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At least 721 records · Page 40

Portable Parallel Algorithms and Frameworks for Exascale Graph Analytics

Graphs (or networks) are a tool used to model the interactions among various entities. Efficiently processing large graphs has recently attracted significant attention due to the applications of graphs in various domains, such as biology, chemistry, and cyber-security. Analyzing the structure and properties of these graphs is an important component of many scientific computing pipelines. With the explosion in the volume of data, graphs have become very large and can contain hundreds of billions of vertices and trillions of edges. Therefore, it is crucial to develop high-performance methods to enable graph analysis to be done quickly and energy-efficiently. Furthermore, these solutions should be highly parallel in order to take advantage of modern parallel machines. However, designing efficient solutions is not enough. With the wide variety of computing environments available, each with different programmability and performance characteristics, it is necessary to develop solutions that are portable in terms of both performance (i.e., provide theoretical guarantees) and programmability (i.e., provide high level abstractions).

97 MATHEMATICS AND COMPUTING↗

Simulated Behavior of DUNE Near Detectors Using Updated CAFAna Analysis Framework

Simulations are run using the new DUNE CAFAna framework to generate pseudo-data modeling the interactions of neutrinos in the DUNE near detector at truth-level and detector-level. Truth-level analysis of neutrino kinematics reveals strong agreement with expected behavior, validating the kinematic portion of the simulation. Examination of the detector-level reconstructions of coordinates of interaction vertex appear consistent with an interaction density independent of detector position. Track lengths of particles resultant from neutrino interactions are aligned with varied particle identities, but are misaligned with prediction of uniform position density.

Fein, Jarrett [Michigan State U.]↗

Mapping Spiking Neural Networks to Heterogeneous Crossbar Architectures using Integer Linear Programming

Advances in novel hardware devices and architectures allow Spiking Neural Network (SNN) evaluation using ultra-low power, mixed-signal, memristor crossbar arrays. As individual network sizes quickly scale beyond the dimensional capabilities of single crossbars, networks must be mapped onto multiple crossbars. Crossbar sizes within modern Memristor Crossbar Architectures (MCAs) are determined predominately not by device technology but by network topology; more, smaller crossbars consume less area thanks to the high structural sparsity found in larger, brain-inspired SNNs. Motivated by continuing increases in SNN sparsity due to improvements in training methods, we propose utilizing heterogeneous crossbar sizes to further reduce area consumption. This approach was previously unachievable as prior compiler studies only explored solutions targeting homogeneous MCAs. Our work improves on the state-of-the-art by providing Integer Linear Programming (ILP) formulations supporting arbitrarily heterogeneous architectures. By modeling axonal interactions between neurons, our methods produce better mappings while removing inhibitive a priori knowledge requirements. We first show a 16.7-27.6% reduction in area consumption for square-crossbar homogeneous architectures. Then, we demonstrate 66.9-72.7% further reduction when using a reasonable configuration of heterogeneous crossbar dimensions. Next, we present a new optimization formulation capable of minimizing the number of inter-crossbar routes. When applied to solutions already near-optimal in area, an 11.9-26.4% routing reduction is observed without impacting area consumption. Finally, we present a profile-guided optimization capable of minimizing the number of runtime spikes between crossbars. Compared to the best-area-then-route optimized solutions, we observe a further 0.5-14.8% inter-crossbar spike reduction while requiring 1–3 orders of magnitude less solver time.

Pohl, Devin [ORNL] (ORCID:0009000040149027)↗

Evolution of Robustness in Growing Random Networks

Networks are widely used to model the interaction between individual dynamic systems. In many instances, the total number of units and interaction coupling are not fixed in time, and instead constantly evolve. In networks, this means that the number of nodes and edges both change over time. Various properties of coupled dynamic systems, such as their robustness against noise, essentially depend on the structure of the interaction network. Therefore, it is of considerable interest to predict how these properties are affected when the network grows as well as their relationship to the growth mechanism. Here, we focus on the time evolution of a network’s Kirchhoff index. We derive closed-form expressions for its variation in various scenarios, including the addition of both edges and nodes. For the latter case, we investigate the evolution where single nodes with one or two edges connecting to existing nodes are added recursively to a network. In both cases, we derive the relations between the properties of the nodes to which the new node connects along with the global evolution of network robustness. In particular, we show how different scalings of the Kirchhoff index can be obtained as a function of the number of nodes. We illustrate and confirm this theory via numerical simulations of randomly growing networks.

