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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Observation of an Excess of Dicharmonium Events in the Four-Muon Final State with the ATLAS Detector

A search is made for potential $cc\bar{c}\bar{c}$ tetraquarks decaying into a pair of charmonium states in the four muon final state using proton-proton collision data at $\sqrt{s}$=13 TeV, corresponding to an integrated luminosity of 140 fb -1 recorded by the ATLAS experiment at LHC. Two decay channels, $J/ψ+J/ψ→4μ$ and $J/ψ+ψ(2S)→4μ$, are studied. Backgrounds are estimated based on a hybrid approach involving Monte Carlo simulations and data-driven methods. Statistically significant excesses with respect to backgrounds dominated by the single parton scattering are seen in the di-J/ψ channel consistent with a narrow resonance at 6.9 GeV and a broader structure at lower mass. A statistically significant excess is also seen in the $J/ψ+ψ$(2S) channel. The fitted masses and decay widths of the structures are reported.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Policy-Driven Sustainable Saline Drainage Disposal and Forage Production in the Western San Joaquin Valley of California

Environmental policies to address water quality impairments in the San Joaquin River of California have focused on the reduction of salinity and selenium-contaminated subsurface agricultural drainage loads from westside sources. On 31 December 2019, all of the agricultural drainage from a 44,000 ha subarea on the western side of the San Joaquin River basin was curtailed. This policy requires the on-site disposal of all of the agricultural drainage water in perpetuity, except during flooding events, when emergency drainage to the River is sanctioned. The reuse of this saline agricultural drainage water to irrigate forage crops, such as ‘Jose’ tall wheatgrass and alfalfa, in a 2428 ha reuse facility provides an economic return on this pollutant disposal option. Irrigation with brackish water requires careful management to prevent salt accumulation in the crop root zone, which can impact forage yields. The objective of this study was to optimize the sustainability of this reuse facility by maximizing the evaporation potential while achieving cost recovery. This was achieved by assessing the spatial and temporal distribution of the root zone salinity in selected fields of ‘Jose’ tall wheatgrass and alfalfa in the drainage reuse facility, some of which have been irrigated with brackish subsurface drainage water for over fifteen years. Electromagnetic soil surveys using an EM-38 instrument were used to measure the spatial variability of the salinity in the soil profile. The tall wheatgrass fields were irrigated with higher salinity water (1.2–9.3 dS m -1 ) compared to the fields of alfalfa (0.5–6.5 dS m -1 ). Correspondingly, the soil salinity in the tall wheatgrass fields was higher (12.5 dS m -1 –19.3 dS m -1 ) compared to the alfalfa fields (8.97 dS mm -1 –14.4 dS mm -1 ) for the years 2016 and 2017. Better leaching of salts was observed in the fields with a subsurface drainage system installed (13–1 and 13–2). The depth-averaged root zone salinity data sets are being used for the calibration of the transient hydro-salinity computer model CSUID-ID (a one-dimensional version of the Colorado State University Irrigation Drainage Model). This user-friendly decision support tool currently provides a useful framework for the data collection needed to make credible, field-scale salinity budgets. In time, it will provide guidance for appropriate leaching requirements and potential blending decisions for sustainable forage production. This paper shows the tie between environmental drainage policy and the role of local governance in the development of sustainable irrigation practices, and how well-directed collaborative field research can guide future resource management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Next-Generation Intensity–Duration–Frequency Curves to Reduce Errors in Peak Flood Design

Precipitation-based intensity–duration–frequency (PREC-IDF) curves are a standard tool used to derive design floods for hydraulic infrastructure worldwide. In snow-dominated regions where a large percentage of flood events are caused by snowmelt and rain-on-snow events, the PREC-IDF design approach can lead to substantial underestimation or overestimation of design floods and associated infrastructure. In this study, next-generation IDF (NG-IDF) curves, which characterize the actual water reaching the land surface, are introduced into the design process to improve hydrologic design. The authors compared peak design flood estimates from the National Resource Conservation Service TR-55 hydrologic model driven by NG-IDF and PREC-IDF curves at 399 Snowpack Telemetry (SNOTEL) stations across the western United States, all of which had at least 30 years of high-quality records. They found that about 70% of the stations in the western United States showed the potential for underdesign, for which the PREC-IDF curves underestimated peak design floods by as much as 324%. These results demonstrated the need to update the use of PREC-IDF curves to the use of NG-IDF curves for hydrologic design in snow-dominated regions.

