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DOE OSTI2023

Mirror dark sector solution of the Hubble tension with time-varying fine-structure constant

Here we explore a model introduced by Cyr-Racine et al. that resolves the Hubble tension by invoking a “mirror world” dark sector with energy density a fixed fraction of the “ordinary” sector of Λ+ cold dark matter. Although it reconciles cosmic microwave background and large-scale structure observations with local measurements of the Hubble constant, H 0 , the model requires a value of the primordial helium mass fraction, Y p = 0.170 ± 0.025, that is discrepant with observations and with the predictions of big bang nucleosynthesis. We consider a variant of the model with standard helium mass fraction but with the value of the electromagnetic fine-structure constant, α, slightly different during photon decoupling from its present value. If α at that epoch is lower than its current value by Δα ≃ -2 x 10 -5 , then we can achieve the same Hubble tension resolution as in the work by Cyr-Racine et al. but with consistent helium abundance. As an example of such time evolution of α, we consider a toy model of an ultralight scalar field, with mass m < 4 x 10 -29 eV, coupled to electromagnetism, which evolves after photon decoupling at redshift z ≃ 10 3 , and that with appropriate coupling appears to be consistent with late-time constraints on α variation and the weak equivalence principle.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Cool dark sector, concordance, and a low σ 8

We investigate a cosmological model in which a fraction of the dark matter is atomic dark matter (ADM). This ADM consists of dark versions of the electron and of the proton, interacting with each other and with dark photons just as their light sector versions do, but interacting with everything else only gravitationally. We find constraints given current cosmic microwave background (CMB) and baryon acoustic oscillation (BAO) data, with and without an H 0 prior, and with and without enforcing a big bang nucleosynthesis consistent helium abundance. We find that, at low dark photon temperature, one can have consistency with BAO and CMB data, with a fraction of dark matter that is ADM (f adm ) as large as ~0.1 . Such a large f adm leads to a suppression of density fluctuations today on scales below about 60 Mpc that may be of relevance to the σ 8 tension. Our work motivates calculation of nonlinear corrections to matter power spectrum predictions in the ADM model. Here, we forecast parameter constraints to come from future ground-based CMB surveys, and find that if ADM is indeed the cause of the σ 8 tension, the influence of the ADM, primarily on CMB lensing, will likely be detectable at high significance.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Majorana neutrinos and dark matter from anomaly cancellation

We discuss a simple theory for neutrino masses where the total lepton number is a local gauge symmetry spontaneously broken below the multi-TeV scale. In this context, the neutrino masses are generated through the canonical seesaw mechanism and a Majorana dark matter candidate is predicted from anomaly cancellation. We discuss in great detail the dark matter annihilation channels and find out the upper bound on the symmetry-breaking scale using the cosmological bounds on the relic density. Since in this context the dark matter candidate has suppressed couplings to the Standard Model quarks, one can satisfy the direct detection bounds even if the dark matter mass is close to the electroweak scale. This theory predicts a light pseudo-Nambu-Goldstone boson (the Majoron) associated to the mechanism of neutrino mass. We discuss briefly the properties of the Majoron and the impact of the big bang nucleosynthesis bounds. Published by the American Physical Society 2024

Astronomy & Astrophysics↗

DOE OSTI2024

Could SBND-PRISM probe lepton flavor violation?

We investigate the possibility of using the Short-Baseline Near Detector (SBND) at Fermilab to constrain lepton flavor violating decays of pions and kaons. We study how to leverage SBND-PRISM, the use of the neutrino beam angular spread to mitigate systematic uncertainties, to enhance this analysis. We show that SBND-PRISM can put stringent limits on the flavor violating branching ratios BR ( π + → μ + ν e ) = 8.9 × 10 − 4 , BR ( K + → μ + ν e ) = 3.2 × 10 − 3 , improving previous constraints by factors 9 and 1.25, respectively. We also estimate the SBND-PRISM sensitivity to lepton number violating decays, BR ( π + → μ + ν ¯ e ) = 2.1 × 10 − 3 and BR ( K + → μ + ν ¯ e ) = 7.4 × 10 − 3 , though not reaching previous Big European Bubble Chamber limits. Last, we identify several ways how the SBND collaboration could improve this analysis. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Neutrino-dark sector equilibration and primordial element abundances

After neutrinos decouple from the photon bath, they can populate a thermal dark sector. If this neutrino cooling occurs at a temperature above ∼ 100 keV , it can impact light element abundances. We calculate the changes to the Y P and D / H abundances in this scenario with a suitably customized version of the public big bang nucleosynthesis code PRIMAT. If electron neutrinos significantly participate in the cooling, we find limits down to temperatures as low as 100 keV. And if the dark sector undergoes a “step” in N eff , there can be additional, ω b -dependent constraints. Published by the American Physical Society 2025

