Results for “Discovery Program” — Page 27

DOE OSTI2016

Mini-apps for high performance data analysis

Scaling-up scientific data analysis and machine learning algorithms for data-driven discovery is a grand challenge that we face today. Despite the growing need for analysis from science domains that are generating `Big Data' from instruments and simulations, building high-performance analytical workflows of data-intensive algorithms have been daunting because: (i) the `Big Data' hardware and software architecture landscape is constantly evolving, (ii) newer architectures impose new programming models, and (iii) data-parallel kernels of analysis algorithms and their performance facets on different architectures are poorly understood. To address these problems, we have: (i) identified scalable data-parallel kernels of popular data analysis algorithms, (ii) implemented `Mini-Apps' of those kernels using different programming models (e.g. Map Reduce, MPI, etc.), (iii) benchmarked and validated the performance of the kernels in diverse architectures. In this paper, we discuss two of those Mini-Apps and show the execution of principal component analysis built as a workflow of the Mini-Apps. We show that Mini-Apps enable scientists to (i) write domain-specific data analysis code that scales on most HPC hardware and (ii) and offers the ability (most times with over a 10x speed-up) to analyze data sizes 100 times the size of what off-the-shelf desktop/workstations of today can handle.

Sukumar, Sreenivas R.↗

DOE OSTI2016

On-demand data analytics in HPC environments at leadership computing facilities: Challenges and experiences

Scaling-up scientific data analysis and machine learning algorithms for data-driven discovery is a grand challenge that we face today. Despite the growing need for analysis from science domains that are generating `Big Data' from instruments and simulations, building high-performance analytical workflows of data-intensive algorithms have been daunting because: (i) the `Big Data' hardware and software architecture landscape is constantly evolving, (ii) newer architectures impose new programming models, and (iii) data-parallel kernels of analysis algorithms and their performance facets on different architectures are poorly understood. To address these problems, we have: (i) identified scalable data-parallel kernels of popular data analysis algorithms, (ii) implemented `Mini-Apps' of those kernels using different programming models (e.g. Map Reduce, MPI, etc.), (iii) benchmarked and validated the performance of the kernels in diverse architectures. In this paper, we discuss two of those Mini-Apps and show the execution of principal component analysis built as a workflow of the Mini-Apps. We show that Mini-Apps enable scientists to (i) write domain-specific data analysis code that scales on most HPC hardware and (ii) and offers the ability (most times with over a 10x speed-up) to analyze data sizes 100 times the size of what off-the-shelf desktop/workstations of today can handle.

Harney, John↗

DOE OSTI2021

SARS-CoV2 Docking Dataset

Description: Small-molecule conformations and docking scores for 1.4 billion molecules docked against 6 protein targets from SARS-CoV2: MPro 5R84, MPro 6WQF, NSP15 6WLC, PLPro 7JIR, Spike 6M0J, and a hand-optimized model of the RNA-dependent RNA polymerase. Docking was carried out using the Autodock-GPU program performing 20 independent structure minimizations per dock - saving 3 results per molecule. Scores reported include the Autodock free energy estimate as well as RF3 and VS-DUD-E v2 machine-learned rescoring models. Protein structure files and maps in the format input to Autodock-GPU are included. Literature Ref: Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19, J. Chem. Inf. Model. 2020, 60(12): 5832â€“5852.

36 MATERIALS SCIENCE↗

DOE OSTI2017

Minor Contribution of Quasars to Ionizing Photon Budget at z ∼ 6: Update on Quasar Luminosity Function at the Faint End with Subaru/Suprime-Cam