Yan, Hongxiang↗
DOE OSTI2025

Multiphysics Modeling of Microreactors with NEAMS codes, and Validation Based on KRUSTY Reactivity Insertion

The NEAMS Multiphysics Applications team continues to assess code usability and functionality for microreactor design and safety analyses, while demonstrating that NEAMS tools capture both steady-state and transient behavior across distinct microreactor concepts. In FY2025, the team advanced full-core, high-fidelity, multiphysics models that solve more complex problems and strengthen verification/validation for several microreactor systems: heat-pipe microreactor (HPMR), gas-cooled microreactor (GCMR), and the KRUSTY experiment. These models employ the MOOSE MultiApp/Transfers architecture with Griffin for neutronics, BISON for heat conduction/thermomechanics, Sockeye for heat pipes, SAM/THM for coolant channels and loops, and SWIFT for hydride behavior, with meshes generated via the MOOSE Reactor Module. The graphite models available in the Grizzly code were also investigated for future analyses. For the HPMR, a Na-HPMR variant was constructed to align with recently validated heat-pipe experiments and Sockeye’s LCVF capability, enabling mechanistic heat-pipe transients and startup modeling. The Na-HPMR will serve as the primary model for HPMR investigations in upcoming tasks. The load-following and single heat-pipe failure scenarios (Griffin/BISON/Sockeye), which were previously modeled for the K-HPMR, were replicated for the Na-HPMR, showing strong negative temperature feedback and highly localized thermal effects, respectively, while the startup case captured vapor-front progression and heat-removal activation. Solid mechanics was added to the previously built K-HPMR full-core model in BISON, showing minimal impact on steady-state reactivity yet enabling stress-field predictions that prepare the path for full-core TRISO performance analyses. For the GCMR, automated steady-state and four transient scenarios were executed using Griffin/BISON/SAM/SWIFT. Results confirm robust inherent safety: power collapses promptly in loss-of-cooling events, the inlet-temperature drop settles to a new equilibrium, and a single-channel blockage yields only a ~30 K local fuel-temperature rise with <0.4% power decrease. SWIFT-predicted hydrogen redistribution affects reactivity during both steady-state and transient conditions, underscoring its importance. A Brayton-cycle balance of plant (BOP) model in SAM/THM demonstrated stable startup behavior, and xenon-driven reactivity during load following was analyzed. To improve TRISO-compact temperature fidelity, a fast multiscale Heat Source Decomposition (HSD) treatment was implemented. Against heterogeneous benchmarks, HSD reduces underprediction of kernel temperatures and lowers predicted peak powers in reactivity-insertion transients compared to previous homogenized models. KRUSTY warm-critical validation progressed from FY2024 baselines: the 15Ȼ insertion shows excellent agreement in peak power (~2% high) and temperature trends, and the 30Ȼ case was automated via a feedback controller that maintained power near 3 kW for ~150 s with close agreement to data. The successful modeling of the warm critical tests has laid a strong foundation for simulating more complex nuclear system tests in the years ahead. Throughout FY2025, developer feedback was provided (e.g., MOOSE batch mesh generation, distributed pre-split meshes, Griffin sweeper on displaced meshes), several new models were contributed to the Virtual Test Bed, and an OECD-NEA WPRS multiphysics benchmark based on the HPMR was initiated to enable broader cross-comparison and best-practice development with the nuclear community at large.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2017

Which Bow Shock Theory, Gasdynamic or Magnetohydrodynamic, Better Explains CME Stand-off Distance Ratios from LASCO-C2 Observations ?