Giovanetti, Cara (ORCID:0000000316113379)↗

DOE OSTI2024

Energy Principles of Scientific Breakeven in an Inertial Fusion Experiment

Fusion “scientific breakeven” (i.e., unity target gain G target , total fusion energy out > laser energy input) has been achieved for the first time (here, G target ∼ 1.5 ). This Letter reports on the physics principles of the design changes that led to the first controlled fusion experiment, using laser indirect drive, on the National Ignition Facility to produce target gain greater than unity and exceeded the previously obtained conditions needed for ignition by the Lawson criterion. Key elements of the success came from reducing “coast time” (the time duration between the end of the laser pulse and implosion peak compression) and maximizing the internal energy delivered to the “hot spot” (the yield producing part of the fusion fuel). The link between coast time and maximally efficient conversion of kinetic energy into internal energy is explained. The energetics consequences of asymmetry and hydrodynamic-induced mixing were part of high-yield big radius implosion design experimental and design strategy. Herein, it is shown how asymmetry and mixing consolidate into one key relationship. It is shown that mixing distills into a kinetic energy cost similar to the impact of implosion asymmetry, shifting the threshold for ignition to higher implosion kinetic energy—a factor not normally included in most statements of the generalized Lawson criterion, but the key needed modifications clearly emerge. Published by the American Physical Society 2024

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Quantum physics of stars

Stars are slowly developing objects; the lifetimes of the different burning phases are determined by the strength of nuclear reactions, which in turn are defined by the quantum structure of the associated nuclei at the threshold and the respective reaction mechanisms. Stars, from the nuclear physics perspective, are cold environments where only a few of the key nuclear reactions have been measured at the actual stellar plasma temperatures. This is also the case for more dynamic astrophysical phenomena from the big bang to stellar explosions. Most of the nuclear reaction rates are therefore based on theoretical extrapolations. A number of discrepancies between these predictions and the associated stellar signatures have been observed, and many may be due to low-energy or near-threshold quantum effects. These effects need to be understood in order to reliably model nuclear reaction processes, not only for stars but also for low-temperature plasma environments such as controlled magnetic or inertial confinement fusion systems, which operate in similar temperature regimes. This review summarizes the various theoretical techniques presently used for deriving reaction rates and discusses possible quantum effects that may impact the reaction cross section near the reaction threshold. These resemble enhanced single-particle and cluster structures near threshold and associated interference effects. New experimental techniques such as deep-underground accelerators or the study of transfer reactions to mimic the quantum-mechanical transition strength, the so-called Trojan horse method, provide ways to directly or indirectly probe the reaction features that determine the reaction rates at stellar energies. Furthermore, this is demonstrated on a number of key nuclear reactions for different nucleosynthesis environments. Finally, current inconsistencies between experimental predictions and observations are discussed.

Models & methods for nuclear reactions↗

DOE OSTI2026

Dark Energy Survey: Implications for cosmological expansion models from the final DES baryon acoustic oscillation and supernova data

The Dark Energy Survey (DES) recently released the final results of its two principal probes of the expansion history: Type Ia supernovae (SNe) and baryonic acoustic oscillations (BAO). In this paper, we explore the cosmological implications of these data in combination with external cosmic microwave background (CMB), big bang nucleosynthesis (BBN), and age-of-the-Universe information. The BAO measurement, which is ∼ 2 σ away from Planck ’s Λ CDM predictions, pushes for low values of Ω m compared to Planck, in contrast to SN which prefers a higher value than Planck. We identify several tensions among datasets in the Λ CDM model that cannot be resolved by including either curvature ( k Λ CDM ) or a constant dark energy equation of state ( w CDM ). By combining BAO + SN + CMB despite these mild tensions, we obtain Ω k = - 5.5 - 4.2 + 4.6 × 10 - 3 in k Λ CDM , and w = - 0.94 8 - 0.027 + 0.028 in w CDM . In w CDM , BAO and SN push again in different directions of parameter space, favoring, respectively, w < - 1 and w > - 1 . If we open the parameter space to w 0 w a CDM [where the equation of state of dark energy varies as w ( a ) = w 0 + ( 1 - a ) w a ], all the datasets are mutually more compatible, and we find concordance in the [ w 0 > - 1 , w a < 0 ] quadrant, with BAO pushing for w a < 0 and SN for [ w 0 > - 1 , w a < 0 ] . For DES BAO and SN in combination with Planck -CMB, we find a 3.2 σ deviation from Λ CDM , with w 0 = - 0.67 3 - 0.097 + 0.098 , w a = - 1.3 7 - 0.50 + 0.51 , a Hubble constant of H 0 = 67.8 1 - 0.86 + 0.96 km s - 1 Mpc - 1 , and an abundance of matter of Ω m = 0.310 9 - 0.0099 + 0.0086 . For the combination of all the background cosmological probes considered (including CMB’s angular acoustic scale θ ⋆ ), we still find a deviation of 2.8 σ from Λ CDM in the w 0 - w a plane. Assuming a minimal neutrino mass, this work provides tentative evidence for non- Λ CDM physics, which is consistent with recent claims in support of evolving dark energy, or a source of unknown systematics.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

