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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Third-order photon correlations extract single-nanocrystal multiexciton properties in solution

Colloidal semiconductor nanocrystals are considered promising materials for high-flux optical applications, including lasing, light-emitting diodes, biological imaging, and quantum optics. In high-flux applications, multiexcitons can significantly contribute to emission, influencing its brightness, spectral purity, and kinetics. As a result, understanding and controlling multiexciton emission in colloidal nanocrystal materials is of the utmost importance. In the past, single-nanocrystal photon correlation methods have been applied to understand biexciton and triexciton efficiencies, lifetimes, and spectra. While powerful, such methods suffer from user selection bias and require stable emission from single nanocrystals. To compensate for this shortcoming, second-order correlation methods were developed to extract sample-averaged biexciton properties from a solution of nanocrystals. Until now, however, the analogous third-order solution photon correlation methods remained unexplored. In this work, we present a pair of third-order photon correlation techniques to obtain the sample-averaged single-nanocrystal triexciton quantum yield and lifetime in a solution-phase experiment. These techniques derive from the relationship between the Poisson probability of nanocrystal photon absorption and the intrinsic probability of nanocrystal photon emission. We validate the theoretical background of these techniques by creating a numerical model to simulate the diffusion and emission of many nanocrystals in solution. Our simulations confirm that the average triexciton quantum yield and triexciton lifetime can be extracted from a solution of nanocrystals. These techniques will enable researchers to gain a better understanding of the fundamental multiexciton properties of colloidal nanocrystals.

Horowitz, Jonah R. [Massachusetts Institute of Tec↗
DOE OSTI2022

A deep learning approach to real-time HIV outbreak detection using genetic data

Pathogen genomic sequence data are increasingly made available for epidemiological monitoring. A main interest is to identify and assess the potential of infectious disease outbreaks. While popular methods to analyze sequence data often involve phylogenetic tree inference, they are vulnerable to errors from recombination and impose a high computational cost, making it difficult to obtain real-time results when the number of sequences is in or above the thousands. Here, we propose an alternative strategy to outbreak detection using genomic data based on deep learning methods developed for image classification. The key idea is to use a pairwise genetic distance matrix calculated from viral sequences as an image, and develop convolutional neutral network (CNN) models to classify areas of the images that show signatures of active outbreak, leading to identification of subsets of sequences taken from an active outbreak. We showed that our method is efficient in finding HIV-1 outbreaks with R0 ≥ 2.5, and overall a specificity exceeding 98% and sensitivity better than 92%. We validated our approach using data from HIV-1 CRF01 in Europe, containing both endemic sequences and a well-known dual outbreak in intravenous drug users. Our model accurately identified known outbreak sequences in the background of slower spreading HIV. Importantly, we detected both outbreaks early on, before they were over, implying that had this method been applied in real-time as data became available, one would have been able to intervene and possibly prevent the extent of these outbreaks. This approach is scalable to processing hundreds of thousands of sequences, making it useful for current and future real-time epidemiological investigations, including public health monitoring using large databases and especially for rapid outbreak identification.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

3D Multiresolution Velocity Model Fusion with Probability Graphical Models

ABSTRACT The variability in spatial resolution of seismic velocity models obtained via tomographic methodologies is attributed to many factors, including inversion strategies, ray-path coverage, and data integrity. Integration of such models, with distinct resolutions, is crucial during the refinement of community models, thereby enhancing the precision of ground-motion simulations. Toward this goal, we introduce the probability graphical model (PGM), combining velocity models with heterogeneous resolutions and nonuniform data point distributions. The PGM integrates data relations across varying resolution subdomains, enhancing detail within low-resolution (LR) domains by utilizing information and prior knowledge from high-resolution (HR) subdomains through a maximum posterior problem. Assessment of efficacy, utilizing both 2D and 3D velocity models—consisting of synthetic checkerboard models and a fault-zone model from Ridgecrest, California—demonstrates noteworthy improvements in accuracy, compared to state-of-the-art fusion techniques. Specifically, we find reductions of 30% and 44% in computed travel-time residuals for 2D and 3D models, respectively, as compared to conventional smoothing techniques. Unlike conventional methods, the PGM’s adaptive weight selection facilitates preserving and learning details from complex, nonuniform HR models and applies the enhancements to the LR background domain.