It is generally believed that fast coronal mass ejections (CMEs) can generate their associated shocks, which are characterized by faint structures ahead of CMEs in white-light coronagraph images. In this study, we examine whether the observational stand-off distance ratio, defined as the CME stand-off distance divided by its radius, can be explained by bow shock theories. Of 535 SOHO /LASCO CMEs (from 1996 to 2015) with speeds greater than 1000 km s{sup −1} and angular widths wider than 60°, we select 18 limb CMEs with the following conditions: (1) their Alfvénic Mach numbers are greater than one under Mann’s magnetic field and Saito’s density distributions; and (2) the shock structures ahead of the CMEs are well identified. We determine observational CME stand-off distance ratios by using brightness profiles from LASCO-C2 observations. We compare our estimates with theoretical stand-off distance ratios from gasdynamic (GD) and magnetohydrodynamic (MHD) theories. The main results are as follows. Under the GD theory, 39% (7/18) of the CMEs are explained in the acceptable ranges of adiabatic gamma ( γ ) and CME geometry. Under the MHD theory, all the events are well explained when we consider quasi-parallel MHD shocks with γ = 5/3. When we use polarized brightness (pB) measurements for coronal density distributions, we also find similar results: 8% (1/12) under GD theory and 100% (12/12) under MHD theory. Our results demonstrate that the bow shock relationships based on MHD theory are more suitable than those based on GD theory for analyzing CME-driven shock signatures.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1990

Thermal plasma in outer planet magnetospheres

The plasma environments of the outer planets are a study in contrasts. The magnetosphere of Jupiter is dominated by the prodigious plasma output of Io, with losses due to diffusion driven by mass loading. At Saturn, the small icy satellites are the major sources of plasma for the inner magnetosphere. The low mass loading rates there imply that the densities of the plasma tori are limited by dissociative recombination, rather than diffusive transport. At Uranus, the icy satellites are negligible plasma sources compared to the input from the extended neutral hydrogen cloud and the ionosphere. Convection driven by the solar wind penetrates deep into the inner magnetosphere because of the unique orientation of the rotation axis of Uranus. The expected magnetosphere of Neptune is similar to that of Saturn and Jupiter, with Triton, the ring arcs, and the planet as possible plasma sources. The Voyager 2 encounter with Neptune holds out the hope of a passage through a nonterrestrial auroral region, a unique event in planetary exploration.

Belcher, J. W.↗
NASA NTRS2008

MESSENGER Observation of Mercury's Magnetopause: Structure and Dynamics

MESSENGER'S 14 January 2008 encounter with Mercury has provided new observations of the magnetopause of this small magnetosphere, particularly concerning the effect of the direction of the interplanetary magnetic field (IMF) on the structure and dynamics of this boundary. The IMF was northward immediately prior to and following the passage of the MESSENGER spacecraft through Mercury's magnetosphere. However, several-minute episodes of southward IMF were observed in the magnetosheath during the inbound portion of the encounter. Evidence for reconnection at the dayside magnetopause in the form of well-developed flux transfer events (FTEs) was observed in the magnetosheath following some of these southward-B, intervals. The inbound magnetopause crossing seen in the magnetic field measurements is consistent with a transition from the magnetosheath into the plasma sheet. Immediately following MESSENGER'S entry into the magnetosphere, rotational perturbations in the magnetic field similar to those seen at the Earth in association with large-scale plasma sheet vortices driven by Kelvin-Helmholtz waves along the magnetotail boundary at the Earth were observed. The outbound magnetopause occurred during northward IMF B(sub z) and had the characteristics of a tangential discontinuity. These new observations by MESSENGER may be combined and compared with the magnetopause measurements collected by Mariner 10 to derive new understanding of the response of Mercury's magnetopause to IMF direction and its effect on the rate of solar wind energy and mass input to this small magnetosphere.

