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This presentation is required for the DHS CWMD T&E week event. Submission to DHS is July 21. Material in this presentation was captured from publicly released information such as NNSS external website, Fact sheets, SDRD annual report.
The objective of the cyclotron radiation emission spectroscopy (CRES) technology is to build precise particle energy spectra. This is achieved by identifying the start frequencies of charged particle trajectories which, when exposed to an external magnetic field, leave semi-linear profiles (called tracks) in the time–frequency plane. Due to the need for excellent instrumental energy resolution in application, highly efficient and accurate track reconstruction methods are desired. Deep learning convolutional neural networks (CNNs) - particularly suited to deal with information-sparse data and which offer precise foreground localization—may be utilized to extract track properties from measured CRES signals (called events) with relative computational ease. In this work, we develop a novel machine learning based model which operates a CNN and a support vector machine in tandem to perform this reconstruction. A primary application of our method is shown on simulated CRES signals which mimic those of the Project 8 experiment—a novel effort to extract the unknown absolute neutrino mass value from a precise measurement of tritium β - -decay energy spectrum. When compared to a point-clustering based technique used as a baseline, we show a relative gain of 24.1% in event reconstruction efficiency and comparable performance in accuracy of track parameter reconstruction.
Context. GRB 221009A, initially detected as an X-ray transient by Swift, was later revealed to have triggered the Fermi satellite about an hour earlier, marking it as a post-peak observation of the event’s emission. This GRB distinguished itself as the brightest ever recorded, presenting an unparalleled opportunity to probe the complexities of GRB physics. The unprecedented brightness, however, challenged observation efforts, as it led to the saturation of several high-energy instruments.Aims. Our study seeks to investigate the nature of the INTEGRAL-detected GRB 221009A and elucidate the environmental conditions conducive to these exceptionally powerful bursts. Moreover, we aim to understand the fundamental physics illuminated by the detection of teraelectronvolt (TeV) photons emitted by GRB 221009A.Methods. We conducted detailed analyses of early photometric and spectroscopic observations that span from the Fermi trigger through to the initial days following the prompt emission phase in order to characterize GRB 221009A’s afterglow, and we complemented these analyses with a comparative study.Results. Our findings from analyzing INTEGRAL data confirm GRB 221009A as the most energetic event observed to date. Early optical observations during the prompt phase negate the presence of bright optical emissions with internal or external shock origins. Spectroscopic analyses enabled us to measure GRB 221009A’s distance and line-of-sight properties. The afterglow’s temporal and spectral analysis suggests prolonged activity of the central engine and a transition in the circumburst medium’s density. Finally, we discuss the implications for fundamental physics of detecting photons as energetic as 18 TeV from GRB 221009A.Conclusions. Early optical observations have proven invaluable for distinguishing between the potential origins of optical emissions in GRB 221009A, underscoring their utility in GRB physics studies. However, the rarity of such data underscores the need for dedicated telescopes capable of synchronous multiwavelength observations. Additionally, our analysis suggests that the host galaxies of TeV GRBs share commonalities with those of long and short GRBs. Expanding the sample of TeV GRBs could further solidify these findings.Key words: techniques: photometric / techniques: spectroscopic / gamma-ray burst: general / gamma-ray burst: individual: GRB 221009A
The ubiquitous domain wall kinetics under magnetic field or current application describes the dynamic properties in nanostructured magnets. However, when the geometrical size of a nanomagnetic system is constricted to the limiting domain wall length scale, the competing energetics between anisotropy, exchange, and dipolar interactions can cause emergent kinetics due to quasiparticle relaxation, similar to bulk magnets of atomic origin. This paper presents a joint experimental and theoretical study to support this argument: constricted nanomagnets, made of antiferromagnetic and paramagnetic neodymium thin film with honeycomb motif, reveal fast kinetic events at picosecond timescales due to the relaxation of topological quasiparticles that persist to low temperature in the absence of any external stimuli. This discovery is especially important considering the fact that paramagnets or antiferromagnets have no net magnetization. Yet, the kinetics in neodymium nanostructures is quantitatively similar to that found in ferromagnetic counterparts and only varies with the thickness of the specimen. This suggests that a universal, topological quasiparticle-mediated dynamical behavior can be prevalent in nanoscopic magnets, irrespective of the nature of the underlying magnetic material.