97 MATHEMATICS AND COMPUTING↗

Symmetric Mass Generation in Lattice Gauge Theory

We construct a four-dimensional lattice gauge theory in which fermions acquire mass without breaking symmetries as a result of gauge interactions. Our model consists of reduced staggered fermions transforming in the bifundamental representation of an SU(2)×SU(2) gauge symmetry. This fermion representation ensures that single-site bilinear mass terms vanish identically. A symmetric four-fermion operator is however allowed, and we give numerical results that show that a condensate of this operator develops in the vacuum.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Illuminating Dark Matter Admixed in Neutron Stars with Simultaneous Mass–Radius Constraints

We investigate how simultaneous mass and radius measurements of massive neutron stars can help constrain the properties of dark matter possibly admixed in them. Within a fermionic dark matter model that interacts only through gravitation, along with a well-constrained nuclear matter equation of state, we show that the simultaneous mass and radius measurement of PSRJ0740+6620 reduces the uncertainty of dark matter central energy density by more than 50% compared to the results obtained from using the two observables independently, while other dark matter parameters remain unconstrained. Additionally, we find that the dark matter fraction fD should be smaller than 2% when constrained by the observed neutron star maximum mass alone, and it could be even smaller than 0.3% with the simultaneous measurement of mass and radius, supporting the conclusion that only a small amount of dark matter exists in dark matter admixed neutron stars (DANSs).

NICER↗

The Encounter of the Parker Solar Probe and a Comet-like Object Near the Sun: Model Predictions and Measurements

The Parker Solar Probe (PSP) aims to explore the nascent solar wind close to the Sun. Meanwhile, PSP is also expected to encounter small objects like comets and asteroids. Here, we survey the ephemerides to find the chance of a recent encounter and then model the interaction between released dusty plasmas and solar wind plasmas. On 2019 September 2, a comet-like object, the 322P/Solar and Heliosphere Observatory, just passed its perihelion flying to a heliocentric distance of 0.12 au and swept by PSP at a relative distance as close as 0.025 au. We present the dynamics of the dust particles released from 322P, forming a curved dust tail. Along the path of PSP in the simulated inner heliosphere, the states of plasma and magnetic field are sampled and illustrated, with the magnetic field sequences from simulation results being compared directly with the in situ measurements from PSP. Through the comparison, we suggest that 322P might be at a deficient activity level releasing limited dusty plasmas on its way to becoming a "rock comet." We also present images of solar wind streamers as recorded by the Wide-field Imager for Solar Probe Plus, showing an indication of dust bombardment for the images superposed with messy trails. We observe from the Large Angle and Spectrometric Coronagraph that 322P was transiting from a dimming region to a relatively bright streamer during its perihelion passage, and perform a simulation to confirm that 322P was flying from relatively faster to slower solar wind streams, modifying the local plasma states of the streams.

79 ASTRONOMY AND ASTROPHYSICS↗

On the Anticorrelation between Duration and Redshift in Gamma-Ray Bursts

Abstract For gamma-ray bursts (GRBs) with durations greater than two seconds (so-called long GRBs), the intrinsic prompt gamma-ray emission appears, on average, to last longer for bursts at lower redshifts. We explore the nature of this duration–redshift anticorrelation, describing systems and conditions in which this cosmological evolution could arise. In particular, we explore its dependence on the metallicity of a massive star progenitor, because we can securely count on the average stellar metallicity to increase with decreasing redshift. Although stars with higher metallicity/lower redshift lose mass and angular momentum through line-driven winds, in some cases these stars are able to form more extended accretion disks when they collapse, potentially leading to longer-duration GRBs. We also examine how this duration–redshift trend may show up in interacting binary models composed of a massive star and compact object companion, recently suggested to be the progenitors of radio-bright GRBs. Under certain conditions, mass loss and equation-of-state effects from massive stars with higher metallicity and lower redshift can decrease the binary separation. This can then lead to spin-up of the massive star and allow for a longer-duration GRB upon the massive star’s collapse. Finally, the duration–redshift trend may also be supported by a relatively larger population of small-separation binaries born in situ at low redshift.

79 ASTRONOMY AND ASTROPHYSICS↗

New Particle Formation Event Dataset at the Southern Great Plains (SGP) Observatory from 2018 to 2023

This data set contains observations of new particle formation (NPF) events collected at the U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) observatory from 2018 to 2023. Measurements include particle number size distributions, radiance measurements, and associated meteorological variables from onsite instrumentation relevant for identifying and characterizing NPF events. Events were identified using standardized criteria and documented to support investigations of aerosol nucleation, growth dynamics, and their interactions with local atmospheric conditions. The data set provides a multi-year record that enables evaluation of seasonal and interannual variability in NPF occurrence and intensity at a mid-continental site. These data are intended to support studies of aerosol-cloud-climate interactions and model evaluation within both ARM and the broader atmospheric science community. More information can be found within the README_NPF_SGP_2018_2023.docx file.

event↗

Investigating Electro-Nuclear Interactions in a New Dark Matter Search

Electro-nuclear (EN) interactions are interactions in which an incident electron collides with a nucleus, scattering the electron and creating byproduct particles. Such interactions are of interest to neutrino physicists, who use EN interactions to inform model building of neutrino-nucleus interactions. The Light Dark Matter Experiment (LDMX) is a small-scale, fixed-target, electron beam experiment which seeks to probe for dark matter and mediator particle production in the sub-GeV mass region. The 8GeV LDMX electron beam will serve as an opportunity to study electro-nuclear interactions in final states in the multi-GeV region. LDMX s missing energy trigger for dark matter interactions however, will not be sufficient to efficiently capture EN interactions. An additional trigger is needed. Using simulated events, that included background and EN interactions, a trigger on momentum was developed.

