Quantum Networks for High Energy Physics (HEP)
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Nuclear reactors are uniquely powerful, abundant, and flavor-pure sources of antineutrinos that continue to play a vital role in the US neutrino physics program. The US reactor antineutrino physics community is a diverse interest group encompassing many detection technologies and many particle physics topics, including Standard Model and short-baseline oscillations, BSM physics searches, and reactor flux and spectrum modeling. The community's aims offer strong complimentary with numerous aspects of the wider US neutrino program and have direct relevance to most of the topical sub-groups composing the Snowmass 2021 Neutrino Frontier. Reactor neutrino experiments also have a direct societal impact and have become a strong workforce and technology development pipeline for DOE National Laboratories and universities. This white paper, prepared as a submission to the Snowmass 2021 community organizing exercise, will survey the state of the reactor antineutrino physics field and summarize the ways in which current and future reactor antineutrino experiments can play a critical role in advancing the field of particle physics in the next decade.
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We present Diagrammatica, a symbolic computation extension to the HEPTAPOD agentic framework, which enables LLM agents to plan and execute multi-step theoretical calculations. Symbolic computation poses a distinctive reliability challenge for LLM agents, as correctness is governed by implicit mathematical conventions that are not encoded in a form that can be easily checked in the computational backend. We identify two complementary remedies, tool-constrained computation and targeted knowledge grounding, and pursue the first as the primary architecture. Concretely, we concentrate the agent's action distribution onto tool calls with convention-fixing semantics, in which the agent specifies a compact, human-auditable diagram specification and a trusted backend performs the symbolic or numerical manipulations exactly. The toolkit provides two complementary calculation paths consuming a shared diagram specification: Naive Dimensional Analysis (NDA) for order-of-magnitude rate estimates and Exact Diagrammatic Analysis (EDA) for tree-level symbolic calculations via automatic FeynCalc code generation, both supplemented by automatic Feynman diagram enumeration and a navigable theory knowledge base. The architecture is validated on two benchmarks: (1) an exhaustive catalog of all tree-level, single-vertex $1\to 2$ partial decay widths across scalar, fermion, and vector parents, with complete massless and threshold limits and Standard Model validation; and (2) an NDA sensitivity study of the muon decay multiplicity $μ^+ \to ν_μ\barν_e + n(e^+e^-) + e^-$, determining the maximum observable $n$ at current and planned muon experiments.
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We demonstrate a silicon photonic transmitter designed for reading out physics detectors in extreme environments. The transmitter operates at room and cryogenic temperature and consists of a micro-ring modulator and a co-designed CMOS serializer and driver.
Diagnostics in high energy density physics, shock physics, and related fields are primarily driven by a need to record rapidly time-evolving signals in single-shot events. These measurements are often limited by channel count and signal degradation issues on cable links between the detector and digitizer. Here we present the Ultrafast Pixel Array Camera (UPAC), a compact and flexible detector readout system with 32 waveform-recording channels at up to 10 Gsample/s and 1.8 GHz analog bandwidth. The compact footprint allows the UPAC to be directly embedded in the detector environment. A key enabling technology is the PSEC4A chip, an eight-channel switch-capacitor array sampling device with up to 1056 samples/channel. The UPAC system includes a high-density input connector that can plug directly into an application-specific detector board, programmable control, and serial readout, with less than 5 W of power consumption in full operation. We present the UPAC design and characterization, including a measured timing resolution of ~20 ps or better on acquisitions of sub-nanosecond pulses with minimal system calibrations. Example applications of the UPAC are also shown to demonstrate operation of a solid-state streak camera, an ultrafast imaging array, and a neutron time-of-flight spectrometer.
High-repetition-rate (HRR) experiments can collect large datasets with high temporal, spatial, and/or parametric resolution or large numbers of repeat measurements for statistics. HRR experiments also enable new experimental designs, including active feedback control loops and novel diagnostics, that can improve the reproducibility as well as the quantity of measurements. Together, these attributes make HRR experiments ideal for performing high-quality repeatable science. Until recently, these techniques have not been applied to high-energy-density–physics (HEDP) experiments, which are typically restricted to repetition rates of a few per day. However, recent advancements in lasers, pulsed-power drivers, target fabrication, and diagnostics are starting to change this fact, opening an exciting new frontier of HRR HEDP experiments. A mini-conference on this subject at the 2021 meeting of the American Physical Society Division of Plasma Physics brought together members of this growing community. As a result, the “High Repetition Rate Frontier in High-Energy-Density Physics” special topic in Physics of Plasmas highlights current progress in this exciting area.
