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NASA NTRS1999

Global and Seasonal Aerosol Optical Depths Derived From Ultraviolet Observations by Satellites (TOMS)

It has been shown that absorbing aerosols (dust, smoke, volcanic ash) can be detected in the ultraviolet wavelengths (331 nm to 380 nm) from satellite observations (TOMS, Total Ozone Mapping Spectrometer) over both land and water. The theoretical basis for these observations and their conversions to optical depths is discussed in terms of an aerosol index AI or N-value residue (assigned positive for absorbing aerosols). The theoretical considerations show that negative values of the AI frequently represent the presence of non-absorbing aerosols (NA) in the troposphere (mostly pollution in the form of sulfates, hydrocarbons, etc., and some natural sulfate aerosols) with particle sizes near 0.1 to 0.2 microns or less. The detection of small-particle non-absorbing aerosols from the measured backscattered radiances is based on the observed wavelength dependence from Mie scattering after the background Rayleigh scattering is subtracted. The Mie scattering from larger particles, 1 micron or more (e.g., cloud water droplets) has too small a wavelength dependence to be detected by this method. In regions that are mostly cloud free, aerosols of all sizes can be seen in the single channel 380 nm or 360 nm radiance data. The most prominent Al feature observed is the strong asymmetry in aerosol amount between the Northern and Southern Hemispheres, with the large majority of NA occurring above 20degN latitude. The maximum values of non-absorbing aerosols are observed over the eastern U.S. and most of western Europe corresponding to the areas of highest industrial pollution. Annual cycles in the amount of NA are observed over Europe and North America with maxima occurring in the summer corresponding to times of minimum wind transport. Similarly, the maxima in the winter over the Atlantic Ocean occurs because of wind borne transport from the land. Most regions of the world have the maximum amount of non-absorbing aerosol in the December to January period except for the eastern North America and Europe. Comparisons of the estimated TOMS aerosol optical depths show good agreement in magnitude and seasonal dependence with sun-photometer optical depths obtained at Goddard Space Flight Center (39degN 76.88degW) in the U.S. and in Lille (50.63degN 3.07degE) in France. The study of these aerosols is important for detecting the sources of industrial pollution and its redistribution by winds on a global basis, as well as its effect on reducing the UV irradiance at the Earth's surface.

Herman, J. R.↗

DOE OSTI2021

Co-design Center for Exascale Machine Learning Technologies (ExaLearn)

We report rapid growth in data, computational methods, and computing power is driving a remarkable revolution in what variously is termed machine learning (ML), statistical learning, computational learning, and artificial intelligence. In addition to highly visible successes in machine-based natural language translation, playing the game Go, and self-driving cars, these new technologies also have profound implications for computational and experimental science and engineering, as well as for the exascale computing systems that the Department of Energy (DOE) is developing to support those disciplines. Not only do these learning technologies open up exciting opportunities for scientific discovery on exascale systems, they also appear poised to have important implications for the design and use of exascale computers themselves, including high-performance computing (HPC) for ML and ML for HPC. The overarching goal of the ExaLearn co-design project is to provide exascale ML software for use by Exascale Computing Project (ECP) applications, other ECP co-design centers, and DOE experimental facilities and leadership class computing facilities.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Calibration of a soft secondary vertex tagger using proton-proton collisions at s = 13 TeV with the ATLAS detector

Several processes studied by the ATLAS experiment at the Large Hadron Collider produce low-momentum b-flavored hadrons in the final state. This paper describes the calibration of a dedicated tagging algorithm that identifies b-flavored hadrons outside of hadronic jets by reconstructing the soft secondary vertices originating from their decays. The calibration is based on a proton-proton collision dataset at a center-of-mass energy of 13 TeV corresponding to an integrated luminosity of 140 fb -1 . Scale factors used to correct the algorithm’s performance in simulated events are extracted for the b-tagging efficiency and the mistag rate of the algorithm using a data sample enriched in $t\overline{t}$ events. Several orthogonal measurement regions are defined, binned as a function of the multiplicities of soft secondary vertices and jets containing a b-flavored hadron in the event. The mistag rate scale factors are estimated separately for events with low and high average numbers of interactions per bunch crossing. The results, which are derived from events with low missing transverse momentum, are successfully validated in a phase space characterized by high missing transverse momentum and therefore are applicable to new physics searches carried out in either phase space regime.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Predictions for the Detectability of Milky Way Satellite Galaxies and Outer-Halo Star Clusters with the Vera C. Rubin Observatory

