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At least 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Effects of spatial energy distribution-induced porosity on mechanical properties of laser powder bed fusion 316L stainless steel

Laser powder bed fusion (LPBF) additive manufacturing (AM) offers a variety of advantages over traditional manufacturing, however its usefulness for manufacturing of high-performance components is currently hampered by internal defects (porosity) created during the LPBF process that have an unknown impact on global mechanical performance. By inducing porosity distributions through variations in print energy density and inspecting the resulting tensile samples using computed tomography, nearly 50,000 pores across 75 samples were identified. Porosity characteristics were quantitatively extracted from inspection data and compared with mechanical properties to understand the strength of relationships between porosity and global tensile performance. Useful porosity characteristics were identified for prediction of part performance. Results indicate that ductility and strain at ultimate tensile strength are the global tensile properties most significantly impacted by porosity and can be predicted with reasonable accuracy using simple porosity shape descriptors such as volume, diameter, and surface area. Moreover, it was found that the largest pores influenced behavior most significantly. Specifically, pores in excess of 125 µm in diameter were found to be a sufficient threshold for property estimation. These results establish an initial understanding of the complex defect-performance relationship in AM 316L stainless steel and can be leveraged to develop certification standards and improve confidence in part quality and reliability for the broader set of engineering alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Nanoparticle-enhanced absorptivity of copper during laser powder bed fusion

We report laser powder bed fusion (LPBF) of pure copper for thermal and electrical applications is hampered by its low near-infrared absorptivity and high thermal diffusivity. These material properties make it very difficult to localize the thermal energy needed to produce high density 3D printed parts. Modification of metal powders via nanoparticle additives is a promising approach to increasing absorptivity, but the effect of nanoparticles on absorptivity and melting behavior during LPBF is not well understood. In this study, we developed an in situ calorimetry system to measure effective absorptivity during LPBF on copper substrates. We decorated copper substrates using three nanoparticle systems (CuS, TiB 2 , multilayer graphene flakes) and demonstrated an enhanced absorptivity of the decorated substrates relative to pure copper. Graphene nanoflakes resulted in the highest improved absorption relative to pure copper from 0.09 to 0.48, due to their stability at high laser scanning powers. A thermomechanical model with convective heat transfer provided confidence in the measurements by reproducing the experimental melt pool traces. Full 3D cylindrical prints demonstrated an improvement in relative density of the copper-graphene powder prints (in the range of 0.930–0.992), relative to that of as-purchased copper powder prints (in the range of 0.854–0.972). This work provides a fundamental study of nanoparticle-enabled LPBF of highly reflective metals and demonstrates a viable route for expanding the library of reliably printable metals.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Scalable in situ non-destructive evaluation of additively manufactured components using process monitoring, sensor fusion, and machine learning

Laser Powder Bed Fusion (L-PBF) Additive Manufacturing (AM) is among the metal 3D printing technologies most broadly adopted by the manufacturing industry. However, the current industry qualification paradigm for critical-application L-PBF parts relies heavily on expensive non-destructive inspection techniques, which significantly limits the use-cases of L-PBF. In situ monitoring of the process promises a less expensive alternative to ex situ testing, but existing sensor technologies and data analysis techniques struggle to detect sub-surface flaws (e.g., porosity and cracking) on production-scale L-PBF printers. In this work, an in situ NDE (INDE) system was engineered to detect subsurface flaws detected in X-Ray Computed Tomography (XCT) directly from process monitoring data. A multilayer, multimodal data input allowed the INDE system to detect numerous subsurface flaws in the size range of 200–1000µm using a novel human-in-the-loop annotation procedure. Furthermore, a framework was established for generating probability-of-detection (POD) and probability-of-false-alarm (PFA) curves compliant with NDE standards by systematically comparing instances of detected subsurface flaws to post-build XCT data. Here, we also introduce for the first time in the AM in situ sensing literature the a 90/95 – the flaw size corresponding to a 90% detection rate on the lower 95% confidence interval of the POD curve. The INDE system successfully demonstrated POD capabilities commensurate with traditional NDE methods. Traditional ML performance metrics were also shown to be inadequate for assessing the ability of the INDE system’s flaw detection performance. It is the hope of the authors that future studies will adopt the POD and PFA approach outlined here to provide better insight into the utility of process monitoring for AM.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Harnessing on-machine metrology data for prints with a surrogate model for laser powder directed energy deposition