Slavin, J. A.↗
NASA NTRS2010

Tidal Marsh Outwelling of Dissolved Organic Matter and Resulting Temporal Variability in Coastal Water Optical and Biogeochemical Properties

Coastal wetlands are highly dynamic environments at the land-ocean interface where human activities, short-term physical forcings and intense episodic events result in high biological and chemical variability. Long being recognized as among the most productive ecosystems in the world, tidally-influenced coastal marshes are hot spots of biogeochemical transformation and exchange. High temporal resolution observations that we performed in several marsh-estuarine systems of the Chesapeake Bay revealed significant variability in water optical and biogeochemical characteristics at hourly time scales, associated with tidally-driven hydrology. Water in the tidal creek draining each marsh was sampled every hour during several semi-diurnal tidal cycles using ISCO automated samplers. Measurements showed that water leaving the marsh during ebbing tide was consistently enriched in dissolved organic carbon (DOC), frequently by more than a factor of two, compared to water entering the marsh during flooding tide. Estimates of DOC fluxes showed a net DOC export from the marsh to the estuary during seasons of both low and high biomass of marsh vegetation. Chlorophyll amounts were typically lower in the water draining the marsh, compared to that entering the marsh during flooding tide, suggesting that marshes act as transformers of particulate to dissolved organic matter. Moreover, detailed optical and compositional analyses demonstrated that marshes are important sources of optically and chemically distinctive, relatively complex, high molecular weight, aromatic-rich and highly colored dissolved organic compounds. Compared to adjacent estuarine waters, marsh-exported colored dissolved organic matter (CDOM) was characterized by considerably stronger absorption (more than a factor of three in some cases), larger DOC-specific absorption, lower exponential spectral slope, larger fluorescence signal, lower fluorescence per unit absorbance, and higher fluorescence at visible wavelengths. Observed patterns in water optical and biogeochemical variables were very consistent among different marsh systems and throughout the year, despite continued tidal exchange, implying rapid transformation of marsh DOM in the estuary through both photochemical and microbial processes. These findings illustrate the importance of tidal marsh ecosystems as sources, sinks and/or transformers of biologically important nutrients, carbon and colored dissolved organic compounds, and their influence on short-term biological, optical and biogeochemical variability in coastal waters.

Tzortziou, Maria↗
NASA NTRS2023

Formation of Amino Acids and Carboxylic Acids in Weakly Reducing Planetary Atmospheres by Solar Energetic Particles from the Young Sun

Life most likely started during the Hadean Eon; however, the environmental conditions which contributed to the complexity of its chemistry are poorly known. A better understanding of various environmental conditions, including global (heliospheric) and local (atmospheric, surface, and oceanic), along with the internal dynamic conditions of the early Earth, are required to understand the onset of abiogenesis. Herein, we examine the contributions of galactic cosmic rays (GCRs) and solar energetic particles (SEPs) associated with superflares from the young Sun to the formation of amino acids and carboxylic acids in weakly reduced gas mixtures representing the early Earth’s atmosphere. We also compare the products with those introduced by lightning events and solar ultraviolet light (UV). In a series of laboratory experiments, we detected and characterized the formation of amino acids and carboxylic acids via proton irradiation of a mixture of carbon dioxide, methane, nitrogen, and water in various mixing ratios. These experiments show the detection of amino acids after acid hydrolysis when 0.5% (v/v) of initial methane was introduced to the gas mixture. In the set of experiments with spark discharges (simulation of lightning flashes) performed for the same gas mixture, we found that at least 15% methane was required to detect the formation of amino acids, and no amino acids were detected in experiments via UV irradiation, even when 50% methane was used. Carboxylic acids were formed in non-reducing gas mixtures (0% methane) by proton irradiation and spark discharges. Hence, we suggest that GCRs and SEP events from the young Sun represent the most effective energy sources for the prebiotic formation of biologically important organic compounds from weakly reducing atmospheres. Since the energy flux of space weather, which generated frequent SEPs from the young Sun in the first 600 million years after the birth of the solar system, was expected to be much greater than that of GCRs, we conclude that SEP-driven energetic protons are the most promising energy sources for the prebiotic production of bioorganic compounds in the atmosphere of the Hadean Earth.

