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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 55 records · Page 3

Dark matter and baryon asymmetry from monopole-axion interactions

We introduce a novel mechanism where the kinetic energy of a rotating axion can be dissipated by the interactions with dark magnetic monopoles. This mechanism leads to a framework where the QCD axion and dark monopoles account for the dark matter density, and the observed baryon asymmetry is generated through the rotating QCD axion via axiogenesis. The monopoles acquire masses from a nonzero axion field, and they can transition between different quantized dyonic levels in the presence of a rotating axion field. The axion kinetic energy is dissipated by the transition, and thus the axion abundance is depleted to the observed dark matter abundance. We predict that the axion decay constant should be below 10 9 GeV to explain the observed dark matter and baryon densities.

Axions and ALPs↗

Masked Particle Modeling on Sets: Towards Self-Supervised High Energy Physics Foundation Models

Abstract We propose masked particle modeling (MPM) as a self-supervised method for learning generic, transferable, and reusable representations on unordered sets of inputs for use in high energy physics (HEP) scientific data. This work provides a novel scheme to perform masked modeling based pre-training to learn permutation invariant functions on sets. More generally, this work provides a step towards building large foundation models for HEP that can be generically pre-trained with self-supervised learning and later fine-tuned for a variety of down-stream tasks. In MPM, particles in a set are masked and the training objective is to recover their identity, as defined by a discretized token representation of a pre-trained vector quantized variational autoencoder. We study the efficacy of the method in samples of high energy jets at collider physics experiments, including studies on the impact of discretization, permutation invariance, and ordering. We also study the fine-tuning capability of the model, showing that it can be adapted to tasks such as supervised and weakly supervised jet classification, and that the model can transfer efficiently with small fine-tuning data sets to new classes and new data domains.

Heinrich, Lukas (ORCID:0000000240487584)↗

Neural architecture codesign for fast physics applications

We develop a pipeline to streamline neural architecture codesign for physics applications to reduce the need for ML expertise when designing models for novel tasks. Our method employs neural architecture search and network compression in a two-stage approach to discover hardware efficient models. This approach consists of a global search stage that explores a wide range of architectures while considering hardware constraints, followed by a local search stage that fine-tunes and compresses the most promising candidates. We exceed performance on various tasks and show further speedup through model compression techniques such as quantization-aware-training and neural network pruning. We synthesize the optimal models to high level synthesis code for FPGA deployment with the hls4ml library. Additionally, our hierarchical search space provides greater flexibility in optimization, which can easily extend to other tasks and domains. We demonstrate this with two case studies: Bragg peak finding in materials science and jet classification in high energy physics, achieving models with improved accuracy, smaller latencies, or reduced resource utilization relative to the baseline models.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Semi-inclusive deep-inelastic scattering on a polarized spin-1 target. II. Deuteron and spectator nucleon tagging

We develop the theoretical framework for semi-inclusive deep-inelastic scattering on a polarized spin-1 target and apply it to scattering on the polarized deuteron with spectator nucleon tagging. In Part I (previous article), we present the general form of the semi-inclusive cross section and polarization observables for the spin-1 target. In Part II (this article), we consider deep-inelastic scattering on the polarized deuteron with spectator nucleon tagging as a special case of target fragmentation. Methods of light-front quantization are employed to separate nuclear and hadronic structure in the high-energy process and achieve a composite description. The light-front wave function of the polarized deuteron is obtained from a rotationally covariant three-dimensional wave function in the center-of-mass frame of the proton-neutron system. The tagged structure functions are computed in the impulse approximation. The momentum and spin distribution of the active nucleon are controlled by the deuteron polarization and the detected spectator momentum (𝐷/𝑆 wave ratio). The cross section and spin asymmetries are evaluated for general deuteron polarization (vector and tensor, longitudinal and transverse) as functions of the spectator momentum. Tensor-polarized spin asymmetries of order unity are achieved for spectator momenta of approximately 300 MeV, which select configurations with a large 𝐷 wave. Sum rules for the tagged spin structure functions are derived. The results can be used for simulations of spectator tagging in future polarized fixed-target experiments (Jefferson Lab) or at the Electron-Ion Collider.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Quantum states of a skyrmion in a two-dimensional antiferromagnet