Geochemistry & Geophysics↗
DOE OSTI2020

Water Network Tool for Resilience (WNTR). User Manual, Version 0.2.3

The Water Network Tool for Resilience (WNTR, pronounced winter) is a Python package designed to simulate and analyze resilience of water distribution networks. Here, a network refers to the collection of pipes, pumps, valves, junctions, tanks, and reservoirs that make up a water distribution system. WNTR has an application programming interface (API) that is flexible and allows for changes to the network structure and operations, along with simulation of disruptive incidents and recovery actions. WNTR is based upon EPANET, which is a tool to simulate the movement and fate of drinking water constituents within distribution systems. Users are encouraged to be familiar with the use of EPANET and/or should have background knowledge in hydraulics and pressurized pipe network modeling before using WNTR. EPANET has a graphical user interface that might be a useful tool to facilitate the visualization of the network and the associated analysis results. Information on EPANET can be found at https://www.epa.gov/water-research/epanet. WNTR is compatible with EPANET 2.00.12 [Ross00]. In addition, users should have experience using Python, including the installation of additional Python packages. General information on Python can be found at https://www.python.org/.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Minimum Resolution Requirements for Gamma Identification Algorithms

Each year there are millions of dollars spent on the research and production of high-resolution detectors. This research indicates that the pursuit of higher resolution detectors is not always necessary. The terminal resolution of a NaI detector, or highest detector resolution, at which identification algorithms fail to identify highly enriched uranium (HEU) was evaluated using GADRAS, Genie, and GammaVision. GADRAS employs a template matching algorithm, while Genie and GammaVision utilize a mathematical approach for peak search and identification. The NaI spectra utilized for evaluation were generated using the GADRAS Inject tab and source modeling functions. Each spectrum included terrestrial and cosmic background from Dallas, TX. The resolutions for each spectrum were increased from a default 8.92% to a point where each algorithm would fail to identify 235 U from a HEU source. Six different source configurations were used in this research: bare HEU, 50% shielded HEU, 90% shielded HEU, bare HEU with an interference source of 99 mTc, bare HEU with 99 mTc both shielded 50%, and bare HEU with 99 mTc both shielded 90%. T

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Detection of Marine Borne Radioactive Sources Report

This report describes a preliminary analysis on the utility of modern capabilities to localize radiological sources in marine environment. This study analyzed expected background sources and identified possible approaches based on modeling and simulation analyses. The effort culminated in the design and fabrication of validation experiments which provided favorable supporting data. The conclusions of this effort are not conclusive as to the viability of the approach nor is the underlining hypothesis discounted. Continuation of the experimental approach is recommended with a focus on open-water experimentation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Methane Integrated Monitoring and Measurement System Design

Methane (CH 4 ), an abundant greenhouse gas, is the second largest contributor to global warming after carbon dioxide (CO 2 ). In comparison to CO 2 , CH 4 has a larger warming effect over a much shorter lifetime. While technologies to radically reduce global carbon dioxide emissions are materializing, rapid reductions in methane emissions are needed to limit near-term warming. Methane is primarily emitted as a byproduct from agricultural activities and energy extraction/utilization and is currently monitored via bottom-up (i.e., activity level) or top-down (via airborne or satellite retrievals) approaches. However, significant methane leaks remain undetected, and emission rates are challenging to characterize with current monitoring frameworks. In this report, we study methane leaks from oil and gas infrastructure using a tiered monitoring approach that combines bottom-up and top-down approaches in an integrated framework. We describe the individual advantages of bottom-up and top-down sensors in both stationary and mobile settings before characterizing how a fully integrated framework can improve predictions and uncertainties of potential leak locations and their emission rates. Further, we study the impact of different atmospheric (wind) conditions on integrated methane monitoring and develop a probabilistic approach to optimal sensor placement, thereby shortening detection times and improving monitoring capabilities. Last, we discuss how biogenic flux modeling can be used to improve assessment of background methane concentrations needed to fully assess the sensitivity of a tiered monitoring system.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Cosmic-ray Antinuclei as Messengers of New Physics: Status and Outlook for the New Decade