In this study, we leverage the massive amount of multi-modal on-machine metrology data generated from Laser Powder Directed Energy Deposition (LP-DED) to construct a comprehensive surrogate model of the 3D printing process. By employing Dynamic Mode Decomposition with Control (DMDc), a data-driven technique, we capture the complex physics inherent in this extensive dataset. This physics-based surrogate model emphasizes thermodynamically significant quantities, enabling us to accurately predict key process outcomes. The model ingests 21 process parameters, including laser power, scan rate, and position, while providing outputs such as melt pool temperature, melt pool size, and other essential observables. Furthermore, it incorporates uncertainty quantification to provide bounds on these predictions, enhancing reliability and confidence in the results. We then deploy the surrogate model on a new, unseen part and monitor the printing process as validation of the method. Our experimental results demonstrate that the predictions align with actual measurements with high accuracy, confirming the effectiveness of our approach. Furthermore, this methodology not only facilitates real-time predictions but also operates at process-relevant speeds, establishing a basis for implementing feedback control in LP-DED.

Digital twins↗
DOE OSTI2023

Emergency department overcrowding and its associated factors at HARME medical emergency center in Eastern Ethiopia

Introduction: Emergency department (ED) overcrowding has become a significant concern as it can lead to compromised patient care in emergency settings. Various tools have been used to evaluate overcrowding in ED. However, there is a lack of data regarding this issue in resource-limited countries, including Ethiopia. This study aimed to validate NEDOCS, assess level of ED overcrowding and identify associated factors at HARME Medical Emergency Center, located in Hiwot Fana Comprehensive Specialized Hospital, Harar, Ethiopia. Methods: A cross-sectional study was conducted at the HARME Medical Emergency Center, Hiwot Fana Comprehensive Specialized Hospital, involving a total of 899 patients during 120 sampling intervals. The area under the receiver operating characteristic curves (AUC) was calculated to evaluate the agreement between objective and subjective assessments of ED overcrowding. A multivariable logistic regression analysis was employed to identify factors associated with ED overcrowding and statistically significant association was declared using 95% confidence level and a p-value < 0.05. Results: The interrater agreement showed a strong correlation with a Cohen's kappa (κ) of 0.80. The National Emergency Department Overcrowding Study Score demonstrated a strong association with subjective assessments from residents and case team nurses, with an AUC of 0.81 and 0.79, respectively. According to residents' perceptions, ED were considered overcrowded 65.8% of the time. Factors significantly associated with ED overcrowding included waiting time for triage (AOR: 2.24; 95% CI: 1.54–3.27), working time (AOR: 2.23; 95% CI: 1.52–3.26), length of stay (AOR: 2.40; 95% CI: 1.27–4.54), saturation level (AOR: 2.35; 95% CI: 1.31–4.20), chronic illness (AOR: 2.19; 95% CI: 1.37–3.53), and abnormal pulse rate (AOR: 1.52; 95% CI: 1.06–2.16). Conclusion: The study revealed that ED were overcrowded approximately two-thirds of the time.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Using the red chromatic coordinate to characterize the phenology of forest canopy photosynthesis