We predict the sensitivity of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) to faint, resolved Milky Way satellite galaxies and outer-halo star clusters. We characterize the expected sensitivity using simulated LSST data from the LSST Dark Energy Science Collaboration (DESC) Data Challenge 2 (DC2) accessed and analyzed with the Rubin Science Platform as part of the Rubin Early Science Program. We simulate resolved stellar populations of Milky Way satellite galaxies and outer-halo star clusters over a wide range of sizes, luminosities, and heliocentric distances, which are broadly consistent with expectations for the Milky Way satellite system. We inject simulated stars into the DC2 catalog with realistic photometric uncertainties and star/galaxy separation derived from the DC2 data itself. We assess the probability that each simulated system would be detected by LSST using a conventional isochrone matched-filter technique. We find that assuming perfect star/galaxy separation enables the detection of resolved stellar systems with $M_V$ = 0 mag and $r_{1/2}$ = 10 pc with >50% efficiency out to a heliocentric distance of ~250 kpc. Similar detection efficiency is possible with a simple star/galaxy separation criterion based on measured quantities, although the false positive rate is higher due to leakage of background galaxies into the stellar sample. When assuming perfect star/galaxy classification and a model for the galaxy-halo connection fit to current data, we predict that 89 +/- 20 Milky Way satellite galaxies will be detectable with a simple matched-filter algorithm applied to the LSST wide-fast-deep data set. Different assumptions about the performance of star/galaxy classification efficiency can decrease this estimate by ~7%-25%, which emphasizes the importance of high-quality star/galaxy separation for studies of the Milky Way satellite population with LSST.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Observation of double parton scattering in same-sign W boson pair production in pp collisions at $\sqrt{s} = 13$ TeV with the ATLAS detector

This letter reports the measurement of double parton scattering in same-sign W boson pair production with the ATLAS detector. The data set used corresponds to an integrated luminosity of 140 fb -1 of proton–proton collisions at a center-of-mass energy of 13 TeV, collected during Run 2 of the Large Hadron Collider. The study is performed in final states including two same-charge leptons, electron or muon, missing transverse momentum, and up to one jet. An excess of events is observed over the expected background contributions with a significance of 8.8 standard deviations. The measured fiducial cross section times leptonic branching fraction is 4.59 ± 0.64 fb. The measurement corresponds to a double parton scattering effective cross section of 10.6 ± 1.8 mb.

DPS↗

DOE OSTI2025

Observation of D + → η ′ μ + ν μ and First Experimental Study of D + → η ′ ℓ + ν ℓ Decay Dynamics

Using 20.3 fb − 1 of e + e − collision data collected at the center-of-mass energy 3.773 GeV with the BESIII detector, we report the first observation of the semileptonic decay D + → η ′ μ + ν μ with significance of 8.6 σ including systematic uncertainties, and an improved measurement of D + → η ′ e + ν e . The branching fractions of D + → η ′ μ + ν μ and D + → η ′ e + ν e are determined to be ( 1.92 ± 0.2 8 stat ± 0.0 8 syst ) × 10 − 4 and ( 1.79 ± 0.1 9 stat ± 0.0 7 syst ) × 10 − 4 , respectively. The ratio of the two branching fractions is determined to be R μ / e = 1.07 ± 0.1 9 stat ± 0.0 3 syst , which agrees with the theoretical expectation of lepton flavor universality within the standard model. From an analysis of the D + → η ′ ℓ + ν ℓ decay dynamics, the product of the hadronic form factor f + η ′ ( 0 ) and the Cabibbo-Kobayashi-Maskawa matrix element | V c d | is measured for the first time, giving f + η ′ ( 0 ) | V c d | = ( 5.92 ± 0.5 6 stat ± 0.1 3 syst ) × 10 − 2 . The η − η ′ mixing angle in the quark flavor basis is determined to be ϕ P = ( 39.8 ± 0.8 stat ± 0.3 syst ) ° . Published by the American Physical Society 2025

