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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

“Beam à la carte”: Laser heater shaping for attosecond pulses in a multiplexed x-ray free-electron laser

Electron beam shaping allows the control of the temporal properties of x-ray free-electron laser pulses from femtosecond to attosecond timescales. Here, we demonstrate the use of a laser heater to shape electron bunches and enable the generation of attosecond x-ray pulses. We demonstrate that this method can be applied in a selective way, shaping a targeted subset of bunches while leaving the remaining bunches unchanged. This experiment enables the delivery of shaped x-ray pulses to multiple undulator beamlines, with pulse properties tailored to specialized scientific applications.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

The Sensitivity of Variational Bayesian Neural Network Performance to Hyperparameters

In scientific applications, predictive modeling is often of limited use without accurate uncertainty quantification (UQ) to indicate when a model may be extrapolating or when more data needs to be collected. Bayesian Neural Networks (BNNs) produce predictive uncertainty by propagating uncertainty in neural network (NN) weights and offer the promise of obtaining not only an accurate predictive model but also accurate UQ. However, in practice, obtaining accurate UQ with BNNs is difficult due in part to the approximations used for model training (such as those made in variational inference) and in part to the need to choose a suitable set of hyperparameters; these hyperparameters outnumber those needed for traditional NNs and often have opaque effects on the results. We aim to shed light on the effects of hyperparameter choices for variational BNNs by performing a global sensitivity analysis of variational BNN performance under varying hyperparameter settings. Our results indicate that many of the hyperparameters interact with each other to affect both predictive accuracy and UQ. For improved usage of variational BNNs in real-world applications, we suggest that thorough hyperparameter tuning, including tuning of prior hyperparameters and loss function parameters, is essential for accurate UQ in variational BNNs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Photometric Redshifts and Galaxy Clusters for DES DR2, DESI DR9, and HSC-SSP PDR3 Data

Photometric redshift (photoz) is a fundamental parameter for multi-wavelength photometric surveys, while galaxy clusters are important cosmological probes and ideal objects for exploring the dense environmental impact on galaxy evolution. We extend our previous work on estimating photoz and detecting galaxy clusters to the latest data releases of the Dark Energy Spectroscopic Instrument (DESI) imaging surveys, Dark Energy Survey (DES) and Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) imaging surveys and make corresponding catalogs publicly available for more extensive scientific applications. The photoz catalogs include accurate measurements of photoz and stellar mass for about 320, 293 and 134 million galaxies with r < 23, i < 24 and i < 25 in DESI DR9, DES DR2 and HSC-SSP PDR3 data, respectively. The photoz accuracy is about 0.017, 0.024 and 0.029 and the general redshift coverage is z < 1, z < 1.2 and z < 1.6, respectively for those three surveys. Furthermore, the uncertainty of the logarithmic stellar mass that is inferred from stellar population synthesis fitting is about 0.2 dex. With the above photoz catalogs, galaxy clusters are detected using a fast cluster-finding algorithm. A total of 532,810, 86,963 and 36,566 galaxy clusters with the number of members larger than 10 is discovered for DESI, DES and HSC-SSP, respectively. Their photoz accuracy is at the level of 0.01. The total mass of our clusters is also estimated by using the calibration relations between the optical richness and the mass measurement from X-ray and radio observations. The photoz and cluster catalogs are available at ScienceDB (https://www.doi.org/10.11922/sciencedb.o00069.00003) and PaperData Repository (https://doi.org/10.12149/101089).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

A lightweight, user-configurable detector ASIC digital architecture with on-chip data compression for MHz X-ray coherent diffraction imaging