Quantum states of a skyrmion in a two-dimensional antiferromagnetic lattice are obtained by quantizing the scaling parameter of Belavin-Polyakov model. Skyrmion classical collapse due to violation of the translational invariance of the continuous spin-field model by the lattice is replaced in quantum mechanics by transitions between discrete energy levels of the skyrmion. Here, the rates of transitions due to emission of magnons are computed. Ways of detecting quantization of skyrmion states are discussed.

36 MATERIALS SCIENCE↗

Energy-efficient, Large-scale Molecular Dynamics Simulations via Hardware- and Algorithm-level Optimization

This work aims to develop a framework for energy-efficient computing that will enable molecular dynamics (MD) simulations of large-scale phenomena with atomic precision and simultaneously remove computational bottlenecks limiting the speed of MD simulations. We seek to implement such an approach through the development of surrogate models for the interatomic force calculation combined with the use of mixed numerical precision formats. For a model system of neutral atoms (only pairwise interactions), significant force calculation efficiency improvements were achieved, without detrimental effects on atomic structures or average energies, using single precision, by developing a surrogate model (deep neural network), and by quantizing this surrogate model. For a model system of charged atoms, the reciprocal-space calculation of electrostatic interactions was identified as the main bottleneck, and the development of a surrogate model should be pursued to achieve an estimated one-order-of-magnitude additional speedup.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Nonperturbative Lorentz violation and field quantization

Regimes of Lorentz-violating effective field theories are studied in which departures from Lorentz symmetry are nonperturbative. Within a free toy theory exhibiting Lorentz breakdown involving an operator of mass dimension three, it is shown that conventional methods suffice to achieve field quantization and Fock-space construction. However, the absence of an observer-invariant energy-positivity condition requires physical input beyond the free theory for the unambiguous identification of a ground state. An investigation of the role of thermodynamics in this context is instigated.

Ground state↗

Semiclassical transition state theory through the lens of the restricted partition function

The wide adoption of transition state theory in resolving the rates of molecular processes relies on the simplification from reducing the formal and numerical expense of dynamics by a geometric constraint. Such a reduction is at odds with the uncertainty in localization that the uncertainty principle requires. While many forms of semiclassical transition state theory (SCTST) have been aimed at addressing this challenge, a popular approach has relied on resolving the underlying phase space structure of the exact rate formula to leverage Bohr–Sommerfeld quantization. Here, the Hernandez–Miller SCTST reframed the thermal rate formula into an integral of the so-called restricted partition function (RPF) over the action associated with the reaction. The density-of-state SCTST has reframed the rate formula in terms of the instanton’s density of states (DoS). Here, we show the relationship between the RPF-SCTST and the DoS-SCTST and derive the latter from the former. In this way, we help unify these branches of SCTST and provide a clearer formalism for future advances.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Energy gap of topological surface states in proximity to a magnetic insulator

Abstract Topological surface-states can acquire an energy gap when time-reversal symmetry is broken by interfacing with a magnetic insulator. This gap has yet to be measured. Such topological-magnetic insulator heterostructures can host a quantized anomalous Hall effect and can allow the control of the magnetic state of the insulator in a spintronic device. In this work, we observe the energy gap of topological surface-states in proximity to a magnetic insulator using magnetooptical Landau level spectroscopy. We measure Pb 1-x Sn x Se–EuSe heterostructures grown by molecular beam epitaxy exhibiting a record mobility and low Fermi energy. Through temperature dependent measurements and theoretical calculations, we show this gap is likely due to quantum confinement and conclude that the magnetic proximity effect is weak in this system. This weakness is disadvantageous for the realization of the quantum anomalous Hall effect, but favorable for spintronic devices which require the preservation of spin-momentum locking at the Fermi level.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Finite- and infinite-volume study of DDπ scattering