Vegetation phenology has received increasing attention in climate change research. Near-surface sensing using digital repeat photography has proven to be useful for ecosystem-scale monitoring of vegetation phenology. However, our understanding of the link between phenological metrics derived from digital repeat photography and the phenology of forest canopy photosynthesis is still incomplete, especially for evergreen plant species. Using 49 site-years of digital images from the PhenoCam Network from eight evergreen needleleaf forest (ENF) and six deciduous broadleaf forest (DBF) sites in North America, we explored the potential of the green chromatic (GCC) and red chromatic coordinates (RCC) in tracking forest canopy photosynthesis by comparing camera-derived start- and end-of-growing season (SOS and EOS, respectively) with corresponding estimates derived from eddy covariance-derived daily gross primary productivity (GPP). We found that for DBF sites, both GCC and RCC performed comparable in capturing SOS and EOS. However, similar to earlier studies, GCC had limited potential in capturing GPP-based SOS or EOS for ENF sites. In contrast, we found RCC was a better predictor of both GPP-based SOS and EOS for ENF sites. Environmental and ecological explanations were both provided that phenological transitions derived from RCC were highly correlated with spring and autumn meteorological conditions, as well as having overall higher correlations with phenology based on LAI, a critical variable for describing canopy development. Our results demonstrate that RCC is an underappreciated metric for tracking vegetation phenology, especially for ENF sites where GCC failed to provide reliable estimates for GPP-based SOS or EOS. Our results improve confidence in using digital repeat photography to characterize the phenology of canopy photosynthesis across forest types.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

The application of flow cytometry for kelp meiospore isolation

The goal of this study was to evaluate flow-cytometric techniques for isolating individual kelp meiospores into 96-welled plates. Previously reported low success rates for sorting tens of thousands of meiospores at a time have been improved by technological developments, specifically, the low nozzle pressure of the commercially available, JSAN (Bay Bioscience Co. Ltd) instrument. We monitored growth and gametophyte development post-sorting for 10 months. Our data demonstrate that successful kelp meiospore isolations of up to 76% viability can be achieved with flow-cytometry. This method can save time as compared to traditional, manual isolations using pipettes and improves confidence that self-fertilized individuals will not contaminate specific crosses of resulting gametophytes. Our results highlight a new application for the flow cytometer to produce clonal kelp gametophytes with direct applications for germplasm and culture collection development.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Autonomous monitoring of algal biomass: Success stories and lessons learned from long-term field deployment

Autonomous, high-frequency monitoring of outdoor algal ponds is needed to quantify biomass productivity and detect culture decline in environments prone to contamination, grazers, and variable operating conditions. We report successes and lessons learned in translating a laboratory spectroradiometric monitoring approach to a multi-year autonomous field deployment at the Arizona Center for Algae Technology and Innovation (AzCATI). The system measures spectrally resolved pond reflectance by ratioing upwelling radiance from each raceway to simultaneous downwelling sky irradiance using fiber-coupled spectrometers. A physics-based reflectance model (ASHARP) is fit to each spectrum pair to estimate optical parameters, including a biomass-proxy coefficient (C a ) which enables near-real-time tracking of biomass accumulation and culture state at 2–5 min intervals. From May 2022 through September 2025 the platform operated continuously while scaling from two to six raceway ponds. Several strains of algae were monitored successfully, including the high productivity Tetraselmis striata and Picochlorum celeri. Transitioning data acquisition from a Windows laptop to a Raspberry Pi improved uptime from 57% (2022) to ~89% (2024–2025) and enabled routine real-time analysis. Further, we converted relative biomass estimates to absolute ash-free dry weight (AFDW) using experimentally-derived calibrations, providing field-relevant biomass predictions with conservative confidence bounds. These results demonstrate the feasibility of long-term, autonomous optical monitoring for well-mixed open-raceway algal cultivation and provide practical guidance for reliable field operation and scaling.

Katinas, Christopher Michael [Sandia National Labo↗
DOE OSTI2021

Making sense of uncertain nuclear data

At its core, and in an imperfect world, the scientific method relies heavily on the concept of uncertain knowledge. Understanding the nature and magnitude of our lack of knowledge on nuclear data has important ramifications on a wide range of nuclear technologies that encompass defense, energy, medicine, astrophysics, among others. Over the span of a long and fruitful carrier, Dr. Massimo Salvatores has made significant contributions to the fields of nuclear reactor physics, nuclear data, and uncertainty quantification and propagation. Advances in machine learning techniques and computational capabilities are enabling a new level of confidence in our ability to predict key nuclear metrics as well as their associated uncertainties across many applications. Here this short article reviews some of those concepts with an eye towards developing a new paradigm to provide nuclear data libraries of benefit to various applications.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Fuel cycle depletion validation and code-to-code verification studies for High Flux Isotope Reactor highly and low-enriched uranium fuel designs