Kensei Kobayashi↗
DOE OSTI2026

Improving Detector Systematic Uncertainties Through Data-Driven Machine Learning

Detector simulation in liquid argon time projection chambers (LArTPCs) is a constant challenge. In particular the modeling of electrons response on wires is highly nontrivial. However, new machine learning techniques exist which can be leveraged to ameliorate these concerns. We present a novel methodology to attempt to learn from cosmic muon data in the ICARUS detector how reconstructed wire signals are influenced by features of the hits such as location, particle direction, angle relative to the wire plane, etc. A model can then be generated which can apply the learned mapping to Monte Carlo events to create a more data-like simulation sample. By creating such a sample we expect to reduce the systematic uncertainties at ICARUS due to our detector modeling.

Hausner, Harry [Fermilab] (ORCID:0000000188932280)↗
DOE OSTI2026

Improving Detector Systematic Uncertainties Through Data-Driven Machine Learning

Detector simulation in liquid argon time projection chambers (LArTPCs) is a constant challenge. In particular the modeling of electrons response on wires is highly nontrivial. However, new machine learning techniques exist which can be leveraged to ameliorate these concerns. We present a novel methodology to attempt to learn from cosmic muon data in the ICARUS detector how reconstructed wire signals are influenced by features of the hits such as location, particle direction, angle relative to the wire plane, etc. A model can then be generated which can apply the learned mapping to Monte Carlo events to create a more data-like simulation sample. By creating such a sample we expect to reduce the systematic uncertainties at ICARUS due to our detector modeling.

Hausner, Harry [Fermilab] (ORCID:0000000188932280)↗
DOE OSTI2022

A reinforcement learning approach to long-horizon operations, health, and maintenance supervisory control of advanced energy systems

In this work, we develop a Reinforcement Learning (RL) approach to the supervisory control problem for advanced energy systems, such as novel nuclear reactors and other demand-driven, mission-critical, and component-health-sensitive energy plants. The inclusive problem landscape considered captures the stochastic confluence of plant performance, component health evolution, power demand from the grid, diverse maintenance actions, and operator-defined goals and constraints, all considered over meaningfully long-enough reasoning horizons. Key aspects of the proposed approach are a receding horizon control-inspired technique dictating time- or event-triggered supervisory policy (re-)constructions, as well as additional capability-enabling contributions such as timescale compression, to handle long reasoning horizons and uncertainty in parts of the problem, and practical yet demonstrably-effective handling of hybrid action spaces with continuous and discrete decision variables. The resulting algorithm consists of a simulation-based RL agent constructing stochastic supervisory control policies over nontrivial action spaces and for long horizons, applying the learned policy to the system for a much shorter interval, and perpetually repeating, to construct the next long-horizon policy. That next policy will only be applied, again, for a short interval, yet originally far-in-time events move progressively closer, their associated uncertainty decreases, and new events and aspects enter the reasoning horizon. The proposed methodology bridges fundamental receding horizon concepts with the unequivocally stronger and more scalable reasoning of contemporary RL. Numerical examples using Soft Actor–Critic Deep RL illustrate the operation and efficacy of the proposed technique for a power plant tasked with health-aware load following missions in a dynamic electricity market landscape.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Estimates of the wavenumber wavelet power spectrum of magnetic fluctuations during magnetic reconnection