We develop a comprehensive framework for extracting the pole position and properties of the doubly-charmed tetraquark $T$$^{+}_{cc}$(3875) from lattice QCD data using the relativistic three-particle formalism. This approach incorporates the effect of the one-pion exchange diagram in DDπ and DD ∗ scattering, making it applicable at energies coinciding with the left-hand cut in the partial-wave projected DD ∗ amplitude. We present an example application of this framework to existing lattice QCD data at mπ = 280 MeV. We solve the integral equations describing the DDπ reaction, use LSZ reduction to determine the corresponding DD ∗ amplitude, and find the values of the infinite-volume two- and three-body K matrices that lead to agreement with lattice DD ∗ phase shifts within their uncertainties. Using these K matrices in the three-particle quantization condition, we describe the finite- volume DD ∗ spectrum and find good agreement with the lattice QCD energies. Our results suggest that, at this pion mass, the tetraquark appears as a pair of subthreshold complex poles whose precise location strongly depends on the value of the DDπ three-particle K matrix.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Criteria for electronic growth of Au on layered semiconductors

An electronic growth mode has been reported to occur in several noble metals on MoS2 but has not been observed on other layered semiconductors. In this work, the experiments show that Au(111) islands initially follow an electronic growth mode on WS2, matching the quantization seen in Au/MoS2. However, while epitaxial nanostructures with similar features are observed on WSe2, there is no sign of electronic growth. Binding energy calculations show that multiple bonding sites have nearly the same energy on both WS2 and MoS2, while Au strongly prefers a single bonding site on WSe2. Having multiple sites with the same energy gives flexibility in interfacial bonding that can alleviate strain from the 9+% lattice mismatch in these systems, which would, otherwise, easily suppress quantum size effects from electronic growth modes. These results should be useful in predicting which systems undergo quantized electronic growth on layered semiconductors.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Even Higher-Level Synthesis: An Exploration of AI Hardware Accelerators using HLS4ML

With the rise of artificial intelligence, the popularization of deep learning, and a constantly evolving industry, the demand for flexible and efficient tools has never been greater. As algorithms grow more complex, their runtime and energy consumption increase exponentially. Customized hardware accelerators, long used for specific mathematical operations, remain essential for managing modern applications' computational and power demands. Hardware accelerators can speed up complex computations by orders of magnitude, but their manual design and verification processes are often challenging and time-consuming. High-Level Synthesis (HLS) provides a solution by transforming high-level algorithm descriptions, typically written in C++ or SystemC, into synthesizable RTL suitable for hardware implementation. This approach reduces development time for RTL engineers while offering flexibility beyond what traditional handwritten RTL can provide. We extended this capability to the machine-learning domain with the open-source framework hls4ml, which allows neural networks trained in Python frameworks like Tensorflow or PyTorch to be synthesized into efficient hardware representations for the traditional FPGA and ASIC flows. This breakthrough addresses the growing need for reduced design turnaround and easy verification of ML hardware accelerators with low latency and power efficiency constraints. During this tutorial, we will demonstrate how Python complements HLS by simplifying the ML design process, bridging the gap between software and hardware development. Attendees will explore how we translate neural networks modeled in Python into fixed-point C++ models suitable for HLS workflows. We will dive into strategies like Value-Range Analysis and Quantization-Aware Training, which optimize these designs for deployment and evaluate their accuracy, power consumption, and energy efficiency. To exemplify these concepts, experts from Fermilab will share their experiences applying this technology to high-energy physics experiments, where real-time, low-latency processing is critical. Over the years, Fermilab engineers have demonstrated how deep neural networks, optimized for hardware using hls4ml, can meet the stringent requirements of trigger systems at the CERN Large Hadron Collider. These systems rely on rapid decision-making to process immense data volumes while retaining only the most relevant events for further analysis. The application of hls4ml has also been extended to innovative technologies like smart pixel arrays. These smart pixels integrate ML inference capabilities directly into sensor devices, enabling localized data processing at the pixel level. This approach drastically reduces the need to transmit raw data to external processing units, significantly decreasing power consumption and latency. By embedding neural networks within the pixel architecture, the smart pixels can identify and prioritize relevant data in real time, providing a highly efficient solution for edge computing in scenarios such as particle detectors and imaging systems. Fermilab's work highlights the potential of hardware-accelerated ML in scenarios where both speed and power efficiency are mission-critical. Through this tutorial, attendees will gain valuable insights into the challenges and solutions of deploying ML in hardware. Understanding how HLS and hls4ml streamline the development of neural network-based hardware accelerators is fundamental for the industry's future. Participants will learn how these technologies are shaping the future of AI and scientific computing.