We constrain the quasar contribution to the cosmic reionization based on our deep optical survey of z ∼ 6 quasars down to z {sub R} = 24.15 using Subaru/Suprime-Cam in three UKIDSS-DXS fields covering 6.5 deg{sup 2}. In Kashikawa et al. (2015), we select 17 quasar candidates and report our initial discovery of two low-luminosity quasars (M{sub 1450}∼−23) from seven targets, one of which might be a Lyα-emitting galaxy. From an additional optical spectroscopy, none of the four candidates out of the remaining 10 turn out to be genuine quasars. Moreover, the deeper optical photometry provided by the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) shows that, unlike the two already-known quasars, the i − z and z − y colors of the last six candidates are consistent with M- or L-type brown dwarfs. Therefore, the quasar luminosity function (QLF) measurement in the previous paper is confirmed. Compiling the QLF measurements from the literature over a wide magnitude range, including an extremely faint AGN candidate from Parsa et al. (2017), to fit them with a double power law, we find that the best-fit faint-end slope is α=−2.04{sub −0.18}{sup +0.33} (−1.98{sub −0.21}{sup +0.48}) and characteristic magnitude is M{sub 1450}{sup ∗}=−25.8{sub −1.9}{sup +1.1} (−25.7{sub −1.8}{sup +1.0}) in the case of two (one) quasar detection. Our result suggests that, if the QLF is integrated down to M{sub 1450}=−18, quasars produce ∼1%–12% of the ionizing photons required to fully ionize the universe at z ∼ 6 with a 2σ confidence level, assuming that the escape fraction is f{sub esc}=1 and the intergalactic medium clumpy factor is C = 3. Even when the systematic uncertainties are taken into account, our result supports the scenario that quasars are the minor contributors of the reionization.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2019

Nuclear Physics Network Requirements Review Report

The Energy Sciences Network (ESnet) is the Office of Science’s high-performance network user facility, delivering highly reliable data transport capabilities optimized for the requirements of data-intensive science. In essence, ESnet is the circulatory system that enables the U.S. Department of Energy (DOE) science mission by connecting each and every DOE lab and its user facilities. ESnet is funded and stewarded by the Advanced Scientific Computing Research (ASCR) Program, and managed and operated by the Scientific Networking Division at Lawrence Berkeley National Laboratory (LBNL). ESnet is widely regarded as a global leader in the research and education networking community. ESnet connects DOE national laboratories, user facilities, and major experiments so scientists can use remote instruments and computing resources as well as share data with collaborators, transfer large data sets, and access distributed data repositories. While ESnet provides network connectivity, it cannot be characterized as an internet service provider as it is specifically built to provide a range of network services that are tailored to meet the unique requirements of DOE’s data-intensive science. In short, ESnet’s mission is to enable and accelerate scientific discovery by delivering unparalleled network infrastructure, capabilities, and tools. ESnet’s vision is summarized by these three points. 1. Scientific progress will be completely unconstrained by the physical location of instruments, people, computational resources, or data. 2. Collaborations at every scale, in every domain, will have the information and tools they need to achieve maximum benefit from scientific facilities, global networks, and emerging network capabilities. 3. ESnet will foster the partnerships and pioneer the technologies necessary to ensure that these transformations occur. In May 2019, ESnet and the Office of Nuclear Physics (NP) of the DOE Office of Science organized an ESnet requirements review of NP-supported activities. Preparation for this event included identification of key stakeholders to the process: program and facility management, research groups, technology providers, and a number of external observers. These individuals were asked to prepare formal case study documents about their relationship to the NP program to build a complete understanding of the current, near-term, and long-term status, expectations, and processes that will support the science going forward. A series of pre-planning meetings better prepared case study authors for this task and facilitated a smooth transition to the in-person review.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2019

First Cosmology Results using Type Ia Supernovae from the Dark Energy Survey: Constraints on Cosmological Parameters