Fluctuation analyses of experimental observations generally lack high temporal resolution and are in frequency-space f, contrary to theoretical efforts in wavenumber-space k. This is due to the inherent limits of the Fourier transform, though it is prominent due to the ease of diagnostic implementation. Advances in wavelet-based analysis have provided relief due to its temporal resolution, but in its common use, is still hard to compare to theoretical models. By using the two-point correlation technique in conjunction with large data sets, a wavelet power spectrum in wavenumber-space can be created. Dubbed the wavenumber wavelet power spectrum, this spectrum relates wavenumber to power in time. Further, this analysis technique more closely connects characterizations of experimentally observed fluctuations with other system parameters and theoretical predictions. In this article, we develop the wavenumber wavelet power spectrum using magnetic fluctuations caused by tearing instability driven magnetic reconnection in reproducible, high temperature laboratory plasmas. These dynamic magnetic fluctuations generated in reversed field pinch plasmas are broadband, ranging from the low frequency, 10's of kHz, up to the ion gyroradii frequencies, 100's of kHz. The dominant fluctuations have poloidal and toroidal mode numbers (m,n)=(1,6−10) and can grow to 2%–3% of the mean magnetic field. During these reconnection events, ions, and electrons are energized, magnetic fluctuation amplitudes increase, plasma flow is halted, and the toroidal magnetic flux increases, all on a semi-periodic basis. The newly developed spectrum provides better temporal resolution of spectrum characteristics to correlate with these particle energization phenomena.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Improving neutrino oscillation measurements through event classification

Precise neutrino energy reconstruction is essential for next-generation long-baseline oscillation experiments, yet current methods remain limited by large uncertainties in neutrino-nucleus interaction modeling. Even so, it is well established that different interaction channels produce systematically varying amounts of missing energy and therefore yield different reconstruction performance–information that standard calorimetric approaches do not exploit. We introduce a strategy that incorporates this structure by classifying events according to their underlying interaction type prior to energy reconstruction. Using supervised machine-learning techniques trained on labeled generator events, we leverage intrinsic kinematic differences among quasielastic scattering, meson-exchange current, resonance production, and deep-inelastic scattering processes. A cross-generator testing framework demonstrates that this classification approach is robust to microphysics mismodeling and, when applied to a simulated DUNE 𝜈 𝜇 disappearance analysis, yields improved accuracy and sensitivity at the 10%–20% level. These results highlight a practical path toward reducing reconstruction-driven systematics in future oscillation measurements.

Ellis, Sebastian A. R. [King's College, London (Un↗
NASA NTRS2023

Evaluating Greenland Surface-Mass-Balance and Firn-Densification Data Using ICESat-2 Altimetry

Surface-mass-balance (SMB) and firn-densification (FD) models are widely used in altimetry studies as a tool to separate atmospheric-driven from ice-dynamics-driven ice-sheet mass changes and to partition observed volume changes into ice-mass changes and firn-air-content changes. Until now, SMB models have been principally validated based on comparison with ice core and weather station data or comparison with widely separated flight radar-survey flight lines. Firn-densification models have been primarily validated based on their ability to match net densification over decades, as recorded in firn cores, and the short-term time-dependent component of densification has rarely been evaluated at all. The advent of systematic ice-sheet-wide repeated ice-surface-height measurements from ICESat-2 (the Ice Cloud, and land Elevation Satellite, 2) allows us to measure the net surface-height change of the Greenland ice sheet at quarterly resolution and compare the measured surface-height differences directly with those predicted by three FD–SMB models: MARv3.5.11 (Modèle Atmosphérique Régional version 3.5.11) and GSFCv1.1 and GSFCv1.2 (the Goddard Space Flight Center FD–SMB models version 1.1 and 1.2). By segregating the data by season and elevation, and based on the timing and magnitude of modelled processes in areas where we expect minimal ice-dynamics-driven height changes, we investigate the models' accuracy in predicting atmospherically driven height changes. We find that while all three models do well in predicting the large seasonal changes in the low-elevation parts of the ice sheet where melt rates are highest, two of the models (MARv3.5.11 and GSFCv1.1) systematically overpredict, by around a factor of 2, the magnitude of height changes in the high-elevation parts of the ice sheet, particularly those associated with melt events. This overprediction seems to be associated with the melt sensitivity of the models in the high-elevation part of the ice sheet. The third model, GSFCv1.2, which has an updated high-elevation melt parameterization, avoids this overprediction.