Here, this paper documents fuel cycle depletion validation and code-to-code verification studies for the High Flux Isotope Reactor (HFIR) highly enriched uranium (HEU) and proposed low-enriched uranium (LEU) fuel designs. In support of HFIR’s world-leading performance, transport and depletion simulations are performed to ensure safe operations, design and qualify irradiation experiments, enhance core components and irradiation facilities, and design and characterize LEU fuel designs. Identifying well-validated, computationally efficient codes is required for the success of these efforts. The HFIR Controller, Shift, and VESTA codes were deployed to simulate HEU uranium–oxide dispersion fuel cycles at 85, 95, and 100 MW operations, as well as LEU fuel cycles operating at 95 MW with uranium–silicide dispersion and uranium–molybdenum monolithic alloy fuel forms. Excellent agreement between the codes and with experimentally obtained 235 U enrichment distributions provides increased confidence in the ability of these codes to model and simulate HFIR’s unique core design.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Predicting critical heat flux with uncertainty quantification and domain generalization using conditional variational autoencoders and deep neural networks

Deep generative models (DGMs) can generate synthetic data samples that closely resemble the original dataset, addressing data scarcity. In this work, we developed a conditional variational autoencoder (CVAE) to augment critical heat flux (CHF) data used for the 2006 Groeneveld lookup table. To compare with traditional methods, a fine-tuned deep neural network (DNN) regression model was evaluated on the same dataset. Both models achieved small mean absolute relative errors, with the CVAE showing more favorable results. Uncertainty quantification (UQ) was performed using repeated CVAE sampling and DNN ensembling. The DNN ensemble improved performance over the baseline, while the CVAE maintained consistent results with less variability and higher confidence. Both models achieved small errors inside and outside the training domain, with slightly larger errors outside. Altogether, the CVAE performed better than the DNN in predicting CHF and exhibited better uncertainty behavior.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Uncertainty quantification of a physics-informed model based on sparse identification of a Thermal Energy Distribution System

Integrated energy systems (IES)s are crucial for enhancing the economy and efficiency of power generation sources (e.g., nuclear energy) necessary to unleash American energy dominance. These systems can be integrated with thermal energy storage (TES) and intermittent renewable energies to optimize overall energy use, peak-load regulation, and demand-side responses. However, the stabilization of energy generation, transport, and utilization introduces operational complexities that exceed the challenges of managing each sub-component individually. Currently, though IESs rely on human operators for efficiency and stability, reducing human error risk and enhancing performance through automation is highly desirable. Recent advances at Idaho National Laboratory have demonstrated successful control of the Thermal Energy Distributed System (TEDS). However, the automatic control system depends on a deterministic Sparse Identification of Nonlinear Dynamics with Control (SINDyC) model, which are trained based on simulation data from physics-based simulations. Because of uncertainties in physics-based simulation, SINDyC model results in large discrepancies against experimental data and cannot be reliably used in automatic control. In this paper, we present an innovative approach to address these discrepancies by quantifying uncertainties and developing a more robust model. We first generated trajectories by using first-principles physics codes to encapsulate the experiment. Next, we trained thousands of models by randomly sampling these trajectories. We then collapsed all those models into one probabilistic SINDyC by fitting a multivariate Gaussian distribution onto the resulting coefficient’s distribution. Despite its simplicity, our approach successfully produced 95% confidence intervals that captured the experimental trajectories. It even did so with a higher probability and better U-pooling score across six of the seven relevant quantities of interest (QoIs), as compared to other classical approaches. In conclusion, ongoing research is focusing on generating new experimental trajectories to validate this approach, and on employing Bayesian calibration to refine parametric uncertainties and guide future model development efforts.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Short-term apartment-level load forecasting using a modified neural network with selected auto-regressive features

