description implementations of running experiments are mostly specific implementations. DD4hep  is an open source toolkit created in 2012 to serve as a generic detector description solution. The main motivation behind DD4hep is to provide the community with an integrated solution for all these stages and address detector description in a broad sense, including the geometry and the materials used in the device, and additional parameters describing e.g. the detection techniques, constants required for alignment and calibration, description of the readout structures and conditions data. In these proceedings, we will give an overview of the project and discuss recent developments in DD4hep as well as showcase adaptions of the framework by LHC and upcoming accelerator projects together with the road map of future developments.
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