We present the first cosmological parameter constraints using measurements of type Ia supernovae (SNe Ia) from the Dark Energy Survey Supernova Program (DES-SN). The analysis uses a subsample of 207 spectroscopically confirmed SNe. Ia from the first three years of DES-SN, combined with a low-redshift sample of 122 SNe from the literature. Our "DES-SN3YR" result from these 329 SNe. Ia is based on a series of companion analyses and improvements covering SN. Ia discovery, spectroscopic selection, photometry, calibration, distance bias corrections, and evaluation of systematic uncertainties. For a flat Lambda CDM model we find a matter density Omega(m) = 0.331 +/- 0.038. For a flat wCDM model, and combining our SN. Ia constraints with those from the cosmic microwave background (CMB), we find a dark energy equation of state w = -0.978 +/- 0.059, and Omega(m) = 0.321 +/- 0.018. For a flat w(0)w(a)CDM model, and combining probes from SN. Ia, CMB and baryon acoustic oscillations, we find w(0) = -0.885 +/- 0.114 and w(a) = -0.387 +/- 0.430. These results are in agreement with a cosmological constant and with previous constraints using SNe. Ia (Pantheon, JLA).

dark energy↗

DOE OSTI2022

From Protection and Prevention to Research and Discovery: Eligibility Assessment of the Health Research Laboratory (TA-43) and Historic Documentation for TA-43-0001 - Volume 1

The U.S. Department of Energy National Nuclear Security Administration Los Alamos Field Office (NA-LA) requests the New Mexico State Historic Preservation Officer (SHPO) to concur with the National Register of Historic Places (NRHP)–eligibility determinations detailed in this report for the Health Research Laboratory (HRL) complex in Technical Area 43 (TA-43) at the Los Alamos National Laboratory (LANL). As part of the LANL Footprint Reduction Program’s Decommissioning and Demolition (D&D) process, all facilities of the HRL complex are scheduled for characterization and future demolition.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2017

ASCR/HEP Exascale Requirements Review Report

This draft report summarizes and details the findings, results, and recommendations derived from the ASCR/HEP Exascale Requirements Review meeting held in June, 2015. The main conclusions are as follows. 1) Larger, more capable computing and data facilities are needed to support HEP science goals in all three frontiers: Energy, Intensity, and Cosmic. The expected scale of the demand at the 2025 timescale is at least two orders of magnitude -- and in some cases greater -- than that available currently. 2) The growth rate of data produced by simulations is overwhelming the current ability, of both facilities and researchers, to store and analyze it. Additional resources and new techniques for data analysis are urgently needed. 3) Data rates and volumes from HEP experimental facilities are also straining the ability to store and analyze large and complex data volumes. Appropriately configured leadership-class facilities can play a transformational role in enabling scientific discovery from these datasets. 4) A close integration of HPC simulation and data analysis will aid greatly in interpreting results from HEP experiments. Such an integration will minimize data movement and facilitate interdependent workflows. 5) Long-range planning between HEP and ASCR will be required to meet HEP's research needs. To best use ASCR HPC resources the experimental HEP program needs a) an established long-term plan for access to ASCR computational and data resources, b) an ability to map workflows onto HPC resources, c) the ability for ASCR facilities to accommodate workflows run by collaborations that can have thousands of individual members, d) to transition codes to the next-generation HPC platforms that will be available at ASCR facilities, e) to build up and train a workforce capable of developing and using simulations and analysis to support HEP scientific research on next-generation systems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

ASCR/HEP Exascale Requirements Review Report

This draft report summarizes and details the findings, results, and recommendations derived from the ASCR/HEP Exascale Requirements Review meeting held in June, 2015. The main conclusions are as follows. 1) Larger, more capable computing and data facilities are needed to support HEP science goals in all three frontiers: Energy, Intensity, and Cosmic. The expected scale of the demand at the 2025 timescale is at least two orders of magnitude -- and in some cases greater -- than that available currently. 2) The growth rate of data produced by simulations is overwhelming the current ability, of both facilities and researchers, to store and analyze it. Additional resources and new techniques for data analysis are urgently needed. 3) Data rates and volumes from HEP experimental facilities are also straining the ability to store and analyze large and complex data volumes. Appropriately configured leadership-class facilities can play a transformational role in enabling scientific discovery from these datasets. 4) A close integration of HPC simulation and data analysis will aid greatly in interpreting results from HEP experiments. Such an integration will minimize data movement and facilitate interdependent workflows. 5) Long-range planning between HEP and ASCR will be required to meet HEP's research needs. To best use ASCR HPC resources the experimental HEP program needs a) an established long-term plan for access to ASCR computational and data resources, b) an ability to map workflows onto HPC resources, c) the ability for ASCR facilities to accommodate workflows run by collaborations that can have thousands of individual members, d) to transition codes to the next-generation HPC platforms that will be available at ASCR facilities, e) to build up and train a workforce capable of developing and using simulations and analysis to support HEP scientific research on next-generation systems.