The precise measurement of cosmic-ray antinuclei serves as an important means for identifying the nature of dark matter and other new astrophysical phenomena, and could be used with other cosmic-ray species to understand cosmic-ray production and propagation in the Galaxy. For instance, low-energy antideuterons would provide a "smoking gun" signature of dark matter annihilation or decay, essentially free of astrophysical background. Studies in recent years have emphasized that models for cosmic-ray antideuterons must be considered together with the abundant cosmic antiprotons and any potential observation of antihelium. Therefore, a second dedicated Antideuteron Workshop was organized at UCLA in March 2019, bringing together a community of theorists and experimentalists to review the status of current observations of cosmic-ray antinuclei, the theoretical work towards understanding these signatures, and the potential of upcoming measurements to illuminate ongoing controversies. This review aims to synthesize this recent work and present implications for the upcoming decade of antinuclei observations and searches. This includes discussion of a possible dark matter signature in the AMS-02 antiproton spectrum, the most recent limits from BESS Polar-II on the cosmic antideuteron flux, and reports of candidate antihelium events by AMS-02; recent collider and cosmic-ray measurements relevant for antinuclei production models; the state of cosmic-ray transport models in light of AMS-02 and Voyager data; and the prospects for upcoming experiments, such as GAPS. This provides a roadmap for progress on cosmic antinuclei signatures of dark matter in the coming years.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

SENSEI: Characterization of Single-Electron Events Using a Skipper-CCD

We use a science-grade Skipper Charge Coupled Device (Skipper-CCD) operating in a low-radiation background environment to develop a semi-empirical model that characterizes the origin of single-electron events in CCDs. We identify, separate, and quantify three independent contributions to the single-electron events, which were previously bundled together and classified as ``dark counts'': dark current, amplifier light, and spurious charge. We measure a dark current, which depends on exposure, of (5.89+-0.77)x10^-4 e-/pix/day, and an unprecedentedly low spurious charge contribution of (1.52+-0.07)x10^-4 e-/pix, which is exposure-independent. In addition, we provide a technique to study events produced by light emitted from the amplifier, which allows the detector's operation to be optimized to minimize this effect to a level below the dark-current contribution. Our accurate characterization of the single-electron events allows one to greatly extend the sensitivity of experiments searching for dark matter or coherent neutrino scattering. Moreover, an accurate understanding of the origin of single-electron events is critical to further progress in ongoing R&D efforts of Skipper and conventional CCDs.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Wavelet flow for extragalactic foreground simulations

Extragalactic foregrounds in cosmic microwave background (CMB) observations are both a source of cosmological and astrophysical information and a nuisance to the CMB. Effective field-level modeling that captures their non-Gaussian statistical distributions is increasingly important for optimal information extraction, particularly given the low-noise observations from current and upcoming experiments. Here, we explore the use of Wavelet Flow (WF) models to tackle the novel task of modeling the field-level probability distributions of multi-component CMB secondaries and foregrounds. Specifically, we jointly train correlated CMB lensing convergence (κ) and cosmic infrared background (CIB) maps with a WF model and obtain a network that statistically recovers the input to high accuracy — the trained network generates samples of κ and CIB fields whose average power spectra are within a few percent of the inputs across all scales, and whose Minkowski functionals are similarly accurate compared to the inputs. Leveraging the multiscale architecture of these models, we fine-tune both the model parameters and the priors at each scale independently, optimizing performance across different resolutions. These results demonstrate that WF models can accurately simulate correlated components of CMB secondaries, supporting improved analysis of cosmological data. Our code and trained models can be found on this GitHub repo.

cosmological simulations↗
DOE OSTI2025

DESI 2024: Constraints on physics-focused aspects of dark energy using DESI DR1 BAO data

Baryon acoustic oscillation data from the first year of the Dark Energy Spectroscopic Instrument (DESI) provide near percent-level precision of cosmic distances in seven bins over the redshift range z=0.1–4.2. Here, this paper is the follow-up to the original DESI BAO cosmology paper [A. G. Adame et al. (DESI Collaboration), arXiv:2404.03002], which considered the conventional w 0 w a cold dark matter (CDM) model. We use the novel DESI data, together with other cosmic probes, to constrain the background expansion history using some well-motivated physical classes of dark energy. In particular, we explore three physics-focused behaviors of dark energy from the equation of state and energy density perspectives: the thawing class (matching many simple quintessence potentials), emergent class (where dark energy comes into being recently, as in phase transition models), and mirage class [where phenomenologically the distance to cosmic microwave background (CMB) last scattering is close to that from a cosmological constant Λ despite dark energy dynamics]. All three classes fit the data at least as well as Λ ⁢CDM, and indeed can improve on it by Δ⁢χ 2 ≈ –5 to –17 for the combination of DESI BAO with CMB and supernova data while having one more parameter. The mirage class does essentially as well as w 0 ⁢w a CDM and exhibits moderate to strong Bayesian evidence preference with respect to Λ⁢ CDM. These classes of dynamical behaviors highlight worthwhile avenues for further exploration into the nature of dark energy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Impact of short-read sequencing on the misassembly of a plant genome