The Energy Sciences Network (ESnet) is the high-performance network user facility for the US Department of Energy (DOE) Office of Science (SC) and delivers highly reliable data transport capabilities optimized for the requirements of data-intensive science. In essence, ESnet is the circulatory system that enables the DOE science mission by connecting all of its laboratories and facilities in the US and abroad. ESnet is funded and stewarded by the Advanced Scientific Computing Research (ASCR) program and managed and operated by the Scientific Networking Division at Lawrence Berkeley National Laboratory (LBNL). ESnet is widely regarded as a global leader in the research and education networking community. On May 1, 2021, ESnet and the DOE Office of Energy Efficiency and Renewable Energy (EERE), organized an ESnet requirements review of the ARIES (Advanced Research on Integrated Energy Systems) platform. Preparation for this event included identification of key stakeholders to the process: program and facility management, research groups, technology providers, and a number of external observers. These individuals were asked to prepare formal case study documents in order to build a complete understanding of the current, near-term, and long-term status, expectations, and processes that will support the science going forward.
Physical and budget constraints often result in irregular sampling, which complicates accurate subsurface imaging. Preprocessing approaches, such as missing trace or shot interpolation, are typically employed to enhance seismic data in such cases. Recently, deep learning has been used to address the trace interpolation problem at the expense of large amounts of training data to adequately represent typical seismic events. Nonetheless, most research in this area has focused on trace reconstruction, with little attention having been devoted to shot interpolation. Furthermore, existing methods assume regularly spaced receivers/sources failing in approximating seismic data from real (irregular) surveys. This work presents a novel shot gather interpolation approach which uses a continuous coordinate-based representation of the acquired seismic wavefield parameterized by a neural network. The proposed unsupervised approach, which we call coordinate-based seismic interpolation (CoBSI), enables the prediction of specific seismic characteristics in irregular land surveys without using external data during neural network training. Importantly, experimental results on real and synthetic 3-D data validate the ability of the proposed method to estimate continuous smooth seismic events in the time-space and frequency-wavenumber domains, improving sparsity or low-rank-based interpolation methods.
This feasibility study provides the technical and economic rationale for the Microrred de la Montaña as a viable solution for supplying reliable and resilient electricity to an underserved region in Puerto Rico – a region that is the first to be disconnected from the grid and the last to be restored, enduring months long outages. It begins the process of the development of the microgrid through conceptual design, engineering, detailed electrical design, implementation, and operation. The Microrred de la Montaña is organized under the governance of the Cooperativa Hidroeléctrica de la Montaña (Cooperativa) which has legal status as a cooperative and power provider under the laws of Puerto Rico. The members are the municipalities of Adjuntas, Jayuya, Lares and Utuado with a combined population of 90,000. These municipalities are in central Puerto Rico – a mountainous region that is extremely vulnerable to weather events and excessive, long-duration power outages and are among the poorest and most disadvantaged regions in Puerto Rico. Funding from external sources is necessary to acquire the resources for a resilient power delivery system. Regulatory support is necessary to establish operational relationships with the incumbent power providers. The Microrred de la Montaña is envisioned as a resilient microgrid that manages distributed energy resources – local solar, storage, and hydroelectricity over an upgraded distribution network within the boundaries of four municipalities. This feasibility study provides the results of an extensive techno-economic analysis to form a sound basis for decision-making by community leaders, their constituents, funding and investment agencies, and regulators to realize the benefits that are achievable by fully implementing the design and planning recommended for the Microrred de la Montaña.