Di Guglielmo, Giuseppe [Fermilab]↗

Achievable Rate and Energy Efficiency Analysis of Multiuser Relay-Aided Massive MIMO Downlink

In this article, the achievable sum rate and the energy efficiency (EE) are investigated for a multiuser relay-aided massive multiple-input–multiple-output (MIMO) downlink. Low-resolution digital-to-analog converters (DACs) are equipped at both the base station (BS) and the relay station (RS), and the amplify-and-forward protocol is adopted at the RS. Under the Rician fading channel, closed-form approximate expressions for the achievable sum rate are derived with perfect and imperfect channel state information. Furthermore, a more general power law is extracted to save transmit power without reducing the achievable sum rate, and a local optimal power allocation scheme is proposed to improve the channel capacity of active users. Then, the tradeoff between the achievable sum rate and the EE is discussed. The numerical results show that, due to the quantization noise, the transmit power has a limited increase in the achievable rate. In addition, it is more valuable to improve the DAC quantization bit at the RS when the number of BS antennas is larger than that of RS antennas. In addition, under strong line-of-sight channels or high pilot transmit power conditions, the channel estimation accuracy is higher, and the best tradeoff between the achievable sum rate and the EE can be obtained when the DAC quantization bit is 4.

25 ENERGY STORAGE↗

Quadrupole topological photonic crystals

Quadrupole topological phases, exhibiting protected boundary states that are themselves topological insulators of lower dimensions, have recently been of great interest. Extensions of these ideas from current tight binding models to continuum theories for realistic materials require the identification of quantized invariants describing the bulk quadrupole order. Here we identify the analog of quadrupole order in Maxwell’s equations for a gyromagnetic photonic crystal (PhC) through a double-band-inversion process. The quadrupole moment is quantized by the simultaneous presence of crystalline symmetry and broken time-reversal symmetry, which is confirmed using three independent methods: analysis of symmetry eigenvalues, numerical calculations of the nested Wannier bands and the expectation value of the quadrupole operator. Furthermore, we reveal the boundary manifestations of quadrupole phases as quantized edge polarizations and fractional corner charges. The latter are the consequence of a filling anomaly of energy bands as first predicted in electronic systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Self-Similarity, Fractality and Entropy Principle in Collisions of Hadrons and Nuclei at Tevatron, RHIC and LHC

$z$-Scaling of inclusive spectra as a manifestation of self-similarity and fractality of hadron interactions is illustrated.The scaling for negative particle production in $Au+Au$ collisionsfrom BES-I program at RHIC is demonstrated. The scaling variable $z$ depends on the momentum fractions of the colliding objects carried by the interacting constituents, and momentum fractions of the produced objects in the scattered and recoil directions, which carries the inclusive particle and its counterpart, respectively. Structures of the colliding objects and fragmentation processes in final stateare expressed by fractal dimensions. Medium produced in the collisions is described by a specific heat. The scaling function $\psi(z)$ reveals energy, angular, multiplicity, and flavor independence.It has a power behavior at high $z$ (high $p_T$). Based on entropy principle and $z$-scaling, energy loss as a function of the collision energy, centrality and transverse momentum of inclusive particle is estimated.New conservation law including fractal dimensions is found. Quantization of fractal dimensions is discussed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Resource-Efficient Chemistry on Quantum Computers with the Variational Quantum Eigensolver and The Double Unitary Coupled-Cluster approach