Abstract Background Availability of plant genome sequences has led to significant advances. However, with few exceptions, the great majority of existing genome assemblies are derived from short read sequencing technologies with highly uneven read coverages indicative of sequencing and assembly issues that could significantly impact any downstream analysis of plant genomes. In tomato for example, 0.6% (5.1 Mb) and 9.7% (79.6 Mb) of short-read based assembly had significantly higher and lower coverage compared to background, respectively. Results To understand what the causes may be for such uneven coverage, we first established machine learning models capable of predicting genomic regions with variable coverages and found that high coverage regions tend to have higher simple sequence repeat and tandem gene densities compared to background regions. To determine if the high coverage regions were misassembled, we examined a recently available tomato long-read based assembly and found that 27.8% (1.41 Mb) of high coverage regions were potentially misassembled of duplicate sequences, compared to 1.4% in background regions. In addition, using a predictive model that can distinguish correctly and incorrectly assembled high coverage regions, we found that misassembled, high coverage regions tend to be flanked by simple sequence repeats, pseudogenes, and transposon elements. Conclusions Our study provides insights on the causes of variable coverage regions and a quantitative assessment of factors contributing to plant genome misassembly when using short reads and the generality of these causes and factors should be tested further in other species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Removing numerical pathologies in a turbulence parameterization through convergence testing

Discretized numerical models of the atmosphere are usually intended to faithfully represent an underlying set of continuous equations, but this necessary condition is violated sometimes by subtle pathologies that have crept into the discretized equations. Such pathologies can introduce undesirable artifacts, such as sawtooth noise, into the model solutions. The presence of these pathologies can be detected by numerical convergence testing. This study employs convergence testing to verify the discretization of the Cloud Layers Unified By Binormals (CLUBB) model of clouds and turbulence. That convergence testing identifies two aspects of CLUBB's equation set that contribute to undesirable noise in the solutions. First, numerical limiters (i.e. clipping) used by CLUBB introduce discontinuities or slope discontinuities in model fields. Second, this noise can be amplified by an advective term in CLUBB's background diffusion. Smoothing the limiters and removing the advective component of the background diffusion reduces the noise and restores the expected first-order convergence in CLUBB's solutions. These model reformulations improve the results at coarser, near-operational grid spacing and time step in cumulus cloud and dry turbulence tests. In addition, convergence testing is proved to be a valuable tool for detecting pathologies, including unintended discontinuities and grid dependence, in the model equation set.

Mathematics and Computing, Geosciences↗
DOE OSTI2024

Beginner's Guide to Understanding Power System Model Results for Long-Term Resource Plans

This guide was developed to improve decision-making in the electricity planning process by strengthening dialogue between electricity system planners and stakeholders throughout the process. This document was created for anyone who is first getting exposed to scenario results from power system planning models, or for those who are beginning to interact with power system modelers. It does not assume any experience with power system modeling, or familiarity with electricity planning concepts. This guide can be helpful to a wide range of people, including but not limited to state utility commissioners and commission staff, state energy offices, and intervenors in the planning process. It is targeted to those who are evaluating or reviewing model results presented by a utility, which typically span utility-scale generation and storage (and associated transmission) assets. This guide is designed to provide enough background and guidance that stakeholders can formulate and ask questions to evaluate model results through a deeper understanding of the key assumptions, data, and methods that drive decision outcomes. The ultimate purpose of this work is to increase transparency in the electricity planning process by enabling stakeholders to better engage and evaluate model results. A follow-on guide will provide a deeper dive into emerging topics, which are expected to become more prominent in the electricity planning process, but whose methods and approaches are still being refined.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