Today, most X-ray pixel detectors used at light sources transmit raw pixel data off the detector ASIC. With the availability of more advanced ASIC technology nodes for scientific application, more digital functionalities from the computing domains (e.g., compression) can be integrated directly into a detector ASIC to increase data velocity. In this paper, we describe a lightweight, user-configurable detector ASIC digital architecture with on-chip compression which can be implemented in 130 nm technologies in a reasonable area on the ASIC periphery. In addition, we present a design to efficiently handle the variable data from the stream of parallel compressors. The architecture includes user-selectable lossy and lossless compression blocks. The impact of lossy compression algorithms is evaluated on simulated and experimental X-ray ptychography datasets. This architecture is a practical approach to increase pixel detector frame rates towards the continuous 1 MHz regime for not only coherent imaging techniques such as ptychography, but also for other diffraction techniques at X-ray light sources.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Pixel response characterization of the ARCADIA fully depleted MAPS

Monolithic Active Pixel Sensors (MAPS) achieved widespread use in several scientific applications, thanks to their properties, such as low material budget and high granularity. The ARCADIA INFN project developed a Fully-Depleted MAPS (FD-MAPS), using a modified LFoundry 110 nm CIS process. This work presents the first laboratory characterization of the ARCADIA MD3 prototype. Measurements include threshold uniformity studies using both test-pulse injection and a 55 Fe source, as well as threshold and noise calibration achieved thanks to monochromatic X-ray sources. Ultimately, charge-collection efficiency is evaluated using an infrared laser setup.

Particle tracking detectors↗
DOE OSTI2020

Domain-specific compilers for dynamic simulations of quantum materials on quantum computers

Abstract Simulation of the dynamics of quantum materials is emerging as a promising scientific application for noisy intermediate-scale quantum (NISQ) computers. Due to their high gate-error rates and short decoherence times, however, NISQ computers can only produce high-fidelity results for those quantum circuits smaller than some given circuit size. Dynamic simulations, therefore, pose a challenge as current algorithms produce circuits that grow in size with each subsequent time-step of the simulation. This underscores the crucial role of quantum circuit compilers to produce executable quantum circuits of minimal size, thereby maximizing the range of physical phenomena that can be studied within the NISQ fidelity budget. Here, we present two domain-specific (DS) quantum circuit compilers for the Rigetti and IBM quantum computers, specifically designed to compile circuits simulating dynamics under a special class of time-dependent Hamiltonians. The compilers outperform state-of-the-art general-purpose compilers in terms of circuit size reduction by around 25%–30% as well as wall-clock compilation time by around 40% (dependent on system size and simulation time-step). Drawing on heuristic techniques commonly used in artificial intelligence, both compilers scale well with simulation time-step and system size. Code for both compilers is open-source and packaged into a full-stack quantum simulation software with tutorials included for ease of use for future researchers wishing to perform dynamic simulations of quantum materials on quantum computers. As our DS compilers provide significant improvements in both compilation time and simulation fidelity, they provide a building block for accelerating progress toward physical quantum supremacy.

Physics↗
DOE OSTI2020

archi : pipeline for light curve extraction of CHEOPS background stars

ABSTRACT High precision time-series photometry from space is being used for a number of scientific cases. In this context, the recently launched CHaracterizing ExOPlanet Satellite (CHEOPS) (ESA) mission promises to bring 20 ppm precision over an exposure time of 6 h, when targeting nearby bright stars, having in mind the detailed characterization of exoplanetary systems through transit measurements. However, the official CHEOPS (ESA) mission pipeline only provides photometry for the main target (the central star in the field). In order to explore the potential of CHEOPS photometry for all stars in the field, in this paper, we present archi, an additional open-source pipeline module1 to analyse the background stars present in the image. As archi uses the official data reduction pipeline data as input, it is not meant to be used as an independent tool to process raw CHEOPS data but, instead, to be used as an add-on to the official pipeline. We test archi using CHEOPS simulated images, and show that photometry of background stars in CHEOPS images is only slightly degraded (by a factor of 2–3) with respect to the main target. This opens a potential for the use of CHEOPS to produce photometric time-series of several close-by targets at once, as well as to use different stars in the image to calibrate systematic errors. We also show one clear scientific application where the study of the companion light curve can be important for the understanding of the contamination on the main target.