Crystal growth is a complex process with far-reaching implications for high-performance materials across various fields. Recent advancements in structural analysis methods such as polyhedral template matching, which allows semiconductor-specific analysis, coupled with simulation technology, have enabled the comprehensive study of crystallization dynamics in semiconductors. However, the exploration of polycrystalline semiconductors created with minimal external intervention of the crystallization processes is relatively uncharted in comparison with metals. In this study, we employ molecular dynamics to simulate the growth of polycrystalline CdTe/CdS with the assumptions of classical mechanics, a Stillinger–Weber potential, an amorphous substrate, and common vapor growth conditions to allow the polycrystalline structures to evolve naturally. Post-simulation, we identify and analyze impactful structures and events, comparing them to theory and experiment to gain insight into various modes of crystallization dynamics. Two research questions guided the study: (1) How realistic are assumption-less simulated polycrystalline semiconductor structures? (2) To what extent can the approach provide insight into crystallization? The simulations, performed with minimal external control, yield polycrystalline structures mirroring experimental findings. The analysis reveals key crystallization insights, such as the role of amorphous atoms in the transition from nucleation to grain growth and the transformative impact of single events, such as dislocations, on crystallization dynamics. The method paves the way for reproducing and analyzing realistic polycrystalline semiconductor structures with minimal simulation assumptions across various growth modes.
In this work, we theoretically study the effect of external deformation on activated structural relaxation and aspects of the nonlinear mechanical response of glassy hard sphere fluids in the context of elastically collective nonlinear Langevin equation theory. This microscopic force-based approach describes activated relaxation as a coupled local–nonlocal event involving caging and longer range collective elasticity, with the latter becoming more important and ultimately dominant with increasing packing fraction under equilibrium conditions. The central new question we address is how this physical picture of activated relaxation, and the relative importance of local caging vs collective elasticity physics, depends on external deformation. Theoretical predictions are presented for deformation-induced enhancement of mobility, the onset of relaxation speed up at remarkably low values of stress, strain, or shear rate, apparent power law thinning of the steady state structural relaxation time and viscosity, a non-vanishing activation barrier in the shear thinning regime, an apparent Herschel–Bulkley form of the rate dependence of the steady state shear stress, exponential growth of different measures of a dynamic yield or flow stress with the packing fraction, and reduced fragility and dynamic heterogeneity under deformation. The results are contrasted with experiments and simulations, and qualitative or better agreement is found. An overarching conclusion is that deformation strongly reduces the importance of longer range collective elastic effects relative to the local caging aspect for most, but not all, physical questions, with deformation-dependent fragility and dynamic heterogeneity phenomena being qualitatively sensitive to collective elasticity. Overall, nonlinear rheology is predicted to be a more local problem than quiescent structural relaxation, albeit with deformation-modified activated processes still important.
The passive safety systems (PSSs) within water-cooled reactors are meticulously engineered to function autonomously, requiring no external power source or manual intervention. They depend exclusively on inherent natural forces and the fundamental principles of reactor physics, such as gravity, natural convection, and phase changes, to manage, alleviate, and avert the release of radioactive materials into the environment during accident scenarios like a loss-of-coolant accident (LOCA). PSSs are already integrated into such operating commercial reactors as the Advanced Pressurized Reactor-1000 MWe (AP1000) and the Water-Water Energetic Reactor-1200 MWe (WWER-1200) are adopted in most of the upcoming small modular reactor (SMR) designs. Examples of water-cooled SMR PSSs are the passive emergency core-cooling system (ECCS), passive containment cooling system (PCCS), and passive decay-heat removal system, the designs of which vary based on reactor system-design requirements. However, understanding the accident-event progression and phases of a LOCA is pivotal for adopting a specific PSS for a new SMR design. This study covers the accident-event progression for direct vessel injection (DVI) small-break loss-of-coolant accident (SB-LOCA), associated physics phenomena, knowledge gaps, and important figures of merit (FOMs) that may need to be evaluated and assessed to validate thermal-hydraulics models with an available experimental dataset to support new SMR design and development.
The PIP-II timing system is planned to be a two-part system consisting of a global timing system (referred to as ACLK) that provides high level, event-based timing for the whole Fermilab accelerator complex while the second part is a RF synchronized clock system unique to the PIP-II Linac itself (referred to as LCLK). The ACLK System will make use of an external 10 MHz GPS based signal source as a reference for its 650 MHz phase lock as it is the reference frequency for the TCLK output of the system (needed to support legacy hardware around complex). The LCLK System will use a PIP-II Linac RF reference (162.5 MHz) from the Linac LLRF system to allow beam synchronized event placement. Both the ACLK and LCLK systems will have a clock output with a data frame of 16 event bits + 32 data bits with frames broadcast at 650 MHz, phase locked to the 10MHz reference in the case of ACLK and the PIP-II LLRF sourced 162.5 MHz reference in the case of LCLK.
