Scalable AI-assisted workflow management for detector design optimization using distributed computing
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97 MATHEMATICS AND COMPUTING↗
Engineering topics
Lin, M.
Publications and source records attributed to Lin, M..
AI-assisted detector design for the EIC (AID(2)E)
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 using G EANT 4 simulations, 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.
97 MATHEMATICS AND COMPUTING↗
Artificial Intelligence for the Electron Ion Collider (AI4EIC)
The Electron-Ion Collider (EIC), a state-of-the-art facility for studying the strong force, is expected to begin commissioning its first experiments in 2028. This is an opportune time for artificial intelligence (AI) to be included from the start at this facility and in all phases that lead up to the experiments. The second annual workshop organized by the AI4EIC working group, which recently took place, centered on exploring all current and prospective application areas of AI for the EIC. This workshop is not only beneficial for the EIC, but also provides valuable insights for the newly established ePIC collaboration at EIC. This paper summarizes the different activities and R&D projects covered across the sessions of the workshop and provides an overview of the goals, approaches and strategies regarding AI/ML in the EIC community, as well as cutting-edge techniques currently studied in other experiments.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Cosmological shocks around galaxy clusters: A coherent investigation with DES, SPT & ACT
We search for signatures of cosmological shocks in gas pressure profiles of galaxy clusters using the cluster catalogs from three surveys: the Dark Energy Survey (DES) Year 3, the South Pole Telescope (SPT) SZ survey, and the Atacama Cosmology Telescope (ACT) data releases 4, 5, and 6, and using thermal Sunyaev-Zeldovich (SZ) maps from SPT and ACT. The combined cluster sample contains around $10^5$ clusters with mass and redshift ranges $10^{13.7} < M_{\rm 200m}/M_\odot < 10^{15.5}$ and $0.1 < z < 2$, and the total sky coverage of the maps is $\approx 15,000 \,\,{\rm deg}^2$. We find a clear pressure deficit at $R/R_{\rm 200m}\approx 1.1$ in SZ profiles around both ACT and SPT clusters, estimated at $6\sigma$ significance, which is qualitatively consistent with a shock-induced thermal non-equilibrium between electrons and ions. The feature is not as clearly determined in profiles around DES clusters. We verify that measurements using SPT or ACT maps are consistent across all scales, including in the deficit feature. The SZ profiles of optically selected and SZ-selected clusters are also consistent for higher mass clusters. Those of less massive, optically selected clusters are suppressed on small scales by factors of 2-5 compared to predictions, and we discuss possible interpretations of this behavior. An oriented stacking of clusters -- where the orientation is inferred from the SZ image, the brightest cluster galaxy, or the surrounding large-scale structure measured using galaxy catalogs -- shows the normalization of the one-halo and two-halo terms vary with orientation. Finally, the location of the pressure deficit feature is statistically consistent with existing estimates of the splashback radius.
79 ASTRONOMY AND ASTROPHYSICS↗
Results of the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC)
Abstract Next-generation surveys like the Legacy Survey of Space and Time (LSST) on the Vera C. Rubin Observatory (Rubin) will generate orders of magnitude more discoveries of transients and variable stars than previous surveys. To prepare for this data deluge, we developed the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC), a competition that aimed to catalyze the development of robust classifiers under LSST-like conditions of a nonrepresentative training set for a large photometric test set of imbalanced classes. Over 1000 teams participated in PLAsTiCC, which was hosted in the Kaggle data science competition platform between 2018 September 28 and 2018 December 17, ultimately identifying three winners in 2019 February. Participants produced classifiers employing a diverse set of machine-learning techniques including hybrid combinations and ensemble averages of a range of approaches, among them boosted decision trees, neural networks, and multilayer perceptrons. The strong performance of the top three classifiers on Type Ia supernovae and kilonovae represent a major improvement over the current state of the art within astronomy. This paper summarizes the most promising methods and evaluates their results in detail, highlighting future directions both for classifier development and simulation needs for a next-generation PLAsTiCC data set.