Ablikim, M.↗

DOE OSTI2023

Integrated and differential fiducial cross-section measurements for the vector boson fusion production of the Higgs boson in the $H → WW^* → evμv$ decay channel at 13 TeV with the ATLAS detector

The vector-boson production cross-section for the Higgs boson decay in the H→WW*→eνμν channel is measured as a function of kinematic observables sensitive to the Higgs boson production and decay properties as well as integrated in a fiducial phase space. The analysis is performed using the proton-proton collision data collected by the ATLAS detector in Run 2 of the LHC at $\sqrt{s}$=13 TeV center-of-mass energy corresponding to an integrated luminosity of 139 fb -1 . The different flavor final state is studied by selecting an electron and a muon originating from a pair of W bosons and compatible with the Higgs boson decay. The data are corrected for the effects of detector inefficiency and resolution, and the measurements are compared with different state-of-the-art theoretical predictions. The differential cross-sections are used to constrain anomalous interactions described by dimension-six operators in an Effective Field Theory.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

High-redshift millennium and astrid galaxies in effective field theory at the field level

Effective field theory (EFT) modeling is expected to be a useful tool in the era of future higher-redshift galaxy surveys such as DESI-II and Spec-S5 due to its robust description of various large-scale structure tracers. However, large values of EFT bias parameters of higher-redshift galaxies could jeopardize the convergence of the perturbative expansion. Here, in this paper we measure the bias parameters and other EFT coefficients from samples of two types of star-forming galaxies in the state-of-the-art MilleniumTNG and astrid hydrodynamical simulations. Our measurements are based on the field-level EFT forward model that allows for precision EFT parameter measurements by virtue of cosmic variance cancellation. Specifically, we consider approximately representative samples of Lyman-break galaxies (LBGs) and Lyman-𝛼 emitters (LAEs) that are consistent with the observed (angular) clustering and number density of these galaxies at 𝑧 = 3. Reproducing the linear biases and number densities observed from existing LAE and LBG data, we find quadratic bias parameters that are roughly consistent with those predicted from the halo model coupled with a simple halo occupation distribution model. We also find nonperturbative velocity contributions (fingers of God) of a similar size for LBGs to the familiar case of luminous red galaxies. However, these contributions are quite small for LAEs despite their large satellite fraction values of up to ∼ 30%. Our results indicate that the effective momentum reach 𝑘 max at 𝑧 = 3 for LAEs (LBGs) will be in the range 0.3−0.6⁢ℎ Mpc −1 (0.2−0.8⁢ℎ Mpc −1 ), suggesting that EFT will perform well for high-redshift galaxy clustering. This work provides the first step toward obtaining realistic simulation-based priors on EFT parameters for LAEs and LBGs.

Sullivan, James M. [Massachusetts Inst. of Technol↗

DOE OSTI2024

Study of e + e − → ω X ( 3872 ) and γ X ( 3872 ) from 4.66 to 4.95 GeV

Using data samples with an integrated luminosity of 4.5 fb − 1 collected by the BESIII detector at center-of-mass energies ranging from 4.66 GeV to 4.95 GeV, we study the processes of e + e − → ω X ( 3872 ) and e + e − → γ X ( 3872 ) . With the e + e − → ω X ( 3872 ) process, the branching fraction ratio R ≡ B ( X ( 3872 ) → γ J / ψ ) B ( X ( 3872 ) → π + π − J / ψ ) is measured to be 0.38 ± 0.2 0 stat ± 0.0 1 syst ( R < 0.83 at 90% confidence level). In addition, we measure the ratio of the average cross section of e + e − → ω X ( 3872 ) to e + e − → ω χ c 1 ( ω χ c 2 ) to be σ ω X ( 3872 ) / σ ω χ c 1 ( σ ω X ( 3872 ) / σ ω χ c 2 ) = 5.2 ± 1.0 stat ± 1.9 syst ( 5.5 ± 1.1 stat ± 2.4 syst ) . Finally, we search for the process of e + e − → γ X ( 3872 ) , and no obvious signal is observed. The upper limit on the ratio of the average cross section of e + e − → γ X ( 3872 ) to e + e − → ω X ( 3872 ) is set as σ γ X ( 3872 ) / σ ω X ( 3872 ) < 0.23 at 90% confidence level. Published by the American Physical Society 2024