In many high energy physics neutrino beamlines, targets are made up of isotropic POCO graphite grade for production of neutrinos for high energy particle physics research and are bombarded with highly energetic pulsed proton beam. The pulsed proton beam with a small beam size creates thermal stress waves as well as radiation damage such as displacement damage, void formation, swelling, and gas formation amongst others. As a result, after a few years of operation one of the longest operated targets appears to have undergone bulk swelling and fractured along the beam centerline. A complex interaction of dynamic loading due to beam, material degradation due to irradiation are thought to have caused such a failure. Here, we present a numerical simulation to capture the combined effects of swelling and the dynamic effect of thermal stress waves due to beam heating, to explain the failure of target. An empirical formula has been developed to take into account swelling as a function of temperature and proton fluence and has been implemented in a commercial finite element code. Extensive X-ray diffraction (XRD) were performed on graphite samples from the failed target to understand the lattice parameter changes due to irradiation and build a valid empirical model. Simulation results show that swelling plays a major role in elevating the stress state in the material exceeding the failure strength while the pulsed beam heating also introduces fatigue loading that would explain brittle fracture emanating from inside of the material and propagating outward. Such empirical formulations will help in identifying the threshold values of critical parameters to extend the service life of future multi-megawatt neutrino targets and targets in other accelerator environments.
Abstract In the current era of noisy intermediate-scale quantum computers, noisy qubits can result in biased results for early quantum algorithm applications. This is a significant challenge for interpreting results from quantum computer simulations for quantum chemistry, nuclear physics, high energy physics (HEP), and other emerging scientific applications. An important class of qubit errors are readout errors. The most basic method to correct readout errors is matrix inversion, using a response matrix built from simple operations to probe the rate of transitions from known initial quantum states to readout outcomes. One challenge with inverting matrices with large off-diagonal components is that the results are sensitive to statistical fluctuations. This challenge is familiar to HEP, where prior-independent regularized matrix inversion techniques (“unfolding”) have been developed for years to correct for acceptance and detector effects, when performing differential cross section measurements. We study one such method, known as iterative Bayesian unfolding, as a potential tool for correcting readout errors from universal gate-based quantum computers. This method is shown to avoid pathologies from commonly used matrix inversion and least squares methods.
Significant technology maturation efforts are underway by privately funded fusion startups with the goal to demonstrate mature HTS magnet technology. To support the private sector development effort and the DOE milestone based program, a U.S. Fusion Magnet Community Workshop was held on March 14-15, 2023 in Princeton, NJ. This was the first U.S. community workshop focused on fusion magnet technologies aimed at determining the structure and technical direction for a public program designed to complement the private fusion industry landscape. Based on the wide range of different contributions, a set of general themes and fusion magnet R&D needs were identified and discussed. Feedback received to the workshop charge questions highlighted critical magnet R&D gaps such as availability of existing large cable and coil test facilities, a magnet education program that can generate a trained and essential workforce by leveraging R&D capabilities of universities, U.S. national labs, and fusion industry. Other opportunities synergistic and complementary with high energy physics, high field magnets that are open for a broad range of science drivers. The defined R&D gaps underpin the need for a mid-term and long-term public program in fusion magnet development, which reflects the purpose of the workshop in developing the rationale and consent for such a base program. A self-consistent, fusion specific U.S. fusion magnet program will complement and de-risk fusion pilot plants (FPPs) of promising magnetic configurations developed by private companies on a timeline consistent with the NASEM report on bringing fusion to the U.S. grid. We describe the magnet challenges presented and R&D needs discussed in the workshop. In conclusion, these challenges and R&D needs provide focus for the development of U.S. mid-term and long term roadmaps on enabling HTS for high field fusion.
Large scale high-energy density science facilities continue to grow in scale and complexity worldwide. The increase in driver capabilities, including pulsed-power and lasers, continue to push the boundaries of temperature, pressure, and densities, opening up new physics regimes. X-ray imaging is one of the many diagnostic techniques that are used to probe states of matter in these extreme conditions. Improved fabrication and polishing methods have provided improved x-ray microscope performance, while improving detector and x-ray sources now enable pico-second imaging with few micron resolutions. Here this Review will cover x-ray imaging methods, primarily absorption imaging, and their improvements over the last few decades.