79 ASTRONOMY AND ASTROPHYSICS↗
Snowmass 2021 Computational Frontier CompF4 Topical Group Report Storage and Processing Resource Access
Computing plays a significant role in all areas of high energy physics. The Snowmass 2021 CompF4 topical group's scope is facilities R&D, where we consider "facilities" as the computing hardware and software infrastructure inside the data centers plus the networking between data centers, irrespective of who owns them, and what policies are applied for using them. In other words, it includes commercial clouds, federally funded High Performance Computing (HPC) systems for all of science, and systems funded explicitly for a given experimental or theoretical program. This topical group report summarizes the findings and recommendations for the storage, processing, networking and associated software service infrastructures for future high energy physics research, based on the discussions organized through the Snowmass 2021 community study.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
A new cyclic carbonate enables high power/ low temperature lithium-ion batteries
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99 GENERAL AND MISCELLANEOUS↗
Near-Term Reliability and Resilience (NTRR) (Final Report)
The Near-Term Reliability and Resiliency (NTRR) was awarded in December 2020 as an inter-lab project to examine the reliability and resilience of the electricity grid and natural gas transportation availability. The project builds on studies conducted by The North American Electric Reliability Corporation (NERC), the U.S. Department of Energy (DOE), and other non-governmental research and operational focused on reliability and resilience analyses challenges. The research was conceived to address near-term scenarios (within 10 years), when many local and regional policy transitions could begin to impact grid reliability, resilience, and supporting infrastructure availability. To integrate the natural gas interdependency, the team began with the generating capacity and demand projections from the 2020 NERC Long-Term Reliability Assessment and the Bulk Electric System (BES) transmission topologies defined in the Western Electricity Coordinating Council (WECC) Anchor Data Set, Eastern Interconnection Reliability Assessment Group Multi-Regional Modeling Working Group (ERAG/MMWG) Data Set, the team calculated baseline regional power sector gas demands from present electricity delivery year through the end of delivery year 2030/31 by applying security constrained economic dispatch. This demand was compiled along with demand projections for regional residential, commercial, and industrial natural gas demands from the most recent Energy Information Administration (EIA) Annual Energy Outlook Reference Case into Deloitte’s MarketBuilder® North American Gas Model. Through the application of these demands, MarketBuilder® was projected the topology of natural gas flows in the natural gas pipeline network across the interconnected North American system along with regional natural gas prices that may be seen by market participants in future years Additionally, contingencies and sensitivities focused on the built models of the Eastern Interconnection (EI) and Western Interconnection (WI). They address challenges from the following with the outcomes being an identification of performance under the extreme conditions and an identification of potential grid weaknesses that should be addressed to mitigate the reduced performance and improve the resilience and reliability of the specific regions as well as the National Grid: • Weather events including extreme heat, extreme cold, high wind, no wind, wind and solar forecasting errors, and wildfires. • Gas availability, factoring in supply disruption (contractual and physical), seasonal availability constraints, and infrastructure limitations; and • Transmission availability and congestion.
03 NATURAL GAS↗
Liquid-phase mega-electron-volt ultrafast electron diffraction
The conversion of light into usable chemical and mechanical energy is pivotal to several biological and chemical processes, many of which occur in solution. To understand the structure–function relationships mediating these processes, a technique with high spatial and temporal resolutions is required. Here, we report on the design and commissioning of a liquid-phase mega-electron-volt (MeV) ultrafast electron diffraction instrument for the study of structural dynamics in solution. Limitations posed by the shallow penetration depth of electrons and the resulting information loss due to multiple scattering and the technical challenge of delivering liquids to vacuum were overcome through the use of MeV electrons and a gas-accelerated thin liquid sheet jet. To demonstrate the capabilities of this instrument, the structure of water and its network were resolved up to the 3rd hydration shell with a spatial resolution of 0.6 Å; preliminary time-resolved experiments demonstrated a temporal resolution of 200 fs.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Preindustrial to Present-Day Changes in Tropospheric Hydroxyl Radical and Methane Lifetime from the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP)
We have analysed time-slice simulations from 17 global models, participating in the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP), to explore changes in present-day (2000) hydroxyl radical (OH) concentration and methane (CH4) lifetime relative to preindustrial times (1850) and to 1980. A comparison of modeled and observation-derived methane and methyl chloroform lifetimes suggests that the present-day global multi-model mean OH concentration is overestimated by 5 to 10% but is within the range of uncertainties. The models consistently simulate higher OH concentrations in the Northern Hemisphere (NH) compared with the Southern Hemisphere (SH) for the present-day (2000; inter-hemispheric ratios of 1.13 to 1.42), in contrast to observation-based approaches which generally indicate higher OH in the SH although uncertainties are large. Evaluation of simulated carbon monoxide (CO) concentrations, the primary sink for OH, against ground-based and satellite observations suggests low biases in the NH that may contribute to the high north–south OH asymmetry in the models. The models vary widely in their regional distribution of present-day OH concentrations (up to 34%). Despite large regional changes, the multi-model global mean (mass-weighted) OH concentration changes little over the past 150 yr, due to concurrent increases in factors that enhance OH (humidity, tropospheric ozone, nitrogen oxide (NOx) emissions, and UV radiation due to decreases in stratospheric ozone), compensated by increases in OH sinks (methane abundance, carbon monoxide and non-methane volatile organic carbon (NMVOC) emissions). The large inter-model diversity in the sign and magnitude of preindustrial to present-day OH changes (ranging from a decrease of 12.7% to an increase of 14.6%) indicate that uncertainty remains in our understanding of the long-term trends in OH and methane lifetime. We show that this diversity is largely explained by the different ratio of the change in global mean tropospheric CO and NOx burdens (Delta CO/Delta NOx, approximately represents changes in OH sinks versus changes in OH sources) in the models, pointing to a need for better constraints on natural precursor emissions and on the chemical mechanisms in the current generation of chemistry-climate models. For the 1980 to 2000 period, we find that climate warming and a slight increase in mean OH (3.5 +/- 2.2%) leads to a 4.3 +/- 1.9% decrease in the methane lifetime. Analysing sensitivity simulations performed by 10 models, we find that preindustrial to present-day climate change decreased the methane lifetime by about four months, representing a negative feedback on the climate system. Further, we analysed attribution experiments performed by a subset of models relative to 2000 conditions with only one precursor at a time set to 1860 levels. We find that global mean OH increased by 46.4 +/- 12.2% in response to preindustrial to present-day anthropogenic NOx emission increases, and decreased by 17.3 +/-2.3%, 7.6 +/- 1.5%, and 3.1 +/- 3.0% due to methane burden, and anthropogenic CO, and NMVOC emissions increases, respectively.
nitrogen oxides↗