Habib, Salman↗

DOE OSTI2011

Proceedings of RIKEN BNL Research Center Workshop: Brookhaven Summer Program on Nucleon Spin Physics

Understanding the structure of the nucleon is of fundamental importance in sub-atomic physics. Already the experimental studies on the electro-magnetic form factors in the 1950s showed that the nucleon has a nontrivial internal structure, and the deep inelastic scattering experiments in the 1970s revealed the partonic substructure of the nucleon. Modern research focuses in particular on the spin and the gluonic structure of the nucleon. Experiments using deep inelastic scattering or polarized p-p collisions are carried out in the US at the CEBAF and RHIC facilities, respectively, and there are other experimental facilities around the world. More than twenty years ago, the European Muon Collaboration published their first experimental results on the proton spin structure as revealed in polarized deep inelastic lepton-nucleon scattering, and concluded that quarks contribute very little to the proton's spin. With additional experimental and theoretical investigations and progress in the following years, it is now established that, contrary to naive quark model expectations, quarks and anti-quarks carry only about 30% of the total spin of the proton. Twenty years later, the discovery from the polarized hadron collider at RHIC was equally surprising. For the phase space probed by existing RHIC experiments, gluons do not seem to contribute any to the proton's spin. To find out what carries the remaining part of proton's spin is a key focus in current hadronic physics and also a major driving force for the new generation of spin experiments at RHIC and Jefferson Lab and at a future Electron Ion Collider. It is therefore very important and timely to organize a series of annual spin physics meetings to summarize the status of proton spin physics, to focus the effort, and to layout the future perspectives. This summer program on 'Nucleon Spin Physics' held at Brookhaven National Laboratory (BNL) on July 14-27, 2010 [http://www.bnl.gov/spnsp/] is the second one following the Berkeley Summer Program taken place in June of 2009. This program at BNL focused on theory and had many presentations on a wide range of theoretical aspects on nucleon spin, from perturbative-QCD calculations to models, and to the first principle lattice calculation. It also had a good number of summary talks from all major experimental collaborations on spin physics. The program facilitated many discussions between theorists as well as experimentalists. With five transparencies from each presentation at the Summer Program, this proceedings provides a valuable summary on the status and progress, as well as the future prospects of spin physics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2019

The Zwicky Transient Facility: Surveys and Scheduler

Presented here is a novel algorithm for scheduling the observations of time-domain imaging surveys. Our integer linear programming approach optimizes an observing plan for an entire night by assigning targets to temporal blocks, enabling strict control of the number of exposures obtained per field and minimizing filter changes. A subsequent optimization step minimizes slew times between each observation. Our optimization metric self-consistently weights contributions from time-varying airmass, seeing, and sky brightness to maximize the transient discovery rate. We describe the implementation of this algorithm on the surveys of the Zwicky Transient Facility and present its on-sky performance.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Deep drilling in the time domain with DECam: survey characterization