We report on the design and implementation of a new system used to characterize the energy-dependent x-ray transmission curve, Θ(E), through filters used in high-energy density physics diagnostics. Using an Amptek X-123-CdTe x-ray spectrometer together with a partially depleted silicon surface barrier detector, both the energy spectrum and total emission of an x-ray source have been accurately measured. By coupling these detectors with a custom PROTO-XRD x-ray source with interchangeable cathodes, accurate characterizations of Θ(E) for filters of varying materials and thicknesses have been obtained. The validity of the technique has been confirmed by accurately reproducing areal densities for high-purity filters with known x-ray transmission properties. In this paper, the experimental setup is described and the results of absorption calibrations performed on a variety of different filters are presented.
Machine learning techniques are increasingly used in high-energy nuclear physics because they can exploit multivariate correlations more efficiently than conventional cut-based analyses. A central challenge is the construction of training samples that faithfully reproduce the detector response observed in data. Signal samples are usually derived from detector simulations; therefore, mismatches between simulation and data can degrade classifier performance and introduce systematic biases. This work presents two practical correction procedures, namely cumulative distribution function (CDF) mapping and a shift-and-scale transformation, to align simulated signal features with those measured in data. Their performance is demonstrated with $J$/$\psi$ yield measurements in $\sqrt{s_{nn}}$ = 200 GeV Ru+Ru and Zr+Zr collisions recorded by STAR. A set of self-consistency tests shows that these procedures substantially suppress the systematic bias associated with data-simulation discrepancies in machine-learning-based signal extraction.
High-energy physics requires the generation of large numbers of simulated data samples from complex but analytically tractable distributions called matrix elements. Surrogate models, such as normalizing flows, are gaining popularity for this task due to their computational efficiency. We adopt an approach based on flow annealed importance sampling bootstrap (FAB) that evaluates the differentiable target density during training and helps avoid the costly generation of training data in advance. We show that FAB reaches higher sampling efficiency with fewer target evaluations in high dimensions in comparison to other methods.
Optical collective Thomson scattering is used to diagnose magnetized high energy density physics experiments at the Magpie pulsed-power generator at Imperial College London. The system uses an amplified pulse from the 2nd harmonic of a Nd:YAG laser (3 J, 8 ns, 532 nm) to probe a wide diversity of hightemperature plasma objects; with densities in the range of 10 17 -10 19 cm -3 and temperatures between 10 eV and a few keV. The scattered light is collected from 100 μm-scale volumes within the plasmas, which are imaged onto optical fiber arrays. Multiple collection systems observe these volumes from different directions, providing simultaneous probing with different scattering K-vectors (and different associated α-parameters, typically in the range 0.5 – 3) allowing independent measurements of separate velocity components of the bulk plasma flow. The fiber arrays are coupled to an imaging spectrometer with a gated ICCD. The spectrometer is configured to view the ion-acoustic waves (IAWs) of the collective Thomson scattered spectrum. Fits to the spectra with the theoretical spectral density function S(K,ω) yield measurements of the local plasma temperatures and velocities. Fitting is constrained by independent measurements of the electron density from laser interferometry, and the corresponding spectra for different scattering vectors. Furthermore, this TS diagnostic has been successfully implemented on a wide range of experiments, revealing temperature and flow velocity transitions across magnetized shocks, inside rotating plasma jets and imploding wire arrays, as well as providing direct measurements of drift velocities inside a magnetic reconnection current sheet.
Convergent high-energy-density (HED) experimental platforms are used to study matter under some of the most extreme conditions that can be produced on Earth, comparable to the interior of stars. There are many challenges in using these systems for fundamental measurements currently being addressed by new analysis methods, such as the combination of a reduced physics model and Bayesian inference, allowing a self-consistent inference of physical quantities with a robust error analysis. These methods in combination with simple (as compared to inertial confinement fusion implosions) implosion platforms, which can be modified to show sensitivity to different physical mechanisms of interest, are used to study the physical properties of matter under extreme conditions. This work discusses a subset of implosion targets for studying opacity effects, electron-ion equilibration, and thermal conductivity and, as an example, a system consisting of a thick-shelled, gas-filled laser-direct-drive implosion is used to show how a reduced model and Bayesian inference can help inform experimental design decisions such as diagnostic choice. It is shown that for this system that a combination of neutron and x-ray self-emission diagnostics is critical for constraining the details of the thermodynamic states in the system and that the conductivity exponent in a Spitzer like framework can be constrained to the 30% level in deuterium at gigabar conditions. Furthermore, this process can be applied to many HED systems to make underlying model assumptions explicit and facilitate experimental design and analysis.