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EPICS for small-scale laboratories with Python soft IOCs
While the Experimental Physics and Industrial Control System (EPICS) is widely used at large laboratories for slow controls and instrumentation, the deployment of a full EPICS installation can be difficult, with a steep learning curve to new users. Taking advantage of the pythonSoftIOC module, we developed an EPICS slow controls implementation for Jefferson Lab's Hall B cryotarget written entirely in Python and based on software IOCs that communicate with instruments over Ethernet. Here, this system ran successfully, interfacing with Jefferson Lab's full EPICS network, and we offer it as an example of the capabilities of pythonSoftIOC to build lightweight, yet robust and flexible instrumentation platforms that would be easily adapted for use at a small-scale laboratory. University groups can use these examples to build complete slow controls systems, from device communication to data archiving and display, using open-source, mature EPICS tools and student-friendly Python as an alternative to expensive and proprietary systems such as LabVIEW.
MPGD Trackers in the ePIC Detector at the EIC
The future Electron-Ion Collider (EIC) at Brookhaven National Laboratory will collide polarized electrons with polarized proton/ions. The electron ? Proton / Ion Collider (ePIC) Experiment is the EIC general-purpose detector aiming at delivering the full physics program of the EIC. This unique environment imposes stringent requirements on the tracking system needed for the measurement of the scattered electron and charged particles produced in the collisions at the EIC. The central tracker of the ePIC detector has silicon layers for tracking and vertexing based on MAPS technology, which are complemented by large Micro-Pattern Gaseous Detector (MPGD) trackers in the barrel and in both the electron and hadron end cap regions. The MPGD layers provide fast timing (10-20 ns) and additional hit points (150 µm) for pattern recognition during track finding. Two MPGD technologies are used in ePIC central trackers: cylindrical Micromegas for the barrel inner tracker and planar thin-gap GEM-µRWELL hybrid detector for the barrel outer tracker and the end cap disks. In this talk, we will introduce these two technologies in the context of ePIC and we will discuss the ongoing R&D efforts to address the requirements. Finally, we will discuss the plans and timeline for the production and testing and commissioning of the MPGD trackers for the installation in the ePIC detector.
EPiC-GAN: Equivariant point cloud generation for particle jets
With the vast data-collecting capabilities of current and future high-energy collider experiments, there is an increasing demand for computationally efficient simulations. Generative machine learning models enable fast event generation, yet so far these approaches are largely constrained to fixed data structures and rigid detector geometries. In this paper, we introduce EPiC-GAN - equivariant point cloud generative adversarial network - which can produce point clouds of variable multiplicity. This flexible framework is based on deep sets and is well suited for simulating sprays of particles called jets. The generator and discriminator utilize multiple EPiC layers with an interpretable global latent vector. Crucially, the EPiC layers do not rely on pairwise information sharing between particles, which leads to a significant speed-up over graph- and transformer-based approaches with more complex relation diagrams. We demonstrate that EPiC-GAN scales well to large particle multiplicities and achieves high generation fidelity on benchmark jet generation tasks.
The ePIC Simulation Campaign Workflow on the Open Science Grid
The ePIC collaboration is realizing the first experiment of the future Electron-Ion Collider (EIC) at the Brookhaven National Laboratory that will allow for a precision study of the nucleons and the nucleus at the scale of sea quarks and gluons through the study of electron-proton/ion collisions. This paper will discuss the current workflow for running centralized simulation campaigns for ePIC on the Open Science Grid (OSG) infrastructure. This involves monthly releases of ePIC software and container deployments to CVMFS, generation of input datasets in HepMC format according to collaboration-defined policy, using Snakemake in CI/CD for validation and benchmarking, and submitting jobs to the OSG condor scheduler for opportunistic running on available resources. File transfers utilize XrootD, and Rucio is used for data management. The workflow is continuously refined to improve daily throughput (currently 50-100k core hours per day) and minimize job failures. Since May 2023, monthly simulation campaigns employing the workflow have cumulatively used over 20 million core hours on the OSG and produced over 350 TB of simulation data. The campaigns incorporate simulations for the broad science program of the EIC and are actively used for the detector and physics studies in preparation of the Technical Design Report (TDR).
Embedded EPICS server for PowerPMAC motion controllers
An embedded server layer of Experimental Physics and Industrial Control System (EPICS) for PowerPMAC motion controllers has been developed and deployed at two undulator beamlines of the National Institute of General Medical Sciences and the National Cancer Institute (GM/CA) Structural Biology Facility at the Advanced Photon Source (APS). This compact, open source solution makes the power and versatility of PowerPMAC motion controls directly accessible to distributed EPICS clients. At GM/CA the system controls about 200 servo and stepper motors — both encoded and unencoded — and multiple digital and analog I/O accessories. The server stack comprises two sublayers: a lower-level driver and database that communicates directly with PowerPMAC, and a facility-specific soft sublayer built on top. The paper describes installing EPICS on PowerPMAC, the implementation of both layers and client examples, including on-the-fly scanning.