Astronomy & Astrophysics↗

NASA NTRS1994

Designing an autonomous environment for mission critical operation of the EUVE satellite

Since the launch of NASA's Extreme Ultraviolet Explorer (EUVE) satellite in 1992, there has only been a handful of occurrences that have warranted manual intervention in the EUVE Science Operations Center (ESOC). So, in an effort to reduce costs, the current environment is being redesigned to utilize a combination of off-the-shelf packages and recently developed artificial intelligence (AI) software to automate the monitoring of the science payload and ground systems. The successful implementation of systemic automation would allow the ESOC to evolve from a seven day/week, three shift operation, to a seven day/week one shift operation. First, it was necessary to identify all areas considered mission critical. These were defined as follows: (1) The telemetry stream must be monitored autonomously and anomalies identified. (2) Duty personnel must be automatically paged and informed of the occurrence of an anomaly. (3) The 'basic' state of the ground system must be assessed. (4) Monitors should check that the systems and processes needed to continue in a 'healthy' operational mode are working at all times. (5) Network loads should be monitored to ensure that they stay within established limits. (6) Connectivity to Goddard Space Flight Center (GSFC) systems should be monitored as well, not just for connectivity of the network itself but also for the ability to transfer files. (7) All necessary peripheral devices should be monitored. This would include the disks, routers, tape drives, printers, tape carousel, and power supplies. (8) System daemons such as the archival daemon, the Sybase server, the payload monitoring software, and any other necessary processes should be monitored to ensure that they are operational. (9) The monitoring system needs to be redundant so that the failure of a single machine will not paralyze the monitors. (10) Notification should be done by means of looking though a table of the pager numbers for current 'on call' personnel. The software should be capable of dialing out to notify, sending email, and producing error logs. (11) The system should have knowledge of when real-time passes and tape recorder dumps will occur and should know that these passes and data transmissions are successful. Once the design criteria were established, the design team split into two groups: one that addressed the tracking, commanding, and health and safety of the science payload and another group that addressed the ground systems and communications aspects of the overall system.

Abedini, Annadiana↗

DOE OSTI2024

Measurements of the branching fractions of semileptonic D s + decays via e + e − → D s * + D s * −

We measure the absolute branching fractions of semileptonic D s + decays via the e + e − → D s * + D s * − process using e + e − collision data corresponding to an integrated luminosity of 10.64 fb − 1 collected by the BESIII detector at center-of-mass energies between 4.237 and 4.699 GeV. The branching fractions are B ( D s + → η e + ν e ) = ( 2.35 ± 0.11 stat ± 0.10 syst ) % , B ( D s + → η ′ e + ν e ) = ( 0.82 ± 0.09 stat ± 0.04 syst ) % , B ( D s + → ϕ e + ν e ) = ( 2.21 ± 0.16 stat ± 0.11 syst ) % , B ( D s + → f 0 ( 980 ) e + ν e , f 0 ( 980 ) → π + π − ) = ( 0.15 ± 0.02 stat ± 0.01 syst ) % , B ( D s + → K 0 e + ν e ) = ( 0.24 ± 0.0 4 stat ± 0.0 1 syst ) % , and B ( D s + → K * 0 e + ν e ) = ( 0.19 ± 0.0 3 stat ± 0.0 1 syst ) % . These results are consistent with those measured via the e + e − → D s * ± D s ∓ process by BESIII and CLEO. Using two-parameter series expansion, the hadronic transition form factors of D s + → η e + ν e , D s + → η ′ e + ν e , and D s + → K 0 e + ν e are determined to be f + η ( 0 ) = 0.442 ± 0.02 2 stat ± 0.01 7 syst , f + η ′ ( 0 ) = 0.557 ± 0.06 2 stat ± 0.02 4 syst , and f + K 0 ( 0 ) = 0.677 ± 0.09 8 stat ± 0.02 3 syst . Published by the American Physical Society 2024

Ablikim, M.↗

DOE OSTI2025

The BTSbot-nearby Discovery of SN 2024jlf: Rapid, Autonomous Follow-up Probes Interaction in an 18.5 Mpc Type IIP Supernova