Silva, André M.↗
DOE OSTI2021

Anomaly detection in the Zwicky Transient Facility DR3

We present results from applying the SNAD anomaly detection pipeline to the third public data release of the Zwicky Transient Facility (ZTF DR3). The pipeline is composed of three stages: feature extraction, search of outliers with machine learning algorithms, and anomaly identification with followup by human experts. Our analysis concentrates in three ZTF fields, comprising more than 2.25 million objects. A set of four automatic learning algorithms was used to identify 277 outliers, which were subsequently scrutinized by an expert. From these, 188 (68 per cent) were found to be bogus light curves – including effects from the image subtraction pipeline as well as overlapping between a star and a known asteroid, 66 (24 per cent) were previously reported sources whereas 23 (8 per cent) correspond to non-catalogued objects, with the two latter cases of potential scientific interest (e.g. one spectroscopically confirmed RS Canum Venaticorum star, four supernovae candidates, one red dwarf flare). Moreover, using results from the expert analysis, we were able to identify a simple bi-dimensional relation that can be used to aid filtering potentially bogus light curves in future studies. We provide a complete list of objects with potential scientific application so they can be further scrutinised by the community. These results confirm the importance of combining automatic machine learning algorithms with domain knowledge in the construction of recommendation systems for astronomy. Our code is publicly available.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Preparing sparse solvers for exascale computing

Sparse solvers provide essential functionality for a wide variety of scientific applications. Highly parallel sparse solvers are essential for continuing advances in high-fidelity, multi-physics and multi-scale simulations, especially as we target exascale platforms. This paper describes the challenges, strategies and progress of the US Department of Energy Exascale Computing project towards providing sparse solvers for exascale computing platforms. We address the demands of systems with thousands of high-performance node devices where exposing concurrency, hiding latency and creating alternative algorithms become essential. The efforts described here are works in progress, highlighting current success and upcoming challenges.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Tunable Plasma Linearizer for Compensation of Nonlinear Energy Chirp

The removal of undesired nonlinear time-energy correlation (energy chirp) present in relativistic electron beams is crucial for many accelerator-based scientific applications, such as free-electron lasers, high-energy electron radiography, and MeV ultrafast electron microscopy. Here, we propose and demonstrate that a low-density plasma section can be used as a passive “linearizer” to significantly compensate for the nonlinear energy chirp imprinted on a beam by the curvature of the radio-frequency field in a conventional accelerator. Physically, the passage of the beam through the plasma excites a strong quasi-cosinoidal longitudinal decelerating wakefield that acts to mitigate the beam’s nonlinear energy chirp by superimposing a reverse chirp on the beam. Time-resolved phase-space measurements, combined with high-fidelity three-dimensional particle-in-cell simulations show that the longitudinal phase space of the beam core is almost completely linearized, leading to a fourfold reduction of the beam overall energy spread from 0.148% to 0.036% (FWHM).

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Experimental Characterization of Photoemission from Plasmonic Nanogroove Arrays

Metal photocathodes are an important source of high-brightness electron beams, ubiquitous in the operation of both large-scale accelerators and table-top microscopes. When the surface of a metal is nanoengineered with patterns on the order of the optical wavelength, it can lead to the excitation and confinement of surface-plasmon-polariton waves that drive nonlinear photoemission. In this work, we aim to evaluate gold plasmonic nanogrooves as a concept for producing bright electron beams for accelerators via nonlinear photoemission. Here, we do this by first comparing their optical properties to numerical calculations from first principles to confirm our ability to fabricate these nanoscale structures. Their nonlinear photoemission yield is found by measuring emitted photocurrent as the intensity of their driving laser is varied. Finally, the mean transverse energy of this electron source is found using the solenoid-scan technique. Our data demonstrate the ability of these cathodes to provide a tenfold enhancement in the efficiency of photoemission over flat metals driven with a linear process. We find that these cathodes are robust and capable of reaching sustained average currents over 100 nA at optical intensities larger than 2 GW cm –2 with no degradation of performance. The emittance of the generated beam is found to be highly asymmetric, a fact we can explain with calculations involving the also asymmetric roughness of the patterned surface. These results demonstrate the use of nanoengineered surfaces as enhanced photocathodes, providing a robust air-stable source of high-average-current electron beams hopefully with great potential for industrial and scientific applications.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Linearization of an Electron Beam’s Longitudinal Phase Space Using a Hollow-Channel Plasma