ABSTRACT This paper presents a new optical imaging survey of four deep drilling fields (DDFs), two Galactic and two extragalactic, with the Dark Energy Camera (DECam) on the 4-m Blanco telescope at the Cerro Tololo Inter-American Observatory (CTIO). During the first year of observations in 2021, >4000 images covering 21 deg2 (seven DECam pointings), with ∼40 epochs (nights) per field and 5 to 6 images per night per filter in g, r, i, and/or z have become publicly available (the proprietary period for this program is waived). We describe the real-time difference-image pipeline and how alerts are distributed to brokers via the same distribution system as the Zwicky Transient Facility (ZTF). In this paper, we focus on the two extragalactic deep fields (COSMOS and ELAIS-S1) characterizing the detected sources, and demonstrating that the survey design is effective for probing the discovery space of faint and fast variable and transient sources. We describe and make publicly available 4413 calibrated light curves based on difference-image detection photometry of transients and variables in the extragalactic fields. We also present preliminary scientific analysis regarding the Solar system small bodies, stellar flares and variables, Galactic anomaly detection, fast-rising transients and variables, supernovae, and active Galactic nuclei.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

helixCAM: A platform for programmable cellular assembly in bacteria and human cells

Interactions between cells are indispensable for signaling and creating structure. The ability to direct precise cell-cell interactions would be powerful for engineering tissues, understanding signaling pathways, and directing immune cell targeting. In humans, intercellular interactions are mediated by cell adhesion molecules (CAMs). However, endogenous CAMs are natively expressed by many cells and tend to have cross-reactivity, making them unsuitable for programming specific interactions. Here, we showcase “helixCAM,” a platform for engineering synthetic CAMs by presenting coiled-coil peptides on the cell surface. helixCAMs were able to create specific cell-cell interactions and direct patterned aggregate formation in bacteria and human cells. Based on coiled-coil interaction principles, we built a set of rationally designed helixCAM libraries, which led to the discovery of additional high-performance helixCAM pairs. Finally, we applied this helixCAM toolkit for various multicellular engineering applications, such as spherical layering, adherent cell targeting, and surface patterning.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Mic-hackathon 2024: hackathon on machine learning for electron and scanning probe microscopy

Microscopy is one of the primary sources of information on materials structure and functionality at the nanometer and atomic scales. The data generated through microscopy is often contained in well-structured datasets, enriched with extensive metadata and sample histories, although not always with the same level of detail or storage format. The broad incorporation of data management plans by major funding agencies ensures the preservation and accessibility of this data. However, deriving insights from these rich datasets remains challenging due to the lack of established code ecosystems, standardized benchmarks, and integration strategies. Correspondingly, the efficiency of data usage is very low, and time expenditures at the analysis stage are enormous. In addition to post-acquisition data analysis, the emergence of application programming interfaces by major microscope manufacturers now creates opportunities for real-time ML-based data analytics to enable automated decision making, and particularly ML-agent controlled real-time microscope operation. Despite these opportunities, there is a significant gap in integrating the ML community with the broader microscopy community, limiting the value that these methods bring to physics and materials discovery and materials optimization. Hackathons address these challenges by fostering collaboration between ML experts and microscopy professionals, encouraging the development of innovative solutions that leverage ML for microscopy and preparing the workforce of the future both for microscopy-intensive domains areas, instrument manufacturers, and ML scientists interested in real world applications for fundamental research, materials optimization, and manufacturing. The hackathon generated benchmark datasets and digital twins of microscopes that further contribute to the development of the field and establish data analysis ecosystems. All the codes can be found at GitHub(https://github.com/KalininGroup/Mic-hackathon-2024-codes-publication/tree/1.0.0.1) and Zenodo (https://zenodo.org/records/15579940).

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

FRIB Transition to User Operations, Power Ramp Up, and Upgrade Perspectives

After project completion on scope, on cost, and ahead of schedule, the Facility for Rare Isotope Beams began operations for scientific users in May of 2022. During the first 12 months of user operations, the FRIB accelerator complex delivered 5250 beam hours, including 1528 hours to nine science experiments conducted with primary beams of 36Ar, 48Ca, 70Zn, 82Se, 124Xe, and 198Pt at beam energies >200 MeV/u; 2724 hours for beam developments, studies, and tuning; and 998 hours to industrial users and non-scientific programs using the FRIB Single Event Effect (FSEE) beam line. The ramp-up to a beam power of 400 kW is planned over a six-year period; 1 kW was delivered for initial user runs from in 2022, and 5 kW was delivered as of February 2023. Upgrade plans include doubling the primary-beam energy to 400 MeV/nucleon for enhanced discovery potential (¿FRIB 400¿). This talk reports on FRIB status and progress since SRF2021, emphasizing lessons learned during the transition from beam commissioning to machine operations, challenges and resolutions for the power ramp-up, progress with accelerator improvements, and R&D for the energy upgrade.