We present observations of the Type IIP supernova (SN) SN 2024jlf, including spectroscopy beginning just 0.7 days (∼17 hr) after first light. Rapid follow-up was enabled by the new BTSbot-nearby program, which involves autonomously triggering target-of-opportunity requests for new transients in Zwicky Transient Facility data that are coincident with nearby (D < 60 Mpc) galaxies and identified by the BTSbot machine learning model. Early photometry and nondetections shortly prior to first light show that SN 2024jlf initially brightened by >4 mag day −1 , quicker than ∼90% of Type II SNe. Early spectra reveal weak flash ionization features: narrow, short-lived (1.3 < τ[days] < 1.8) emission lines of Hα, He II , and C IV . Assuming a wind velocity of v w = 50 km s −1 , these properties indicate that the red supergiant progenitor exhibited enhanced mass loss in the last year before explosion. We constrain the mass-loss rate to $1{0}^{-4}\lt \dot{M}\,[{M}_{\odot }\,{\mathrm{yr}}^{-1}]\lt 1{0}^{-3}$ by matching observations to model grids from two independent radiative hydrodynamics codes. BTSbot-nearby automation minimizes spectroscopic follow-up latency, enabling the observation of ephemeral early-time phenomena exhibited by transients.

core-collapse supernovae↗

DOE OSTI2026

Crowdsourcing the Frontier: Advancing Hybrid Physics‐ML Climate Simulation via a $\$$50,000 Kaggle Competition

Subgrid machine-learning (machine learning [ML]) parameterizations have the potential to introduce a new generation of climate models that incorporate the effects of higher-resolution physics without incurring the prohibitive computational cost associated with more explicit physics-based simulations. However, important issues, ranging from online instability to inconsistent online performance, have limited their operational use for long-term climate projections. To more rapidly drive progress in solving these issues, domain scientists and ML researchers opened up the offline aspect of this problem to the broader ML and data science community with the release of ClimSim, a NeurIPS Data sets and Benchmarks publication, and an associated Kaggle competition. This paper reports on the downstream results of the Kaggle competition by coupling emulators inspired by the winning teams' architectures to an interactive climate model (including full cloud microphysics, a regime historically prone to online instability) and systematically evaluating their online performance. Our results demonstrate that online stability in the low-resolution real-geography setting is reproducible across multiple diverse architectures, which we consider a key milestone. All tested architectures exhibit strikingly similar offline and online biases, though their responses to architecture-agnostic design choices (e.g., expanding the list of input variables) can differ significantly. Multiple Kaggle-inspired architectures achieve state-of-the-art results on certain metrics such as zonal mean bias patterns and global Root Mean Squared Error, indicating that crowdsourcing the essence of the offline problem is one path to improving online performance in hybrid physics-AI climate simulation.

Environmental sciences↗

NASA NTRSDate not supplied

Application of ML/AI for Identifying Earth Science Datasets in Research Publications

NASA Data Active Archive Centers, or DAACs, ingest, store and distribute data acquired from satellites, ground systems as well as modelling data. These data are organized by the datasets, each presenting collection of files usually associated with the certain mission, instrument, processing level, parameter(s), algorithm and/or model. The number of datasets offered by a single DAAC to the public varies. GES DISC, for example, currently offers for public use approximately ~1,300 datasets. While each publicly offered dataset comes with supporting documentation, it is challenging for novice and even experienced scientists to navigate among the datasets that offer similar parameters to find the datasets for their particular research application. Supplying dataset documentation with the scientific paper citations that refer to that dataset provides means for the dataset users to educate themselves with the application research that dataset is being used in. Collecting citations of the papers that use the datasets for their research yield valuable insights into application areas of those datasets, information about usage of the dataset groups for specific applications and those application topics. It also gives insights into the “deep metrics” of the dataset usage, as opposed to the common metrics of the dataset usage such as number of users who downloaded the dataset files and volumes of downloaded data. Association of a certain scientific paper with the dataset(s) presents a challenge because most of the paper authors do not properly cite the datasets, datasets usually have cryptic names and Digital Object Identifiers (DOIs) that are used for dataset identification were assigned to the datasets only few years ago. Simple Google or online library search do not provide even meaningful fraction of the results when performed by the dataset name or DOI, however they provide too many results when the search is done by more broader terms such as mission and instrument names. Attempts to create an AI system capable to identify dataset in the scientific papers have already been made using neural networks classifiers on the basis of the dataset mission, instrument and variable name. This method was applied to NASA SEDAC, which has 41 datasets in total. In GES DISC there can be as many as ~100 datasets per mission/instrument with some of the datasets consisting of multiple variables so there is a need for more differentiating parameters for dataset identification in the paper. The approach we are currently investigating is creating AI classifiers that are based on multiple dataset features, or keywords, extracted from the NASA Earthdata Common Dataset Repository (CMR). The features are weighted based on how precisely they can identify a dataset. The classifier uses preprocessed paper text as input and searches for the CMR datasets whose feature sets are the closest to the feature sets contained in the paper. The challenges of dataset identification include variety of ways the paper authors describe the datasets in their papers and incomplete tagging of the CMR dataset description (DIFs).