Applications of quantum simulation algorithms to obtain electronic energies of molecules on noisy intermediate-scale quantum (NISQ) devices require careful consideration of resources describing the complex electron correlation effects. In modeling second-quantized problems, the biggest challenge confronted is that the number of qubits scales linearly with the size of molecular basis. This poses a significant limitation on the size of the basis sets and the number of correlated electrons included in quantum simulations of chemical processes. To address this issue and to enable more realistic simulations on NISQ computers, we employ the double unitary coupled-cluster (DUCC) method to effectively downfold correlation effects into the reduced-size orbital space, commonly referred to as the active space. Using downfolding techniques, we demonstrate that properly constructed effective Hamiltonians can capture the effect of the whole orbital space in small-size active spaces. Combining the downfolding pre-processing technique with the Variational Quantum Eigensolver, we solve for the ground-state energy of H2 and Li2 in the cc-pVTZ basis using the DUCC-reduced active spaces. We compare these results to full configuration-interaction and high-level coupled-cluster reference calculations.

quantum computing, variational quantum solver, cou↗

Lightweight jet reconstruction and identification as an object detection task

We apply object detection techniques based on deep convolutional blocks to end-to-end jet identification and reconstruction tasks encountered at the CERN large hadron collider (LHC). Collision events produced at the LHC and represented as an image composed of calorimeter and tracker cells are given as an input to a Single Shot Detection network. The algorithm, named PFJet-SSD performs simultaneous localization, classification and regression tasks to cluster jets and reconstruct their features. This all-in-one single feed-forward pass gives advantages in terms of execution time and an improved accuracy w.r.t. traditional rule-based methods. A further gain is obtained from network slimming, homogeneous quantization, and optimized runtime for meeting memory and latency constraints of a typical real-time processing environment. We experiment with 8-bit and ternary quantization, benchmarking their accuracy and inference latency against a single-precision floating-point. We show that the ternary network closely matches the performance of its full-precision equivalent and outperforms the state-of-the-art rule-based algorithm. Finally, we report the inference latency on different hardware platforms and discuss future applications.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Giant topological longitudinal circular photo-galvanic effect in the chiral multifold semimetal CoSi

The absence of mirror symmetry, or chirality, is behind striking natural phenomena found in systems as diverse as DNA and crystalline solids. A remarkable example occurs when chiral semimetals with topologically protected band degeneracies are illuminated with circularly polarized light. Under the right conditions, the part of the generated photocurrent that switches sign upon reversal of the light’s polarization, known as the circular photo-galvanic effect, is predicted to depend only on fundamental constants. The conditions to observe quantization are non-universal, and depend on material parameters and the incident frequency. In this work, we perform terahertz emission spectroscopy with tunable photon energy from 0.2 –1.1 eV in the chiral topological semimetal CoSi. We identify a large longitudinal photocurrent peaked at 0.4 eV reaching ~550 μ A/V 2 , which is much larger than the photocurrent in any chiral crystal reported in the literature. Using first-principles calculations we establish that the peak originates only from topological band crossings, reaching 3.3 ± 0.3 in units of the quantization constant. Our calculations indicate that the quantized circular photo-galvanic effect is within reach in CoSi upon doping and increase of the hot-carrier lifetime. The large photo-conductivity suggests that topological semimetals could potentially be used as novel mid-infrared detectors.

79 ASTRONOMY AND ASTROPHYSICS↗