Residential electricity load profiles and their diversity have become increasingly important to realize the benefits of Smart or Transactive Energy Networks (TENs). An important element of TENs will be practical, accurate, and implementable residential load forecasting techniques. While there have been many approaches to short-term load forecasting, few have included forecasting for individual households, partly because the high volatility and idiosyncrasies present in individual household load data can pose significant challenges. In this study, we develop a Convolutional Long Short-Term Memory-based neural network with Selected Autoregressive Features (termed a CLSAF model) to improve short-term household electricity load forecasting accuracy by employing three strategies: autoregressive features selection, exogenous features selection, and a “default” state to avoid overfitting at times of high load volatility. We include aggregations of apartments to floor and building level, because utilities may favor transactive approaches that rely on aggregator models, e.g., a cluster of consumers as opposed to an individual. We demonstrate that the CLSAF model, by virtue of its enhanced feature representation and modest computational resources, can accomplish load forecasting in a multi-family residential building across three spatial granularities (individual apartment/household, floor, and building levels), with an accuracy improvement of up to 25% compared to a persistence model. We propose a data screening technique to characterize time-series electricity-load data. This technique is suitable for integration into a TEN ecosystem and allows one to estimate confidence levels of the load forecasts to optimize computational resources and the risks associated with uncertain forecasts.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Stochastic Optimization and Uncertainty Quantification of Natrium-based Nuclear-Renewable Energy Systems for Flexible Power Applications in Deregulated Markets

Rapid integration of variable renewable energy sources (VRES) has made modeling and stochastic optimization of hybrid energy systems crucial for studying their long-term performance and viability. However, most studies have focused on just historical data, which may be unreliable for capturing short-term fluctuations, rare events, and long-term patterns of energy demand, price, and the variability of renewable energy sources. For this study, optimal synthetic time series models were developed using Wasserstein distance. The models were validated by comparing the key statistical measures against those of the historical data. They were then used to optimize the integrated Natrium-style advanced energy systems and their long-term (30 years) economics. The stochastic model performs bi-level optimization to find the optimal sizes for the balance of plant and thermal energy storage, while also optimizing energy dispatch to achieve the maximum net present value. In studies of two deregulated markets (California ISO and the Electric Reliability Council of Texas), the integrated Natrium-style system performed better in CAISO than in ERCOT, given higher and more consistent electricity prices during peak-demand periods. The potentially enlarged cost associated with the variable operation and maintenance of the TES system also plays a significant role in driving the system sizing, thus its impacts on the system are investigated in detail through comparison against a baseline case. The study also finds that the bi-level optimization results based on stochastic gradient descent closely match the grid search results. The uncertainty quantification of the stochastic signals provides further NPV-related insights and probability distributions for the case studies. The normal standard error of the mean of NPV for the case with and without TES VOM for CAISO were found to be 7.73M (plus-minus sign) 1.09M USD and 104.99M (plus-minus sign) 1.25M USD, respectively based on a 95% confidence. Given the relatively small NPV variance based on 150 samples, the analysis affords the most robust possible prediction of the techno-economic performance of the integrated Natrium-style energy systems.

25 ENERGY STORAGE↗
DOE OSTI2025

Physics-based hybrid machine learning for critical heat flux prediction with uncertainty quantification

Critical heat flux (CHF) is a key quantity in nuclear system modeling due to its impact on heat transfer, safety margins, and reactor performance. This study develops and validates an uncertainty-aware hybrid modeling approach that combines machine learning with physics-based models to predict CHF in cases of dryout. The Biasi and Bowring empirical correlations were paired with three ML uncertainty quantification (UQ) techniques: deep neural network (DNN) ensembles, Bayesian neural networks (BNNs), and deep Gaussian processes (DGPs). A pure ML model without a base model was evaluated for comparison. Model performance was assessed under plentiful (7,350 points) and limited (9 points) training data scenarios using parity, uncertainty distributions, and calibration curves. Results show that the Biasi hybrid DNN ensemble achieved the best overall performance, with a mean absolute relative error of 1.846%, and well-calibrated uncertainty estimates. The BNN-based hybrids showed slightly higher error (2.14%) but superior uncertainty calibration. DGP models underperformed, with over 6% error and poor uncertainty calibration. All hybrid models outperformed pure machine learning configurations, demonstrating resistance against data scarcity. These findings indicate that hybrid modeling significantly improves predictive accuracy, interpretability, and resilience to data scarcity. The integration of uncertainty awareness provides actionable confidence in CHF predictions, which is vital for safety-critical decisions in nuclear applications. This hybrid approach offers a viable pathway for deploying ML models in reactor analysis tools while preserving domain knowledge and physical consistency.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Discrimination of phosgene from cyanogen chloride in recovered chemical munitions using tagged neutron interrogation with high-resolution gamma-ray spectroscopy