RMCProfile7 : reverse Monte Carlo for multiphase systems

This work introduces a completely rewritten version of the programRMCProfile(version 7), big-box, reverse Monte Carlo modelling software for analysis of total scattering data. The major new feature ofRMCProfile7is the ability to refine multiple phases simultaneously, which is relevant for many current research areas such as energy materials, catalysis and engineering. Other new features include improved support for molecular potentials and rigid-body refinements, as well as multiple different data sets. An empirical resolution correction and calculation of the pair distribution function as a back-Fourier transform are now also available.RMCProfile7is freely available for download at https://rmcprofile.ornl.gov/.

Chemistry↗

DOE OSTI2020

Novel Fault Location Method for Power Systems Based on Attention Mechanism and Double Structure GRU Neural Network

Fault location is one of the most essential techniques to maintain the stable operation of power systems. A fast and accurate fault location allows operators to restore power grids faster and avoid economic losses. Conventional methods rely on expert knowledge to extract the necessary features (e.g. DWT, DFT). For large systems, more coupling effects of transmission lines require more complex feature engineering, and incomplete features can easily introduce large errors. To overcome this, a deep learning approach without manual feature extraction is introduced to the fault location model under big data application. Towards this end, in the proposed method, the attention mechanism, the Bi-GRU and a dual structure network are applied to analyze the current data from different perspectives. Complete information for the fault features is extracted for the fault location. Based on a time series model and benefit from the ability to internally acquire the information architecture of faulty line, the established model is adaptive to the power grids with very complex topologies. Simulation results indicate that the proposed double-structure model reduces the maximum error and is less affected by noise. In comparison with different structures and different models, the proposed method shows better performance in IEEE 39-bus system.

42 ENGINEERING↗

DOE OSTI2024

CMOS-Based Single-Cycle in-Memory XOR/XNOR

Big data applications are on the rise, and so is the number of data centers. The ever-increasing massive data pool needs to be periodically backed up in a secure environment. Moreover, a massive amount of securely backed-up data is required for training binary convolutional neural networks for image classification. XOR and XNOR operations are essential for large-scale data copy verification, encryption, and classification algorithms. The disproportionate speed of existing compute and memory units makes the von Neumann architecture inefficient to perform these Boolean operations. Compute-in-memory (CiM) has proved to be an optimum approach for such bulk computations. The existing CiM-based XOR/XNOR techniques either require multiple cycles for computing or add to the complexity of the fabrication process. Here, we propose a CMOS-based hardware topology for single-cycle in-memory XOR/XNOR operations. Our design provides at least 2× improvement in the latency compared with other existing CMOS-compatible solutions. We verify the proposed system through circuit/system-level simulations and evaluate its robustness using a 5000-point Monte Carlo variation analysis. This all-CMOS design paves the way for practical implementation of CiM XOR/XNOR at scaled technology nodes.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

An Empirical Validation of a Constrained Bin Packing Algorithm for a Home Energy Management System

The increasing number of intelligent electrical appliances and home energy management systems provide a big opportunity for demand response services from residential and small commercial buildings to the grid. Simultaneously, direct control of individual devices by utilities can cause communication bottlenecks, as well as coordination and privacy concerns. These challenges can be addressed by combining the constituent devices into a single house battery equivalent for the purposes of demand response, using Minkowski sum and a 2d bin packing problem. However, the well-studied traditional problems have not been tested in a real house, as implementation carries significant challenges of its own. We deploy the packing problem on residential devices in a controllable house. We report the barriers we found, such as charge forecast and scalability of the algorithm, and discuss our solutions. The study serves as an intermediate step between existing theoretical research and possible future steps, such as prototype deployments of systems that provide residential demand response.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Adaptive Anomaly Detection for Dynamic Clinical Event Sequences