Probabilistic risk assessment (PRA) is being used increasingly by the nuclear industry for safety during normal operations as well as for the protection against external hazards. Computation of total risk in an external hazard PRA is dependent on hazard assessment, fragility assessment, and systems analysis. A systems analysis for propagation of component fragilities is conducted using event and fault trees. The event and fault trees for an actual power plant can be fairly large in size, which imposes computational challenges. Hence, certain assumptions are employed for computational efficiency. These assumptions typically represent the conditions imposed during the design basis (DB) scenario. The traditional PRA tools based on these assumptions are also widely applied to perform risk assessment in the context of beyond design basis (BDB) scenarios. However, some of these assumptions may not be valid for certain BDB scenarios. In addition, the probability of dependent failures also increases in BDB scenarios due to common cause failures (CCF) which usually results from design modifications, human errors, etc. In this manuscript, a simple and a relatively more complex illustrative examples are used to show the limitation of these assumptions in the numerical quantification of risk for the case of BDB conditions. Case studies with CCF events across multiple fault trees are also presented to illustrate the effect of these assumptions when traditional approach is used in BDB risk assessment. It is shown that the assumptions are valid for the case of DB conditions but may lead to excessively conservative risk estimates in the case of BDB conditions. Finally, a Bayesian network based top-down algorithm is proposed as an alternative tool for accurate numerical quantification of total risk in systems analysis.
Superconducting nanowire single-photon detectors (SNSPD) are a promising technology for particle detection. Although SNSPDs have demonstrated picosecond timing accuracy, scaling up large arrays has proved challenging. In this work, we introduce a 32-channel cryo-CMOS application-specific integrated circuit (ASIC) that can be tightly integrated with SNSPD arrays. The ASIC is designed to operate at a temperature of 4K and can perform up to 32 simultaneous timing measurements with a root-mean-square (RMS) accuracy of 8.0ps. The ASIC includes on-chip circuitry for externally biasing superconducting devices, low-noise amplifiers for reading superconducting devices, high-resolution time-to-digital converters (TDC) for time-tagging events, and serializers for transmitting data to room-temperature electronics. The ASIC is manufactured in a 22nm FDSOI process and occupies an area of 4.0mm x 1.0mm. The performance of the ASIC was verified using custom cryogenic device models internally developed for the 22nm SOI process. Measurement results will be presented at the conference.
IRSN (France), LLNL (USA) and ORNL (USA) began a long-term collaboration effort in 2015 to update the nuclear criticality Slide Rule for the emergency response to a nuclear criticality accident. The Slide Rule permits the estimation of neutron and gamma dose rates and integrated doses based upon estimated fission yields, as a function of distance from the fission source, and time after criticality accidents for different critical systems. This paper presents results from the fourth phase of the current update of the Slide Rule project, in which delayed fission-product gamma (DFG) dose rates of unreflected plutonium critical systems were compared by several modern 3D radiation transport codes (MCNP, COG, SCALE), using updated flux-to-dose conversion factors. Dose rates are calculated for fissile material at five moderation ratios (H/Pu), at 1 m above the ground as a function of distance (between 30 cm and 1,200 m) from the external surface of the source to the center of the detector, and for periods between 1 s and 1,000 min after the critical instantaneous event. Further efforts have been devoted to the determination of the delayed gamma source, by comparing the time-dependent energy spectra obtained from several methods. Overall, DFG dose rates calculated by each participant led to consistent results. Extra effort is under way to identify the cause of the remaining differences, by comparing precisely the gamma source, and particularly nuclides inventories.