The Associated Particle technique has recently been utilized with a germanium-based high-resolution $\gamma$-ray spectrometer and assessed for its capability to improve field identification of recovered chemical warfare (CW) materiel through prompt gamma-ray neutron activation analysis (PGNAA) measurements. An accurate chemical identification is necessary for the safe handling, destruction, and disposal of such hazardous items. A particularly challenging pair of CW agents commonly found in recovered munitions are phosgene (CG) and cyanogen chloride (CK), which have two of three elements in common, i.e. chlorine and carbon, but differ in the third being either oxygen or nitrogen. The detection of both latter elements is complicated by high oxygen concentration in the field environment which interferes with the small signal produced from the chemical agents. The matter is further complicated by the precautionary field practice of overpacking recovered munitions with vermiculite in larger steel multiple round containers (MRCs), which places additional oxygen-rich material around the munition while further attenuating an already weak signal emitted from the munition center. Here, this work reports quantitative results from realistic field measurements of CG and CK simulants in mock 4.2-inch mortar rounds overpacked with vermiculite in a large MRC. Results obtained with the AP technique are compared to those obtained with the traditional PGNAA approach for both overpacked- and bare-munition measurements. The AP technique is shown to provide a much more confident discrimination between the two chemicals, particularly for the more challenging field-relevant overpacked measurements, where a significant gain in sensitivity to all the key elements chlorine, carbon, nitrogen and oxygen is achieved.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Testing claims of the GW170817 binary neutron star inspiral affecting β-decay rates

On August 17, 2017, the first gravitational wave signal from a binary neutron star inspiral (GW170817) was detected by Advanced LIGO and Advanced VIRGO. Here we present radioactive β-decay rates of three independent sources 44 Ti, 60 Co and 137 Cs, monitored during the same period by a precision experiment designed to investigate the decay of long-lived radioactive sources. We do not find any significant correlations between decay rates in a 5 h time interval following the GW170817 observation. This contradicts a previous claim published in this journal of an observed 2.5σ Pearson Correlation between fluctuations in the number of observed decays from two β-decaying isotopes ( 32 Si and 36 Cl) in the same time interval. By correcting for the choice of an arbitrary time interval, we find no evidence of a correlation above 1.5σ confidence. In addition, we argue that such analyses on correlations in arbitrary time intervals should always correct for the so-called Look-Elsewhere effect by quoting the global significance.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Cosmic antihelium-3 nuclei sensitivity of the GAPS experiment

The General Antiparticle Spectrometer (GAPS) is an Antarctic balloon experiment designed for low-energy (0.1-0.3 GeV/n) cosmic antinuclei as signatures of dark matter annihilation or decay. GAPS is optimized to detect low-energy antideuterons, as well as to provide unprecedented sensitivity to low-energy antiprotons and antihelium nuclei. The novel GAPS antiparticle detection technique, based on the formation, decay, and annihilation of exotic atoms, provides greater identification power for these low-energy antinuclei than previous magnetic spectrometer experiments. This work reports the sensitivity of GAPS to detect antihelium-3 nuclei, based on full instrument simulation, event reconstruction, and realistic atmospheric influence simulations. The report of antihelium nuclei candidate events by AMS-02 has generated considerable interest in antihelium nuclei as probes of dark matter and other beyond the Standard Model theories. GAPS is in a unique position to detect or set upper limits on the cosmic antihelium nuclei flux in an energy range that is essentially free of astrophysical background. In three 35-day long-duration balloon flights, GAPS will be sensitive to an antihelium flux on the level of $1.3^{+4.5}_{-1.2} $ ∙ 10 -6 m -2 sr -1 s -1 (GeV/n) -1 (95% confidence level) in the energy range of 0.11-0.3 GeV/n, opening a new window on rare cosmic physics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