EPICS Deployment at Fermilab
Fermilab has traditionally not been an EPICS house, as such expertise in EPICS is limited and scattered. However, PIP-II will be using EPICS for its control system. Furthermore, when PIP-II is operating, it must to interface with the existing, though modernized (see ACORN) legacy control system. We have developed and deployed a software pipeline that addresses these needs and presents to developers a tested and robust software framework, including template IOCs from which new developers can quickly gain experience. In this presentation, we will discuss the motivation for this work, the implementation of a continuous integration/continuous deployment pipeline, testing, template IOCs, and the deployment of user applications. We will also discuss how this is used with the current PIP-II teststand and lessons learned.
Real time heat load calculation software based on EPICS for Fermilab PIP-II CM tests
Fermilab has a project to improve the proton beam energy which is called PIP-II (the 2nd Proton Improvement Plan). There is a superconducting linear accelerator, LINAC, to improve the proton beam power and the LINAC consists of 5 types of cryomodules (CM), 1 HWR CM, 2 SSR1 CM, 4 SSR2 CM, LB650 CM, and HB650 CM. The prototypes of these cryomodules are being tested at Fermilab’s CryoModule Test Facility (CMTF). Heat load measurements are an important part of the prototype CM testing. The CMTF cryogenic control system was developed based on the ACNET (Accelerator Control NETwork) for CM testing for other projects, but the PIP-II cryogenic control system will be implemented using the Experimental Physics and Industrial Control System (EPICS). As part of the prototype CM testing campaign an EPICS based control system has been implemented at CMTF. This EPICS cryogenic control system includes real time heat load calculation software utilizing the Fortran implementation of Hepak. This paper details the real time heat load calculation software developed for the prototype CM testing including the first results from the HB 650 CM.
Centralized System Management of IPMI Enabled Platforms Using EPICS
Intelligent Platform Management Interface (IPMI) is a specification for computer hardware platform management and monitoring. The interface includes features for monitoring hardware sensors like fan speed and device temperature, inventory discovery, event propagation and logging. All IPMI functionality is accessible without the host operating system running. With its wide support across hardware vendors and the backing of a standardization committee, it is a compelling instrumentation for integration into a control system for large experimental physics projects. Integrating IPMI into EPICS provides the benefit of centralized monitoring, archiving and alarming integrated with the facility control system. A new project has been started to enable this capability by creating a native EPICS device driver built on the open-source FreeIPMI library for the remote host connection interface. The driver supports automatic system components discovery for creating EPICS database templates, detailed device information from Field Replaceable Unit interface, sensor monitoring with remote threshold management, geographical PV addressing in PICMG based platforms and PICMG front panel lights readout.
Real time heat load calculation software based on EPICS for Fermilab PIP-II CM tests
Fermilab has a project to improve the proton beam energy which is called PIP-II (the 2nd Proton Improvement Plan). There is a superconducting linear accelerator, LINAC, to improve the proton beam power and the LINAC consists of 5 types of cryomodules (CM), 1 HWR CM, 2 SSR1 CM, 4 SSR2 CM, LB650 CM, and HB650 CM. The prototypes of these cryomodules are being tested at Fermilab’s CryoModule Test Facility (CMTF). Heat load measurements are an important part of the prototype CM testing. The CMTF cryogenic control system was developed based on the ACNET (Accelerator Control NETwork) for CM testing for other projects, but the PIP-II cryogenic control system will be implemented using the Experimental Physics and Industrial Control System (EPICS). As part of the prototype CM testing campaign, an EPICS based control system has been implemented at CMTF. This EPICS cryogenic control system includes real-time heat load calculation software utilizing the Fortran implementation of Hepak. This paper details the real time heat load calculation software developed for the prototype CM testing including the first results from the HB650 CM.
Simulation study for particle identification with the dRICH of the ePIC experiment at the EIC
The dual-radiator Imaging Cherenkov detector (dRICH), employing an aerogel and a gas radiator, is a key component of the forward particle identification system for the ePIC experiment at the Electron-Ion Collider (EIC). This study evaluates the dRICH performance using Geant4 simulations in the context of the global ePIC simulation stack, focusing on the optimization of the aerogel radiator and the impact of sensor noise. We compare two aerogel configurations: the initial design (n = 1.019) and the current default (n = 1.026). The latter, characterized by improved optical properties and a higher refractive index, demonstrates enhanced π/K separation at high momenta, effectively extending the operational overlap with the C 2 F 6 gas radiator (n = 1.00076 at 25 °C, as implemented in the simulation software). Additionally, the study investigates the impact of Silicon Photomultiplier (SiPM) dark noise, showing that a 300 kHz noise rate per 3mm x 3mm channel leads to a moderate reduction (approximately 1.5 GeV/c) in the 3σ separation threshold. These results validate the current dRICH design and quantify the purity levels achievable for both radiators under expected experimental conditions.