Superconducting nanowire single-photon detectors (SNSPD) are a promising technology for particle detection. Although SNSPDs have demonstrated picosecond timing accuracy, scaling up large arrays has proved challenging. In this work, we introduce a 32-channel cryo-CMOS application-specifc integrated circuit (ASIC) that can be tightly integrated with SNSPD arrays. The ASIC is designed to operate at a temperature of 4K and can perform up to 32 simultaneous timing measurements with a root-mean-square (RMS) accuracy of 8.0ps. The ASIC includes on-chip circuitry for externally biasing superconducting devices, low-noise amplifers for reading superconducting devices, high-resolution time-to-digital converters (TDC) for time-tagging events, and serializers for transmitting data to room-temperature electronics. The ASIC is manufactured in a 22nm FDSOI process and occupies an area of 4.0mm x 1.0mm. The performance of the ASIC was verifed using custom cryogenic device models internally developed for the 22nm SOI process. Measurement results will be presented at the conference.
We study neutrino-induced charged-current coherent pion production ( ν μ CC - Coh π ) as a tool for constraining the neutrino flux at the Deep Underground Neutrino Experiment (DUNE). The neutrino energy and flavor in the process can be directly reconstructed from the outgoing particles, making it especially useful to specifically constrain the muon neutrino component of the total flux. The cross section of this process can be obtained using the Adler relation with the π -Ar elastic scattering cross section taken either from external data or, as we explore, from a simultaneous measurement in the DUNE near detector. We develop a procedure that leverages ν μ CC - Coh π events to fit for the neutrino flux while simultaneously accounting for relevant effects in the cross section. We project that this method has the statistical power to constrain the uncertainty on the normalization of the flux at its peak to a few percent. This study demonstrates the potential utility of a ν μ CC - Coh π flux constraint, though further work will be needed to determine the range of validity and precision of the Adler relation upon which it relies, as well as to measure the π -Ar elastic scattering cross section to the requisite precision. We discuss the experimental and phenomenological developments necessary to unlock the ν μ CC - Coh π process as a “standard candle” for neutrino experiments.
We study neutrino induced charge current coherent pion production (νμ CC-Coh π) as a tool for constraining the neutrino flux at the Deep Underground Neutrino Experiment (DUNE). The neutrino energy and flavor in the process can be directly reconstructed from the outgoing particles, making it especially useful to specifically constrain the muon neutrino component of the total flux. The cross section of this process can be obtained using the Adler relation with the π-Ar elastic scattering cross section, taken either from external data or, as we explore, from a simultaneous measurement in the DUNE near detector. We develop a procedure that leverages νμ CC-Coh π events to fit for the neutrino flux while simultaneously accounting for relevant effects in the cross section. We project that this method has the statistical power to constrain the uncertainty on the normalization of the flux at its peak to a few percent. This study demonstrates the potential utility of aνμ CC-Coh π flux constraint, though further work will be needed to determine the range of validity and precision of the Adler relation upon which it relies, as well as to measure the π-Ar elastic scattering cross section to the requisite precision. We discuss the experimental and phenomenological developments necessary to unlock the νμ CC-Coh π process as a "standard candle'' for neutrino experiments.
High-energy X-ray diffraction methods can non-destructively map the 3D microstructure and associated attributes of metallic polycrystalline engineering materials in their bulk form. These methods are often combined with external stimuli such as thermo-mechanical loading to take snapshots of the evolving microstructure and attributes over time. However, the extreme data volumes and the high costs of traditional data acquisition and reduction approaches pose a barrier to quickly extracting actionable insights and improving the temporal resolution of these snapshots. This article presents a fully automated technique capable of rapidly detecting the onset of plasticity in high-energy X-ray microscopy data. The technique is computationally faster by at least 50 times than the traditional approaches and works for data sets that are up to nine times sparser than a full data set. This new technique leverages self-supervised image representation learning and clustering to transform massive data sets into compact, semantic-rich representations of visually salient characteristics ( e.g. peak shapes). These characteristics can rapidly indicate anomalous events, such as changes in diffraction peak shapes. It is anticipated that this technique will provide just-in-time actionable information to drive smarter experiments that effectively deploy multi-modal X-ray diffraction methods spanning many decades of length scales.