Benjamin E. Smith↗
DOE OSTI2015

GRB 130427A AND SN 2013cq: A MULTI-WAVELENGTH ANALYSIS OF AN INDUCED GRAVITATIONAL COLLAPSE EVENT

We performed a data analysis of the observations by the Swift, NuStar, and Fermi satellites in order to probe the induced gravitational collapse (IGC) paradigm for gamma-ray bursts (GRBs) associated with supernovae (SNe) in the terra incognita of GRB 130427A. We compare our data analysis with those in the literature. We have verified that GRB 130427A conforms to the IGC paradigm by examining the power law behavior of the luminosity in the early 10{sup 4} s of the XRT observations. This has led to the identification of the four different episodes of the binary driven hypernovae (BdHNe) and to the prediction, on 2013 May 2, of the occurrence of SN 2013cq, which was also observed in the optical band on 2013 May 13. The exceptional quality of the data has allowed the identification of novel features in Episode 3 including: (1) the confirmation and the extension of the existence of the recently discovered nested structure in the late X-ray luminosity in GRB 130427A, as well as the identification of a spiky structure at 10{sup 2} s in the cosmological rest-frame of the source; (2) a power law emission of the GeV luminosity light curve and its onset at the end of Episode 2; and (3) different Lorentz Γ factors for the emitting regions of the X-ray and GeV emissions in this Episode 3. These results make it possible to test the details of the physical and astrophysical regimes at work in the BdHNe: (1) a newly born neutron star and the supernova ejecta, originating in Episode 1; (2) a newly formed black hole originating in Episode 2; and (3) the possible interaction among these components, observable in the standard features of Episode 3.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

A data-driven method to estimate the antiproton background in Mu2e

The Mu2e experiment will search for the CLFV process of neutrinoless, coherent conversion of muon to electron in the field of an Al nucleus. One of the expected backgrounds is antiprotons produced by the proton beam at the Production Target and annihilating in the Stopping Target to produce signal-like electrons. Although not a dominant background, it has a large uncertainty and cannot be suppressed by the timing cuts used to reduce the prompt background. However, at Mu2e energies, $p\bar{p}$ annihilation is the only source of events with multiple, simultaneous particles coming from the Stopping Target. We utilized this unique feature and developed a novel approach to reconstruct multi-track events and estimate the antiproton background.

Chithirasreemadam, Namitha [Pisa U.; INFN, Pisa]↗
DOE OSTI2022

A graph signal processing‐based multiple model Kalman filter ( GSP‐MMKF ) tool for predictive analytics: An air separation unit process application

Abstract The industrial Air Separations Unit (ASU) is a complicated and tightly operated process. The use of dynamic process analytics is also a key element of safe and economic operation of these processes, with increasing focus on predictive analytics to take preemptive actions. With the availability of real‐time data from hundreds of sensors, the data analysis process should also consider the topology of the data, as seen in sensor networks. In this paper, a novel tool is presented that considers the complex connectivity patterns in the sensor network and uses local adaptive disturbance estimations to predict global network‐scale trends. The paper introduces the emerging field of Graph Signal Processing (GSP) and presents a rigorous derivation of the tool starting from the extraction of the sensor‐network (in a graph theoretical sense) from the data. This network, which is in the form of a matrix, is then used to derive a Kalman‐filter type of state‐space model driven by input disturbances. Multiple disturbance models (e.g., step, ramp, periodic) are included to allow the model to have different kinds of disturbance propagation. Each graph node (representing the sensors used) dynamically adapts to the most recent detected disturbance individually. These estimated disturbances are propagated to the global network using the graph. Modifications to ensure stability are also discussed. The fidelity of the tool is tested on certain downtime events and the paper concludes by discussing the advantages of the method and planned future improvements.

Ghosh, Sambit↗