AI-assisted optimization of the ECCE tracking system at the Electron Ion Collider

arXiv preprint [v2] Fri, 20 May 2022 03:23:44 UTC (2,296 KB) made available under a Creative Commons (CC BY) Attribution Licence, now in press, published by Elsevier: Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, available online 17 November 2022 at: https://doi.org/10.1016/j.nima.2022.167748The Electron-Ion Collider (EIC) is a cutting-edge accelerator facility that will study the nature of the "glue" that binds the building blocks of the visible matter in the universe. The proposed experiment will be realized at Brookhaven National Laboratory in approximately 10 years from now, with detector design and R&D currently ongoing. Notably, EIC is one of the first large-scale facilities to leverage Artificial Intelligence (AI) already starting from the design and R&D phases. The EIC Comprehensive Chromodynamics Experiment (ECCE) is a consortium that proposed a detector design based on a 1.5T solenoid. The EIC detector proposal review concluded that the ECCE design will serve as the reference design for an EIC detector. Herein we describe a comprehensive optimization of the ECCE tracker using AI. The work required a complex parametrization of the simulated detector system. Our approach dealt with an optimization problem in a multidimensional design space driven by multiple objectives that encode the detector performance, while satisfying several mechanical constraints. We describe our strategy and show results obtained for the ECCE tracking system. The AI-assisted design is agnostic to the simulation framework and can be extended to other sub-detectors or to a system of sub-detectors to further optimize the performance of the EIC detector.Office of Nuclear Physics in the Office of Science in the Department of Energy; National Science Foundation, and the Los Alamos National Laboratory Laboratory Directed Research and Development (LDRD) 20200022DR

Estimating clinical morbidity due to ischemic heart disease and congestive heart failure: The future rise of heart failure

Objectives. Many developed countries have seen declining mortality rates for heart disease, together with an alleged decline in incidence and a seemingly paradoxical increase in health care demands. This paper presents a model for forecasting the plausible evolution of heart disease morbidity

QTL mapping for traits associated with stress neuroendocrine reactivity in rats

In the present study we searched for quantitative trait loci (QTLs) that affect neuroendocrine stress responses in a 20-min restraint stress paradigm using Brown–Norway (BN) and Wistar–Kyoto–Hyperactive (WKHA) rats. These strains differed in their hypothalamic–pituitary–adrenal axis (plasma ACTH and corticosterone levels, thymus, and adrenal weights) and in their renin–angiotensin–aldosterone system reactivity (plasma renin activity, aldosterone concentration). We performed a whole-genome scan on a F2 progeny derived from a WKHA × BN intercross, which led to the identification of several QTLs linked to plasma renin activity (Sr6, Sr8, Sr11, and Sr12 on chromosomes RNO2, 3, 19, and 8, respectively), plasma aldosterone concentration (Sr7 and Sr9 on RNO2 and 5, respectively), and thymus weight (Sr10, Sr13, and Srl4 on RNO5, 10, and 16, respectively). The type 1b angiotensin II receptor gene (Agtrlb) maps within the confidence intervals of QTLs on RNO2 linked to plasma renin activity (Sr6, highly significant; LOD = 5.0) and to plasma aldosterone level (Sr7, suggestive; LOD = 2.0). In vitro studies of angiotensin II–induced release of aldosterone by adrenal glomerulosa cells revealed a lower receptor potency (log EC50 = −8.16 ± 0.11 M) and efficiency (Emax = 453.3 ± 25.9 pg/3 × 104 cells/24 h) in BN than in WKHA (log EC50 = −10.66 ± 0.18 M; Emax = 573.1 ± 15.3 pg/3 × 104 cells/24 h). Moreover, differences in Agtr1b mRNA abundance and sequence reinforce the putative role of the Agtr1b gene in the differential plasma renin stress reactivity between the two rat strains.Bastien Llamas, Vincent Contesse, Véronique Guyonnet–Duperat, Hubert Vaudry, Pierre Mormède and Marie-Pierre Moisa

Detector requirements and simulation results for the EIC exclusive, diffractive and tagging physics program using the ECCE detector concept

The version of this article on this repository is an arXiv preprint (arXiv:2208.14575v2 [physics.ins-det] Mon, 6 Mar 2023 19:46:42 UTC (39,794 KB)) submitted to Elsevier and is not the final, peer reviewed, corrected article. It is made available under a Creative Commons (CC BY 4.0) Attribution License.This article presents a collection of simulation studies using the ECCE detector concept in the context of the EIC's exclusive, diffractive, and tagging physics program, which aims to further explore the rich quark–gluon structure of nucleons and nuclei. To successfully execute the program, ECCE proposed to utilize the detector system close to the beamline to ensure exclusivity and tag ion beam/fragments for a particular reaction of interest. Preliminary studies confirm the proposed technology and design satisfy the requirements. The projected physics impact results are based on the projected detector performance from the simulation at 10 or 100 fb−1 of integrated luminosity. Additionally, insights related to a potential second EIC detector are documented, which could serve as a guidepost for future development.Office of Nuclear Physics in the Office of Science in the Department of Energy, the National Science Foundation, USA, the Los Alamos National Laboratory Directed Research and Development (LDRD), USA 20200022DR, the Natural Sciences and Engineering Research Council of Canada (NSERC), and the UK Research and Innovation Science and Technology Facilities Council; This research used resources from the Compute and Data Environment for Science (CADES) at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725; Brookhaven National Lab and the Thomas Jefferson National Accelerator Facility which are operated under contracts DE-SC0012704 and DE-AC05-06OR23177 respectively

AI-assisted Optimization of the ECCE Tracking System at the Electron Ion Collider

The Electron-Ion Collider (EIC) is a cutting-edge accelerator facility that will study the nature of the "glue" that binds the building blocks of the visible matter in the universe. The proposed experiment will be realized at Brookhaven National Laboratory in approximately 10 years from now, with detector design and R&D currently ongoing. Notably, EIC is one of the first large-scale facilities to leverage Artificial Intelligence (AI) already starting from the design and R&D phases. The EIC Comprehensive Chromodynamics Experiment (ECCE) is a consortium that proposed a detector design based on a 1.5T solenoid. The EIC detector proposal review concluded that the ECCE design will serve as the reference design for an EIC detector. Herein we describe a comprehensive optimization of the ECCE tracker using AI. The work required a complex parametrization of the simulated detector system. Our approach dealt with an optimization problem in a multidimensional design space driven by multiple objectives that encode the detector performance, while satisfying several mechanical constraints. We describe our strategy and show results obtained for the ECCE tracking system. The AI-assisted design is agnostic to the simulation framework and can be extended to other sub-detectors or to a system of sub-detectors to further optimize the performance of the EIC detector