Croteau, Beatrice↗

Examination of simulated behavior of ND-LAr and TMS detectors using CAFAna for DUNE analysis framework

Simulations are run using the new DUNE CAFAna framework to generate pseudo-data modeling the interactions of neutrinos in the DUNE near detector at truth-level and detector-level. Truth-level analysis of neutrino kinematics reveals strong agreement with expected behavior, validating the kinematic portion of the simulation. Examination of the detector-level reconstructions of coordinates of interaction vertex appear consistent with an interaction density independent of detector position. Track lengths of particles resultant from neutrino interactions are aligned with varied particle identities, but are misaligned with prediction of uniform position density.

Fein, Jarrett [Fermilab]↗

Optimizing Transportation Networks for E-Waste Reverse Logistics: A Multi-Modal Cost Allocation and Pricing Strategy

The exponential growth of electronic waste (e-waste) poses critical challenges for sustainable reverse logistics and transportation network optimization. This study develops a dual-channel transportation framework for e-waste logistics that integrates dynamic freight pricing, cost allocation mechanisms, and game-theoretic coordination. The model captures interactions between centralized hubs and distributed processing networks, accounting for freight rate elasticity, volume allocation, and capacity constraints. Using Stackelberg game theory and cost-sharing strategies, the framework optimizes transportation efficiency and profit distribution across logistics channels. Numerical simulations show that the dual-channel structure increases centralized hub profit by 226.8% compared to baseline single-channel operations, while boosting total transported volume by 1.2% and nearly doubling freight collector profit under cost-sharing. Scenario analyses across regional infrastructures reveal that network density, policy incentives, and logistics costs shape routing efficiency and profit allocation. These findings suggest that coordinated strategies combining dynamic pricing, targeted infrastructure investment, and strategic cost allocation are needed to design efficient, resilient, and regionally adaptable e-waste transportation systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Application of machine learning for modeling brønsted-guggenheim-scatchard specific ion interaction theory (SIT) coefficients

Machine learning methodologies can provide insight into Brønsted-Guggenheim-Scatchard specific ion interaction theory (SIT) parameter values where experimental data availability may be limited. This study develops and executes machine learning frameworks to model the SIT interaction coefficient, ε. Key findings include successful estimations of ε via artificial neural networks using clustering and value prediction approaches. Additionally, applicability to other chemical parameters is also assessed briefly. Models developed here provide support for a use-case of machine learning in geologic nuclear waste disposal research applications, namely in predictions of chemical behaviors of high ionic strength solutions (i.e., subsurface brines).

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Evidence for a QCD accelerator in relativistic heavy-ion collisions

Here, we report measurements of forward jets produced in Cu + Au collisions at $\sqrt{s{NN}}$=200 GeV at the Relativistic Heavy Ion Collider. The jet-energy distributions extend to energies much larger than expected by Feynman scaling. This constitutes the first clear evidence for Feynman-scaling violations in heavy-ion collisions. Such high-energy particle production has been in models via QCD string interactions, but so far is untested by experiment. One such model calls this a hadronic accelerator. Studies with a particular heavy-ion event generator ( HIJING ) show that photons and mesons exhibit such very high-energy production in a heavy-ion collision, so a QCD accelerator appropriately captures the physics associated with such QCD string interactions. All models other than HIJING used for hadronic interactions in the study of extensive air showers from cosmic rays either do not include these QCD string interactions or have smaller effects from the QCD accelerator.

43 PARTICLE ACCELERATORS↗

X-ray fluorescence standing wave study of the interaction of the antimicrobial peptide indolicidin with a supported model membrane

Indolicidin, a cationic antimicrobial peptide, interacts with lipid bilayers through electrostatic and hydrophobic interactions, disrupting microbial membranes. We investigated the depth-dependent localization of gold-nanoparticle-labeled indolicidin in a supported model membrane using X-ray fluorescence standing wave (SWXF) analysis. Liposomes composed of DMPC and DMPG were incubated with indolicidin labeled at its C-terminus with a 1.8 nm gold nanoparticle, then deposited onto a Si/Mo multilayer substrate via vesicle bursting. SWXF measurements revealed that at low peptide incubation concentrations (2–5 μM), gold-nanoparticle-labeled indolicidin remains primarily associated with the bilayer's outer leaflet. At higher concentrations (10 μM), the peptide penetrates deeper into the bilayer, with the labeled C-terminal region either localizing near the membrane's hydrophobic core or inducing membrane breakup. In conclusion, these findings suggest a concentration-dependent insertion mechanism.

Basnet, Gobind [Northern Illinois Univ., DeKalb, I↗