The removal of undesired beam nonlinear energy chirp (time-energy correlation) or linearization of the beam longitudinal phase space (LPS) is crucial for high-brightness linac-based scientific applications, such as x-ray free-electron lasers. In this paper, we propose that a low-density hollow channel plasma can be used as a near-ideal passive linearizer to significantly linearize the beam LPS and, at the same time, preserve the beam emittance. Physically, the passage of the beam through the hollow plasma channel excites a strong quasicosinoidal longitudinal wakefield that acts to mitigate the beam nonlinear energy chirp by superimposing a reverse chirp on the beam. The theoretical analyses and large-scale three-dimensional start-to-end simulations confirm that the beam longitudinal phase space can be almost completely linearized without noticeable beam emittance growth. In conclusion, application of such a near-ideal linearizer may significantly improve the performance of numerous accelerator-based applications.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Deciphering the crystal structure of a novel nanobody against the NEIL1 DNA glycosylase

Nanobodies (VHHs) are single-domain antibodies with three antigenic CDR regions and are used in diverse scientific applications. Here, an ∼14 kDa nanobody (A5) specific for the endonuclease VIII (Nei)-like 1 or NEIL1 DNA glycosylase involved in the first step of the base-excision repair pathway was crystallized and its structure was determined to 2.1 Å resolution. The crystals posed challenges due to potential twinning and anisotropic diffraction. Despite inconclusive twinning indicators, reprocessing in an orthorhombic setting and molecular replacement in space groupP2 1 2 1 2 enabled the successful modeling of 96% of residues in the asymmetric unit, with finalR work andR free values of 0.199 and 0.229, respectively.

Biochemistry & Molecular Biology↗
DOE OSTI2020

DeepFreeze: Towards Scalable Asynchronous Checkpointing of Deep Learning Models

In the age of big data, deep learning has emerged as a powerful tool to extract insight and exploit its value, both in industry and scientific applications. One common pattern emerging in such applications is frequent checkpointing of the state of the learning model during training, needed in a variety of scenarios: analysis of intermediate states to explain features and correlations with training data, exploration strategies involving alternative models that share a common ancestor, knowledge transfer, resilience, etc. However, with increasing size of the learning models and popularity of distributed data-parallel training approaches, simple checkpointing techniques used so far face several limitations: low serialization performance, blocking I/O, stragglers due to the fact that only a single process is involved in checkpointing. This paper proposes a checkpointing technique specifically designed to address the aforementioned limitations, introducing efficient asynchronous techniques to hide the overhead of serialization and I/O, and distribute the load over all participating processes. Experiments with two deep learning applications (CANDLE and ResNet) on a pre-Exascale HPC platform (Theta) shows significant improvement over state-of-art, both in terms of checkpointing duration and runtime overhead.

checkpointing↗
DOE OSTI2025

A Versatile Simulated Data Transport Layer for in Situ Workflows Performance Evaluation

In situ processing does not only allow scientific applications to face the explosion in data volume and velocity but also to address the time constraints of many simulation-analysis workflows by providing scientists with early insights about their applications at runtime. Multiple frameworks implement the concept of a data transport layer (DTL) to enable such in situ workflows. These tools are very versatile, directly or indirectly access the data generated on the same node, another node of the same compute cluster, or a completely distinct node, and allow data publishers and subscribers to run on the same computing resources or not. This versatility puts on researchers the onus of taking key decisions related to resource allocation and how to transport data to ensure the most efficient execution of their in situ workflows. However, domain scientists and workflow practitioners lack the appropriate tools to assess the respective performance of particular design and deployment options. In this paper we introduce a versatile simulated DTL designed to provide researchers with insights on the respective performance of different execution scenarios of in situ workflows. This open-source, standalone library builds on the SimGrid toolkit and can be linked to any SimGrid-based simulator. It facilitates the evaluation of the performance behavior, at scale, of different data transport configurations and the study of the effects of resource allocation strategies. We demonstrate the scalability, versatility, and accuracy of this simulated DTL by reproducing the execution of two synthetic benchmarks and of a real-world in situ workflow composed of an MPI application and a parallel data analysis. Results of simulations run on a single core show that the proposed library can simulate the interactions of tens of thousands of simulated processes deployed on two interconnected commodity clusters in a few seconds, and the execution by a thousand simulated processes of an in situ workflow in less than three minutes.