A test of the standard cosmological model with geometry and growth

We perform a general test of the ΛCDM and wCDM cosmological models by comparing constraints on the geometry of the expansion history to those on the growth of structure. Specifically, we split the total matter energy density, Ω M , and (for wCDM) dark energy equation of state, w, into two parameters each: one that captures the geometry, and another that captures the growth. We constrain our split models using current cosmological data, including type Ia supernovae, baryon acoustic oscillations, redshift space distortions, gravitational lensing, and cosmic microwave background (CMB) anisotropies. We focus on two tasks: (i) constraining deviations from the standard model, captured by the parameters ΔΩ M ≡ Ω M grow -Ω M geom and Δw ≡ w grow -w geom , and (ii) investigating whether the S 8 tension between the CMB and weak lensing can be translated into a tension between geometry and growth, i.e. ΔΩ M ≠ 0, Δw ≠ 0. In both the split ΛCDM and wCDM cases, our results from combining all data are consistent with ΔΩ M = 0 and Δ w = 0. If we omit BAO/RSD data and constrain the split wCDM cosmology, we find the data prefers Δw < 0 at 3.6σ significance and ΔΩ M > 0 at 4.2σ evidence. We also find that for both CMB and weak lensing, ΔΩ M and S 8 are correlated, with CMB showing a slightly stronger correlation. Furthermore, the general broadening of the contours in our extended model does alleviate the S 8 tension, but the allowed nonzero values of ΔΩ M do not encompass the S 8 values that would point toward a mismatch between geometry and growth as the origin of the tension.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Natural variation in the consequences of gene overexpression and its implications for evolutionary trajectories

Copy number variation through gene or chromosome amplification provides a route for rapid phenotypic variation and supports the long-term evolution of gene functions. Although the evolutionary importance of copy-number variation is known, little is understood about how genetic background influences its tolerance. Here, we measured fitness costs of over 4000 overexpressed genes in 15 Saccharomyces cerevisiae strains representing different lineages, to explore natural variation in tolerating gene overexpression (OE). Strain-specific effects dominated the fitness costs of gene OE. We report global differences in the consequences of gene OE, independent of the amplified gene, as well as gene-specific effects that were dependent on the genetic background. Natural variation in the response to gene OE could be explained by several models, including strain-specific physiological differences, resource limitations, and regulatory sensitivities. This work provides new insight on how genetic background influences tolerance to gene amplification and the evolutionary trajectories accessible to different backgrounds.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

BayesMT: A Probabilistic Bayesian Framework for the Seismic Moment Tensor

Moment tensors (MTs) have long been used in earthquake and explosion source analysis, and there has been a renewed interest in how they can inform us about the seismic source, particularly in the geophysical monitoring community due to its application in event identification and yield analysis. However, parameter uncertainties in seismic MT inversion are rarely available. The inverse procedure often does not quantify MT model errors such as event location, data noise and Earth model that are essential for estimating solution robustness. To address this need, we propose to adopt the Bayesian probabilistic framework to incorporate uncertainties in MT inversions. In this study, we present the theoretical background of a probabilistic Bayesian framework for MT inversion accounting for model and measurements errors and illustrate the implementation of the method using a synthetic example.

58 GEOSCIENCES↗
DOE OSTI2026

Search for low-mass hidden-valley dark showers with non-prompt muon pairs in proton-proton collisions at $\sqrt{s}=13$ TeV

A search for signatures of a dark analog to quantum chromodynamics is performed. The analysis targets long-lived dark mesons that decay into standard-model particles, with a high branching fraction of the dark mesons decaying into muons. The dark mesons are formed by the hadronisation of dark partons, which are produced by a decay of the Higgs boson. The search is performed using a data set corresponding to an integrated luminosity of 41.6 fb −1 , which was collected in proton-proton collisions at $\sqrt{s}=13$ TeV by the CMS experiment at the CERN LHC in 2018 using non-prompt muon triggers. The search is based on resonant muon pair signatures. Machine-learning techniques are employed in the analysis, utilising boosted decision trees to discriminate between signal and background. No significant excess is observed above the standard model expectation. Upper limits on the branching fraction of the Higgs boson decaying to dark partons are determined to be as low as 10−4 at 95% confidence level, surpassing and extending the existing limits on models with dark $\tilde{ω}$ mesons for mean proper decay lengths of less than 500 mm and for $\tilde{ω}$ masses down to 0.3 GeV. First limits are set for extended dark-shower models with two dark flavours that contain dark photons, probing their masses down to 0.33 GeV.

Beyond Standard Model↗