43 PARTICLE ACCELERATORS↗

DOE OSTI2026

Advanced Computing Annual Report 2025 [Slides]

In Fiscal Year (FY) 2025, the National Laboratory of the Rockies (NLR) continued to advance computing as a cornerstone of energy innovation, expanding the Kestrel high-performance computing (HPC) system to 56 peak petaflops. This growth strengthened Kestrel's role as a national asset for applied energy research, enabling larger, more complex simulations and accelerating the integration of artificial intelligence (AI) methods across the laboratory's computing portfolio. In FY 2025, AI was a component of most projects running on Kestrel, underscoring its central role in modern energy science and engineering. Kestrel supported a broad and diverse set of 507 modeling and simulation projects, engaging 855 researchers across the U.S. Department of Energy's (DOE's) Office of Critical Minerals and Energy Innovation (CMEI) portfolio and other offices, as well as partners from industry, academia, and utilities. These efforts span critical materials discovery, energy systems modeling, grid modernization, advanced manufacturing, and other areas essential to strengthening U.S. energy security and competitiveness. Together, these collaborations produced 708 technical outputs, including 293 peer-reviewed publications, reflecting both the depth and impact of the science enabled by NLR's computing capabilities. This year's report highlights the growing importance and benefit of AI throughout NLR's research programs and features work by early career researchers who are helping shape the future of computing-enabled energy innovation. Explore these sections and the many project successes captured in the pages that follow.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2013

DNA/RNA Detection Using DNA-Templated Few-Atom Silver Nanoclusters

DNA-templated few-atom silver nanoclusters (DNA/Ag NCs) are a new class of organic/inorganic composite nanomaterials whose fluorescence emission can be tuned throughout the visible and near-IR range by simply programming the template sequences. Compared to organic dyes, DNA/Ag NCs can be brighter and more photostable. Compared to quantum dots, DNA/Ag NCs are smaller, less prone to blinking on long timescales, and do not have a toxic core. The preparation of DNA/Ag NCs is simple and there is no need to remove excess precursors as these precursors are non-fluorescent. Our recent discovery of the fluorogenic and color switching properties of DNA/Ag NCs have led to the invention of new molecular probes, termed NanoCluster Beacons (NCBs), for DNA detection, with the capability to differentiate single-nucleotide polymorphisms by emission colors. NCBs are inexpensive, easy to prepare, and compatible with commercial DNA synthesizers. Many other groups have also explored and taken advantage of the environment sensitivities of DNA/Ag NCs in creating new tools for DNA/RNA detection and single-nucleotide polymorphism identification. In this review, we summarize the recent trends in the use of DNA/Ag NCs for developing DNA/RNA sensors.

47 OTHER INSTRUMENTATION↗

DOE OSTI2018

Adapting to Artificial Intelligence

There is no need to be afraid of the emerging advances in artificial intelligence, says Argonne’s Marius Stan, Program Lead for Intelligent Materials Design. Although artificial intelligence may upend the job market in some ways, he says, it may also create new industries and positions as employees begin to designing and making robots. “It’s the problem and the solution,” Stan says. Meanwhile, artificial intelligence is playing a significant role in defining new research methods that bring us to resolution or discovery more quickly than ever before. Argonne is using or developing many of these technologies as AI extends to new branches of science and technology, sprouting new buds that represent various facets of human function and cognition — from learning to language processing to visual recognition.

97 MATHEMATICS AND COMPUTING↗