A proximity-focusing RICH detector for the ePIC Experiment at the EIC
The Electron-Proton/Ion Collider Experiment (ePIC) will be a large, multi-purpose detector to be installed at the Electron-Ion Collider (EIC) being built at Brookhaven National Laboratory. As robust particle identification (PID) capabilities are essential for fully realizing the EIC science program, ePIC contains several PID subsystems spanning different angular ranges. PID capability in the electron-going endcap is provided by a proximity-focusing Ring Imaging Cherenkov detector (pfRICH) designed to deliver at least 3σ separation between pions and kaons for momenta up to 7 GeV/ c . It will also aid with electron-hadron discrimination at low momentum and assist in the determination of the collision time (t 0 ). This contribution will summarize the design of the pfRICH as well as ongoing fabrication and component testing efforts. geant-based performance simulations will also be discussed.
Reconstruction framework advancements to support streaming for the ePIC detector at the EIC
The ePIC collaboration adopted the JANA2 framework to manage its reconstruction algorithms. This framework has since evolved substantially in response to ePIC’s needs. There have been three main design drivers: integrating cleanly with the Podio-based data models and other layers of the key4hep stack, enabling external configuration of existing components, and supporting timeframe splitting for streaming readout. The result is a unified component model featuring a new declarative interface for specifying inputs, outputs, parameters, services, and resources. This interface enables the user to instantiate, configure, and wire components via an external file. One critical new addition to the component model is a hierarchical decomposition of data boundaries into levels such as Run, Timeframe, PhysicsEvent, and Subevent. Two new component abstractions, Folder and Unfolder, are introduced in order to traverse this hierarchy, e.g. by splitting or merging. The pre-existing components can now operate at different event levels, and JANA2 will automatically construct the corresponding parallel processing topology. This means that a user may write an algorithm once, and configure it at runtime to operate on timeframes or on physics events. Overall, these changes mean that the user requires less knowledge about the framework internals, obtains greater flexibility with configuration, and gains the ability to reuse the existing abstractions in new streaming contexts.
Collaborative Tools for the ePIC Experiment
The Electron Ion Collider (EIC) will be built at Brookhaven National Laboratory in the US within the next decade. The ePIC Collaboration (where ePIC stands for electron-Proton and Ion Collider) is actively working on the Technical Design Report (TDR) for its future detector to be used at this facility.. An effective set of Collaborative Tools and an open, collaborative environment are instrumental for the success of this effort. In this presentation we describe the requirements, technology choices and progress in this area.
EpIC: novel Monte Carlo generator for exclusive processes
Abstract We present the EpIC Monte Carlo event generator for exclusive processes based on generalised parton distributions. EpIC utilises the PARTONS framework, which provides a flexible software architecture and a variety of modelling options for the partonic description of the nucleon. The generator offers a comprehensive set of features, including simulation of a variety of exclusive processes and radiative corrections. It may be used both in the analysis of experimental data, as well as for impact studies, especially for the future electron-ion colliders.
lume-epics
Lume-epics is a dedicated API for serving LUME model variables with EPICS.
LLM integration into EPICS
The utilization of large language models (LLMs) such as ChatGPT has seen a remarkable increase in various fields over the past few years. These models have demonstrated their versatility and capability in understanding and generating human-like text, making them invaluable tools in numerous applications. In this project, we explore the integration of a LLM into the Experimental Physics and Industrial Control System (EPICS). The primary focus of this integration is to employ the LLM for advanced image processing and spatial analysis on images obtained from the beamlines. By leveraging the capabilities of the LLM, we aim to enhance the accuracy and efficiency of image interpretation, enabling more precise data analysis and decision-making within the EPICS framework. This integration not only showcases the potential of LLMs in scientific and industrial applications but also sets the stage for future advancements in automated control systems.
The ePIC dual-radiator RICH detector
The dual radiator Ring Imaging Cherenkov (dRICH) detector is required to provide continuous hadron identification from ≈3 GeV/c up to ≈50 GeV/c, and to supplement electron and positron identification from a few hundred MeV/c up to about 15 GeV/c, in the forward (ion-side) end-cap of the ePIC experiment. Such an extended momentum range imposes the use of two radiators, gas and aerogel. The common imaging system, that ensures compactness and cost-effectiveness, is based on SiPM sensors to work in a high non-uniform magnetic field. During the R&D phase, the dual radiator principle and the single component performance have been validated. A status overview of the project is presented. The design and technological choices are discussed together with the results obtained from laboratory characterization of the component demonstrators and beam tests of the evolving prototypes.