Irina Gerasimov↗

DOE OSTI2026

Enhancing segmentation fairness through curriculum learning and progressive loss: a centralized and federated perspective on radiograph analysis

Bias in medical image segmentation can lead to unequal performance across demographic subgroups, raising concerns about fairness and reliability in clinical AI systems. While deep learning models have achieved high segmentation accuracy, ensuring equitable performance across race and gender remains a significant challenge, particularly in privacy-sensitive healthcare environments. This study investigates fairness-aware medical image segmentation for hip and knee radiographs using deep learning models evaluated in both centralized and Federated Learning (FL) settings. We introduce Curriculum Learning (CL) strategies and Progressive Loss (PL) functions to regulate sample difficulty during training. In addition, we propose two novel fairness-oriented federated learning algorithms, Federated Intersection over Union (FedIoU) and Federated Intersection over Union with Outlier Analysis (FedIoUoutlier). Experiments are conducted using multiple segmentation backbones and simulated multi-site data partitions derived from the Osteoarthritis Initiative dataset. Model performance is evaluated using Intersection over Union (IoU), IoU standard deviation, Skewed Error Ratio (SER), and Min-Max Disparity across race and gender subgroups. Statistical significance was verified using paired t-tests to compare per-sample IoU performance against baseline configurations. Across both hip and knee segmentation tasks, curriculum learning and progressive loss strategies consistently improved segmentation accuracy and reduced demographic performance disparities in centralized training. In federated settings, fairness-aware aggregation further enhanced performance. Notably, FedIoUoutlier combined with balanced curriculum learning and tiered progressive loss achieved the highest mean IoU while yielding the lowest SER and Min-Max Disparity, indicating improved fairness without sacrificing accuracy. In several configurations, federated models matched or exceeded the performance of optimized centralized models, with statistically significant improvements in per-sample IoU over baseline configurations. The results demonstrate that structured training strategies and fairness-aware federated aggregation can jointly improve accuracy, stability, and demographic fairness in medical image segmentation. By integrating curriculum learning, progressive loss, and novel FL algorithms, this work provides a practical pathway toward equitable and privacy-preserving AI systems for medical imaging.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Observation of the W -Annihilation Process D s + → ω ρ + and Measurement of D s + → ϕ ρ + in D s + → π + π + π − π 0 π 0 Decays

We present the first amplitude analysis and branching fraction measurement of the decay D s + → π + π + π − π 0 π 0 , using e + e − collision data collected with the BESIII detector at center-of-mass energies between 4.128 and 4.226 GeV corresponding to an integrated luminosity of 7.33 fb − 1 , and report the first observation of the pure W -annihilation decay D s + → ω ρ + with a branching fraction of ( 0.99 ± 0.0 8 stat − 0.07 + 0.05 syst ) % . In comparison to the low significance of the D wave in the decay D s + → ϕ ρ + , the dominance of the D wave over the S and P waves, with a fraction of ( 51.85 ± 7.2 8 stat − 7.90 + 4.83 syst ) % observed in the decay D s + → ω ρ + , provides crucial information for the “polarization puzzle,” as well as for the understanding of charm meson decays. The branching fraction of D s + → π + π + π − π 0 π 0 is measured to be ( 4.41 ± 0.1 5 stat ± 0.1 3 syst ) % . Moreover, the branching fraction of D s + → ϕ ρ + is measured to be ( 3.98 ± 0.3 3 stat − 0.19 + 0.21 syst ) % , and the R ϕ = B ( ϕ → π + π − π 0 ) / B ( ϕ → K + K − ) is determined to be ( 0.222 ± 0.01 9 stat − 0.016 + 0.016 syst ), which is consistent with the previous measurement based on charm meson decays, but deviates from the results from e + e − annihilation and K − N scattering experiments by more than 3 σ . Published by the American Physical Society 2025

