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 1.5 T 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. Herein 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.
The Electron Ion Collider (EIC) is the next generation of precision QCD facility to be built at Brookhaven National Laboratory in conjunction with Thomas Jefferson National Laboratory. There are a ...significant number of software and computing challenges that need to be overcome at the EIC. During the EIC detector proposal development period, the ECCE consortium began identifying and addressing these challenges in the process of producing a complete detector proposal based upon detailed detector and physics simulations. In this document, the software and computing efforts to produce this proposal are discussed; furthermore, the computing and software model and resources required for the future of ECCE are described.
The recently approved Electron-Ion Collider (EIC) will provide a unique new opportunity for searches of charged lepton flavor violation (CLFV) and other new physics scenarios. In contrast to the e↔μ ...CLFV transition for which very stringent limits exist, there is still a relatively large discovery space for the e→τ CLFV transition, potentially to be explored by the EIC. With the latest detector design of ECCE (EIC Comprehensive Chromodynamics Experiment) and projected integral luminosity of the EIC, we find the τ-leptons created in the DIS process ep→τX are expected to be identified with high efficiency. A first ECCE simulation study, restricted to the 3-prong τ-decay mode and with limited statistics for the Standard Model backgrounds, estimates that the EIC will be able to improve the current exclusion limit on e→τ CLFV by an order of magnitude. The very high vertex resolution of the ECCE detector configuration plays a critical role in τ identification.
Complex physiological and behavioral traits, including neurological and psychiatric disorders, often associate with distributed anatomical variation. This paper introduces a global metric, called ...morphometricity, as a measure of the anatomical signature of different traits. Morphometricity is defined as the proportion of phenotypic variation that can be explained by macroscopic brain morphology. We estimate morphometricity via a linear mixed-effects model that uses an anatomical similarity matrix computed based on measurements derived from structural brain MRI scans. We examined over 3,800 unique MRI scans from nine large-scale studies to estimate the morphometricity of a range of phenotypes, including clinical diagnoses such as Alzheimer’s disease, and nonclinical traits such as measures of cognition. Our results demonstrate that morphometricity can provide novel insights about the neuroanatomical correlates of a diverse set of traits, revealing associations that might not be detectable through traditional statistical techniques.
Here, we performed feasibility studies for various single transverse spin measurements that are related to the Sivers effect, transversity and the tensor charge, and the Collins fragmentation ...function. The processes studied include semi-inclusive deep inelastic scattering (SIDIS) where single hadrons (pions and kaons) were detected in addition to the scattered DIS lepton. The data were obtained in pythia6 and geant4 simulated e+p collisions at 18 GeV on 275 GeV, 18 on 100, 10 on 100, and 5 on 41 that use the ECCE detector configuration. Typical DIS kinematics were selected, most notably Q2>1 GeV2, and cover the x range from 10-4 to 1. The single spin asymmetries were extracted as a function of x and Q2, as well as the semi-inclusive variables z, which corresponds to the momentum fraction the detected hadron carries relative to the struck parton, and PT, which corresponds to the transverse momentum of the detected hadron relative to the virtual photon. They are obtained in azimuthal moments in combinations of the azimuthal angles of the hadron transverse momentum and transverse spin of the nucleon relative to the lepton scattering plane. In order to extract asymmetries, the initially unpolarized MonteCarlo was re-weighted in the true kinematic variables, hadron types and parton flavors based on global fits of fixed target SIDIS experiments and e+e– annihilation data. The expected statistical precision of such measurements is extrapolated to 10 fb–1 and potential systematic uncertainties are approximated given the deviations between true and reconstructed yields. Similar neutron information is obtained by comparing the ECCE e+p pseudo-data with the same from the EIC Yellow Report and scaling the corresponding Yellow Report e+3He pseudo-data uncertainties accordingly. The impact on the knowledge of the Sivers functions, transversity and tensor charges, and the Collins function has then been evaluated in the same phenomenological extractions as in the Yellow Report. Finally, the impact is found to be comparable to that obtained with the parametrized Yellow Report detector and shows that the ECCE detector configuration can fulfill the physics goals on these quantities.
Rice, one of the world's most important food plants, has important syntenic relationships with the other cereal species and is a model plant for the grasses. Here we present a map-based, finished ...quality sequence that covers 95% of the 389 Mb genome, including virtually all of the euchromatin and two complete centromeres. A total of 37,544 non-transposable-element-related protein-coding genes were identified, of which 71% had a putative homologue in Arabidopsis. In a reciprocal analysis, 90% of the Arabidopsis proteins had a putative homologue in the predicted rice proteome. Twenty-nine per cent of the 37,544 predicted genes appear in clustered gene families. The number and classes of transposable elements found in the rice genome are consistent with the expansion of syntenic regions in the maize and sorghum genomes. We find evidence for widespread and recurrent gene transfer from the organelles to the nuclear chromosomes. The map-based sequence has proven useful for the identification of genes underlying agronomic traits. The additional single-nucleotide polymorphisms and simple sequence repeats identified in our study should accelerate improvements in rice production.
ECCE unpolarized TMD measurements Vladimirov, A.; Akiba, Y.; Gayoso, C. Ayerbe ...
Nuclear instruments & methods in physics research. Section A, Accelerators, spectrometers, detectors and associated equipment,
10/2023, Letnik:
1055
Journal Article
Recenzirano
Odprti dostop
We performed feasibility studies for various measurements that are related to unpolarized TMD distribution and fragmentation functions for the ECCE detector proposal. The processes studied include ...semi-inclusive Deep inelastic scattering (SIDIS) where single hadrons (pions and kaons) were detected in addition to the scattered DIS lepton. The single hadron cross sections and multiplicities were extracted as a function of the DIS variables x and Q2, as well as the semi-inclusive variables z, which corresponds to the momentum fraction the detected hadron carries relative to the struck parton and PT, which corresponds to the transverse momentum of the detected hadron relative to the virtual photon. The expected statistical precision of such measurements is extrapolated to accumulated luminosities of 10 fb−1 and potential systematic uncertainties are approximated given the deviations between true and reconstructed yields. The expected uncertainties are then used to obtain the expected impact on the related TMD distribution and fragmentation functions. We find that the ECCE detector proposal fulfills the physics requirements on these channels as detailed in the EIC Yellow Report.
The Electron Ion Collider (EIC) is the next generation of precision QCD facility to be built at Brookhaven National Laboratory in conjunction with Thomas Jefferson National Laboratory. There are a ...significant number of software and computing challenges that need to be overcome at the EIC. During the EIC detector proposal development period, the ECCE consortium began identifying and addressing these challenges in the process of producing a complete detector proposal based upon detailed detector and physics simulations. Here, in this document, the software and computing efforts to produce this proposal are discussed; furthermore, the computing and software model and resources required for the future of ECCE are described.
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 1.5 T 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.
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