Suter, Fred [ORNL] (ORCID:0000000319021955)↗
DOE OSTI2020

CoSim: A Simulator for Co-Scheduling of Batch and On-Demand Jobs in HPC Datacenters

The increasing scale and complexity of scientific applications are rapidly transforming the ecosystem of tools, methods, and workflows adopted by the high-performance computing (HPC) community. Big data analytics and deep learning are gaining traction as essential components in this ecosystem in a variety of scenarios, such as, steering of experimental instruments , acceleration of high-fidelity simulations through surrogate computations, and guided ensemble searches. In this context, the batch job model traditionally adopted by the supercomputing in-frastructures needs to be complemented with support to schedule opportunistic on-demand analytics jobs, leading to the problem of efficient preemption of batch jobs with minimum loss of progress. In this paper, we design and implement a simulator, CoSim, that enables on-the-fly analysis of the trade-offs arising between delaying the start of opportunistic on-demand jobs, which leads to longer analytics latency, and loss of progress due to preemption of batch jobs, which is necessary to make room for on-demand jobs. To this end, we propose an algorithm based on dynamic programming with predictable performance and scalability that enables supercomputing infrastructure schedulers to analyze the aforementioned trade-off and take decisions in near real-time. Compared with other state-of-art approaches using traces of the Theta pre-Exascale machine, our approach is capable of finding the optimal solution, while achieving high performance and scalability.

batch job preemption↗
DOE OSTI2023

Online and Scalable Data Compression Pipeline with Guarantees on Quantities of Interest

Data compression is becoming critical for data-intensive scientific applications. Scientists require compression techniques that accurately preserve derived quantities of interest (QoIs). Prior work has shown that a pipeline can be built to guarantee error on the primary data (PD) within user-defined bounds and achieve near-floating point QoI errors. In this paper, we present novel computational approaches for accelerating the pipeline and demonstrate results that enable concurrent execution of compression in parallel with the simulation nodes. This allows compression, including the writing of the required compression data, for the previous time step to be completed while the simulation proceeds with the current time step. Overall, the approach presented in this paper results in a 6–8 times improvement in computational overhead compared to previous work. These results were obtained using data generated by a large-scale fusion code called XGC, which produces hundreds of terabytes of data in a single day.

Banerjee, Tania↗
DOE OSTI2024

A General Framework for Error-controlled Unstructured Scientific Data Compression

Data compression plays a key role in reducing storage and I/O costs. Traditional lossy methods primarily target data on rectilinear grids and cannot leverage the spatial coherence in unstructured mesh data, leading to suboptimal compression ratios. We present a multi-component, error-bounded compression framework designed to enhance the compression of floating-point unstructured mesh data, which is common in scientific applications. Our approach involves interpolating mesh data onto a rectilinear grid and then separately compressing the grid interpolation and the interpolation residuals. This method is general, independent of mesh types and typologies, and can be seamlessly integrated with existing lossy compressors for improved performance. We evaluated our framework across twelve variables from two synthetic datasets and two real-world simulation datasets. The results indicate that the multi-component framework consistently outperforms state-of-the-art lossy compressors on unstructured data, achieving, on average, a 2.3 − 3.5× improvement in compression ratios, with error bounds ranging from 1 × 10 the −6 to 1×10−2. We further investigate impact of hyperparameters, such as grid spacing and error allocation, to deliver optimal compression ratios in diverse datasets.

Gong, Qian↗