Ablikim, M.↗

DOE OSTI2024

Measurement of the branching fraction of D s + → ℓ + ν ℓ via e + e − → D s * + D s * −

Based on 10.64 fb − 1 of e + e − collision data taken at center-of-mass energies between 4.237 and 4.699 GeV with the BESIII detector, we study the leptonic D s + decays using the e + e − → D s * + D s * − process. The branching fractions of D s + → ℓ + ν ℓ ( ℓ = μ , τ ) are measured to be B D s + → μ + ν μ = ( 0.547 ± 0.02 6 stat ± 0.01 6 syst ) % and B D s + → τ + ν τ = ( 5.60 ± 0.1 6 stat ± 0.2 0 syst ) % , respectively. The product of the decay constant and Cabibbo-Kobayashi-Maskawa matrix element | V c s | is determined to be f D s + | V c s | = ( 246.5 ± 5.9 stat ± 3.6 syst ± 0.5 input ) μ ν and f D s + | V c s | = ( 252.7 ± 3.6 stat ± 4.5 syst ± 0.6 input ) τ ν MeV , respectively. Taking the value of | V c s | from a global fit in the Standard Model, we obtain f D s + = ( 253.2 ± 6.0 stat ± 3.7 syst ± 0.6 input ) μ ν and f D s + = ( 259.6 ± 3.7 stat ± 4.6 syst ± 0.6 input ) τ ν MeV , respectively. Conversely, taking the value for f D s + from the latest lattice quantum chromodynamics calculation, we obtain | V c s | = ( 0.986 ± 0.02 3 stat ± 0.01 4 syst ± 0.00 3 input ) μ ν and | V c s | = ( 1.011 ± 0.01 4 stat ± 0.01 8 syst ± 0.00 3 input ) τ ν , respectively. Published by the American Physical Society 2024

Astronomy & Astrophysics↗

DOE OSTI2025

Scale-up Unlearnable Examples Learning with High-performance Computing

Recent advancements in AI models, like ChatGPT, are structured to retain user interactions, which could inadvertently include sensitive healthcare data. In the healthcare field, particularly when radiologists use AI-driven diagnostic tools hosted on online platforms, there is a risk that medical imaging data may be repurposed for future AI training without explicit consent, spotlighting critical privacy and intellectual property concerns around healthcare data usage. Addressing these privacy challenges, a novel approach known as Unlearnable Examples (UEs) has been introduced, aiming to make data unlearnable to deep learning models. A prominent method within this area, called Unlearnable Clustering (UC), has shown improved UE performance with larger batch sizes but was previously limited by computational resources (e.g., a single workstation). To push the boundaries of UE performance with theoretically unlimited resources, we scaled up UC learning across various datasets using Distributed Data Parallel (DDP) training on the Summit supercomputer. Our goal was to examine UE efficacy at high-performance computing (HPC) levels to prevent unauthorized learning and enhance data security, particularly exploring the impact of batch size on UE’s unlearnability. Utilizing the robust computational capabilities of the Summit, extensive experiments were conducted on diverse datasets such as Pets, MedMNist, Flowers, and Flowers102. Our findings reveal that both overly large and overly small batch sizes can lead to performance instability and affect accuracy. However, the relationship between batch size and unlearnability varied across datasets, highlighting the necessity for tailored batch size strategies to achieve optimal data protection. The use of Summit’s high-performance GPUs, along with the efficiency of the DDP framework, facilitated rapid updates of model parameters and consistent training across nodes. Our results underscore the critical role of selecting appropriate batch sizes based on the specific characteristics of each dataset to prevent learning and ensure data security in deep learning applications. The source code is publicly available at https: // github. com/ hrlblab/ UE_ HPC .

Zhu, Yanfan [Vanderbilt University, Nashville, TN,↗