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1. Probing axion-like particles at the Electron-Ion Collider

   https://doi.org/10.1007/JHEP02(2024)123  

   Balkin, Reuven; Hen, Or; Li, Wenliang; Liu, Hongkai; Ma, Teng; Soreq, Yotam; Williams, Mike (February 2024, Journal of High Energy Physics)

   A<sc>bstract</sc> The Electron-Ion Collider (EIC), a forthcoming powerful high-luminosity facility, represents an exciting opportunity to explore new physics. In this article, we study the potential of the EIC to probe the coupling between axion-like particles (ALPs) and photons in coherent scattering. The ALPs can be produced via photon fusion and decay back to two photons inside the EIC detector. In a prompt-decay search, we find that the EIC can set the most stringent bound form_a≲ 20 GeV and probe the effective scales Λ ≲ 10⁵GeV. In a displaced-vertex search, which requires adopting an EM calorimeter technology that provides directionality, the EIC could probe ALPs withm_a≲ 1 GeV at effective scales Λ ≲ 10⁷GeV. Combining the two search strategies, the EIC can probe a significant portion of unexplored parameter space in the 0.2 <m_a< 20 GeV mass range. 

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2. AI4EIC Hackathon: PID with the ePIC dRICH

   https://doi.org/10.1051/epjconf/202429508004  

   Fanelli, Cristiano; Giroux, James; McSpadden, Diana; Rajput, Kishansingh; Suresh, Karthik; Cisbani, Evaristo; Deconinck, Wouter; Walter, Eric; Bressan, Andrea; Diefenthaler, Markus; et al (January 2024, EPJ Web of Conferences)

   De_Vita, R; Espinal, X; Laycock, P; Shadura, O (Ed.)

   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. 

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3. AI-assisted detector design for the EIC (AID(2)E)

   https://doi.org/10.1088/1748-0221/19/07/C07001  

   Diefenthaler, M; Fanelli, C; Gerlach, LO; Guan, W; Horn, T; Jentsch, A; Lin, M; Nagai, K; Nayak, H; Pecar, C; et al (July 2024, Journal of Instrumentation)

   Abstract 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. 

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4. Nuclear Physics Mid Term Plan at LNGS

   https://doi.org/10.1140/epjp/s13360-023-04840-2  

   Buompane, R; Cavanna, F; Curceanu, C; D’Onofrio, A; Di_Leva, A; Formicola, A; Gialanella, L; Gustavino, C; Imbriani, G; Junker, M; et al (March 2024, The European Physical Journal Plus)

   Abstract The Istituto Nazionale di Fisica Nucleare—Laboratori Nazionali del Gran Sasso (LNGS) is one of the largest underground physics laboratory, a very peculiar environment suited for experiments in Astroparticle Physics, Nuclear Physics and Fundamental Symmetries. The newly established Bellotti Ion Beam facility represents a major advance in the possibilities of studying nuclear processes in an underground environment. A workshop was organized at LNGS in the framework of the Nuclear Physics Mid Term Plan in Italy, an initiative of the Nuclear Physics Division of the Instituto Nazionale di Fisica Nucleare to discuss the opportunities that will be possible to study in the near future by employing state-of-the-art detection systems. In this report, a detailed discussion of the outcome of the workshop is presented. 

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5. Crack initiation during environment-induced cracking of metals: current status

   https://doi.org/10.1515/corrrev-2024-0034  

   Holroyd, NJ Henry; Burnett, Timothy L; Lewandowski, John J; Scamans, Geoffrey M (July 2024, Corrosion Reviews)

   Abstract Environment-induced cracking (EIC) research spanning the last 80 years for ferrous and non-ferrous metals in aqueous environments at ambient and elevated temperatures has concentrated on crack propagation. Studies clearly reveal EIC involves two differentiable processes, one controlling initiation and the other propagation. Utilization of advanced high-resolution electron microscopy over the last 20 years has enabled more focused studies of crack initiation for stainless steel and nickel-based alloys at elevated temperatures exposed to environments associated with the nuclear industry. More recently, when coupled with advancedin-situexperimental techniques such as time-lapse X-ray computed 3D-tomography, progress has also been made for aluminum alloys suffering EIC at ambient temperatures. Conventional wisdom states that chemical processes are typically rate-controlling during EIC initiation. Additionally, experimental evidence based on primary creep exhaustion ahead of the introduction of an aggressive environment indicates that time-dependent mechanically-driven local microstructural strain accommodation processes (resembling creep-like behavior) often play an important role for many metals, even for temperatures as low as 40 % of their melting points (0.4 T_m). EIC studies reveal initial surface conditions and their associated immediate sub-surface alloy microstructures generated during creation (i.e. disturbed layers) can dictate whether or not EIC initiation occurs under mechanical loading conditions otherwise sufficient to enable initiation and growth. The plethora of quantitative experimental techniques now available to researchers should enable significant advances towards understanding EIC initiation. 

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