Search for: All records

Artificial Intelligence is poised to transform the design of complex, large-scale detectors like ePIC at the future Electron Ion Collider. Featuring a central detector with additional detecting systems in the far forward and far backward regions, the ePIC experiment incorporates numerous design parameters and objectives, including performance, physics reach, and cost, constrained by mechanical and geometric limits.This project aims to develop a scalable, distributed AI-assisted detector design for the EIC (AID(2)E), employing state-of-the-art multiobjective optimization to tackle complex designs. Supported by the ePIC software stack and usingGeant4simulations, our approach benefits from transparent parameterization and advanced AI features.The workflow leverages the PanDA and iDDS systems, used in major experiments such as ATLAS at CERN LHC, the Rubin Observatory, and sPHENIX at RHIC, to manage the compute intensive demands of ePIC detector simulations. Tailored enhancements to the PanDA system focus on usability, scalability, automation, and monitoring.Ultimately, this project aims to establish a robust design capability, apply a distributed AI-assisted workflow to the ePIC detector, and extend its applications to the design of the second detector (Detector-2) in the EIC, as well as to calibration and alignment tasks. Additionally, we are developing advanced data science tools to efficiently navigate the complex, multidimensional trade-offs identified through this optimization process.

The successful realization of the EIC scientific program requires the design and construction of high-performance particle detectors. Recent developments in the field of scientific computing and increased availability of high performance computing resources have made it possible to perform optimization of multi-parameter designs, even when the latter require longer computational times (for example simulations of particle interactions with matter). Procedures involving machine-assisted techniques used to inform the design decision have seen a considerable growth in popularity among the EIC detector community. Having already been realized for tracking and RICH PID detectors, it has a potential application in calorimetry designs. A SciGlass barrel calorimeter originally designed for EIC Detector-1 has a semi-projective geometry that allows for non-trivial performance gains, but also poses special challenges in the way of effective exploration of the design space while satisfying the available space and the cell dimension constraints together with the full detector acceptance requirement. This talk will cover specific approaches taken to perform this detector design optimization.

We propose a new measurement of the ratio of positron-proton to electron-proton elastic scattering at DESY. The purpose is to determine the contributions beyond single-photon exchange, which are essential for the Quantum Electrodynamic (QED) description of the most fundamental process in hadronic physics. By utilizing a 20 cm long liquid hydrogen target in conjunction with the extracted beam from the DESY synchrotron, we can achieve an average luminosity of$$2.12\times 10^{35}$$$2.12\times {10}^{35}$ cm$$^{-2}\cdot $$${}^{-2}·$s$$^{-1}$$${}^{-1}$ ($$\approx 200$$$\approx 200$times the luminosity achieved by OLYMPUS). The proposed two-photon exchange experiment (TPEX) entails a commissioning run at a beam energy of 2 GeV, followed by measurements at 3 GeV, thereby providing new data up to$$Q^2=4.6$$$Q^2=4.6$ (GeV/c)$$^2$$${}^2$(twice the range of current measurements). We present and discuss the proposed experimental setup, run plan, and expectations.

The inaugural AI4EIC Hackathon unfolded as a high-point satellite event during the second AI4EIC Workshop at William & Mary. The workshop itself boasted over two hundred participants in a hybrid format and delved into the myriad applications of Artificial Intelligence and Machine Learning (AI/ML) for the Electron-Ion Collider (EIC). This workshop aimed to catalyze advancements in AI/ML with applications ranging from advancements in accelerator and detector technologies—highlighted by the ongoing work on the ePIC detector and potential development of a second detector for the EIC—to data analytics, reconstruction, and particle identification, as well as the synergies between theoretical and experimental research. Complementing the technical agenda was an enriched educational outreach program that featured tutorials from leading AI/ML experts representing academia, national laboratories, and industry. The hackathon, held on the final day, showcased international participation with ten teams from around the globe. Each team, comprising up to four members, focused on the dual-radiator Ring Imaging Cherenkov (dRICH) detector, an integral part of the particle identification (PID) system in ePIC. The data for the hackathon were generated using the ePIC software suite. While the hackathon presented questions of increasing complexity, its challenges were designed with deliberate simplifications to serve as a preliminary step toward the integration of machine learning and deep learning techniques in PID with the dRICH detector. This article encapsulates the key findings and insights gained from this unique experience.