Over the past decade, health information technology (IT) has enabled the amount of digital information stored in electronic health records (EHRs) to expand greatly. However, according to some studies, hazards in health IT can lead to changes in clinical decisions, care processes, and care outcomes, as well as other issues. Thus, the effects of health IT hazards on patient safety have been at the forefront of recent patient safety research. Nonetheless, hazard detection in health IT remains a challenge. In this paper, the authors assume that safety-related issues in health IT would exhibit anomalous characteristics in EHR data. Although all hazards will exhibit some anomalous characteristics, not all anomalies can be regarded as hazards. The authors hypothesize that errors in health IT could lead to interruptions in the sequence of clinical actions. To this end, the problem of detecting anomalous sequences in big EHR data is considered. This paper focuses on dynamic event sequences, which are a series of clinical actions in motion. The authors propose an adaptive anomaly detection approach that uses higher-order network representation to detect anomalous sequences. Furthermore, the authors propose a contiguous subsequence anomaly detection approach that identifies abnormal subsequences in the detected anomalous sequences. The proposed approaches are tested by using synthetic and real-world EHR data. The proposed methods outperform existing state of the art anomaly detection techniques. To reduce the computational complexity associated with the operational implementation of the proposed approaches, the Apache Spark environment was leveraged, and a much shorter run time together with improved performance were achieved, especially for data with more than 60,000 sequences.

Niu, Haoran↗

DOE OSTI2021

Advanced Health Information Technology Analytic Framework and Application to Hazard Detection

Health Information Technology (HIT) aims to improve healthcare outcomes by organizing and analyzing various health-related data. With data accumulating at a staggering rate, the importance of real-time analytics has been increasing dramatically, shifting the focus of informatics from batch processing to streaming analytics. HIT is also facing unprecedented challenges in adapting to this new requirement and leveraging advanced IT technologies. This paper introduces a HIT data and compute platform that supports multi-granularity real-time analytics from heterogeneous data sources. The paper first identifies functional requirements and proposes a framework that satisfies the requirements using state-of-the-art big data technologies including Apache Kafka, Spark Structured Streaming Engine, and Delta Lake. To demonstrate its capability to support data analytics in multiple time granularities analytics, a statistical process control-based hazard detection algorithm has been implemented on top of the framework to detect unexpected hazards from order cancellation data of the Department of US Veterans Affairs (VA) in near real-time.

Kumar, Mohit↗

DOE OSTI2022

Latent Learning with pyroMind.2020

This paper is for the Proceedings of the IEEE Big Data 2021 Conference (IEEE BigData 2021) published by IEEE Computer Society.

Romanov, Vyacheslav↗

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 OSTI2021

Parallel I/O Evaluation Techniques and Emerging HPC Workloads: A Perspective

Emerging workloads such as artificial intelligence, big data analytics and complex multi-step workflows alongside future exascale applications are anticipated future HPC workloads, which will result in a more diverse I/O system workload and even less predictable I/O behavior and access patterns. Along with the ever increasing gap between the compute and storage performance capabilities, the in-depth understanding of extreme-scale I/O behavior and the I/O performance modeling and prediction are essential tools of the large-scale I/O evaluation process for addressing the needs of extreme-scale hybrid workloads. In this survey article, we focus on the state-of-the-art of the I/O behavior and performance analysis process for HPC systems in a 5-year time window and identify future research challenges.

Neuwirth, Sarah↗

DOE OSTI2021

Mitigating Catastrophic Forgetting in Deep Learning in a Streaming Setting Using Historical Summary

Recent advancements in scientific equipment and the adaptation of electronics and the Internet of Things (IoT) in our everyday lives resulted in large and complex data production at a high rate. Making meaningful and timely knowledge discovery at a modest cost from this big data is difficult for computing power and storage limitations. Training deep learning models incrementally in a streaming setting can help us with overcoming these limitations. However, in a well-known phenomenon named catastrophic forgetting, incrementally trained models increasingly perform poorly on the past data. To mitigate catastrophic forgetting in training in a streaming setting, we propose constructing a historical summary over time and use the summary with newly arrived data during incremental training. We propose various data summarization techniques such as random sampling, micro clustering, coreset computation, and Auto Encoders to counteract catastrophic forgetting. We built a pipeline for incremental training with a historical summary for training deep learning models for streaming data. We demonstrate the effectiveness of historical summary in mitigating catastrophic forgetting using three case studies involving three different deep learning applications: an Artificial Neural Network (ANN) for classification task on MNIST dataset, a language model (RNN-LM) on the WikiText2 dataset, and a Convolutional Neural Network (CNN), ResNet50 to classify the ImageNet dataset. Through the training of the models, we observe that catastrophic forgetting is evident in ANN and CNN but not in an RNN. For the first task, our method recovers up to 47.9% lost accuracy due to catastrophic forgetting. For the third task, the historical summary recovers classification accuracy by up to 25%. For the second task, though there is not proof of catastrophic forgetting, the training performance (PPL) improves by up to 26% with historical summary.

Dash, Sajal↗