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The Standard Model of Particle Physics includes: 1. describes nature in a economic and elegant way (spontaneously broken) gauge symmetry; 2. generates fermion and boson masses via the Higgs mechanism.
The research supported by this DOE grant investigated the nature of dark matter, particularly its particle properties and astrophysical manifestations, over the period of 2018 to 2024. The proposal focused on leveraging astrophysical probes, cosmological models, and high-resolution simulations to further our understanding of dark matter, its possible interactions, and the theoretical models that could explain its properties. This research directly contributes to the DOE's mission of exploring the fundamental constituents of the universe, advancing particle physics, and using cosmological and astrophysical observations to detect dark matter.
Quantum science and technology have the potential to revolutionize particle physics. Fermilab researchers take advantage of the quantum properties of physical systems to acquire, communicate and process information far beyond what we can accomplish with classical capabilities. Successful quantum applications could be transformational for particle physics, enabling studies of nature’s fundamental properties that would not otherwise be possible.
In this work, we study a two-mode bosonic encoding of a quoctit inside a non-Abelian group-structured constellation of coherent states. This work is motivated by the importance of non-Abelian symmetry in particle physics and the desire to have transversal non-Abelian logical gates. We use the previously developed 2 T constellation of states used to encode a so-called 2 T qutrit. The fidelity of the 2 T quoctit is benchmarked against other bosonic qudits for different noise models and find it compare favorably when power constraints are considered. This paves the way for the construction of higher-dimensional qudits (e.g., a quicosotetrit with 2 T group structure) in bosonic systems with practical applications in quantum simulations of particle physics.
Skyrmions were originally introduced in Particle Physics as a possible mechanism to explain the stability of particles. Lately the concept has been applied in Condensed Matter Physics to describe the newly discovered topologically protected magnetic configurations called the magnetic Skyrmions. Here, we introduce the concept at a level suitable for beginning students of Physics.
In a former gold mine in South Dakota, an international particle-physics experiment will delve into the unexplained matter–antimatter imbalance that gave rise to the universe--discussed here.
High granularity silicon pixel sensors are at the heart of energy frontier particle physics collider experiments. At an collision rate of 40\,MHz, these detectors create massive amounts of data. Signal processing that handles data incoming at those rate and intelligently reduces the data within the pixelated region of the detector \textit{at rate} will enhance physics performance and enable physics analyses that are not currently possible. Using the shape of charge clusters deposited in an array of small pixels, the physical properties of the traversing particle can be extracted with locally customized neural networks. In this first work, we present a neural network that can be embedded into the on-sensor readout and filter out hits from low momentum tracks, reducing the detector's data volume by 54.4-75.4\%. The network is designed and simulated as a custom readout integrated circuit with 28\,nm CMOS technology and is expected to operate at less than 300\,$\mu W$ with an area of less than 0.2\,mm$^2$.
The Future Circular electron-positron Collider, FCC- ee, is designed for unprecedented precision for particle physics experiments from the Z-pole up to above the top-pair-threshold, corresponding to a beam energy range from 45.6 to 182.5 GeV. Performing collisions at various particle-physics resonances requires precise knowledge of the centre-of-mass energy (ECM) and collision boosts at all four interaction points. Measurement of the ECM by resonant depolarization of transversely polarized pilot bunches in combination with a 3D polarimeter, aims to achieve a systematic uncertainty of 4 and 100 keV for the Z-pole and W-pair-threshold energies respectively. The ECM itself depends on the RF-cavity locations, beamstrahlung, longitudinal impedance, the Earth’s tides, opposite sign dispersion and possible collision offsets. Application of monochromatization schemes are envisaged at certain beam energies to reduce the energy spread. The latest results of studies of the energy calibration, polarization and monochromatization are reported here.
Data Science methods (many that are Bayesian based) are widely used in the physical sciences to estimate model parameters from experimental data, synthesize heterogeneous data, calibrate models, design experiments, and determine statistical significance of data. These methods provide a wealth of advantages over traditional analysis techniques because: 1) uncertainties are rigorously defined and propagated naturally through complex systems including covariance, 2) prior information is captured within the analysis framework (including rad-MHD and rad-hydro simulations), 3) competing models can be selected and/or ruled out using quantitative criteria, and 4) complex, heterogeneous data can be incorporated simultaneously. While these methods have been widely adopted as the gold standard in fields such as particle physics, astronomy, and biology, they have been slow to catch on in Inertial Confinement Fusion (ICF) and High Energy Density Physics (HEDP) research. Recently, several teams at LLNL, SNL, LANL, and the LLE have been exploring the use of these tools in their research and have found success. Here we propose that a concerted effort to consolidate these independent research efforts by developing and deploying common tools for use across the complex can revolutionize the way we approach data analysis, assimilation of theory and experiment, and decision making. The Bayesian formalism provides a means to accomplish this, but we are lacking certain infrastructure to make it happen on a large scale. Furthermore, once adopted, these techniques can be used to develop standards by which discoveries can be judged, similar to the so-called 5σ rule in high energy particle physics. Such standards may be used in the future to address the issue of unknown reproducibility in ICF and HED experiments caused by low shot rate and high cost per experiment. Our goals as a group are to advance the state of the art in HED measurement science by enabling: 1) better inferences from data with well-defined uncertainties, 2) better use of the data we have and continue to collect, 3) intelligent synthesis of data, 4) evaluation of the statistical significance of our data, and 5) informed decision making regarding the design of new experiments and instruments.
In this contribution to the Proceedings of the US Community Study on the Future of Particle Physics (Snowmass 2021) we review recent progress in the evaluation and application of the Energy-Energy Correlator (EEC) event shape observable in $e^+e^⁻$ annihilation, hadronic collisions, and deep inelastic scattering. The importance of EEC as a precision probe of the perturbative and non perturbative aspects of QCD dynamics is emphasized. It can be used to extract the strong coupling constant and to constrain TMD distribution functions. Closely related energy-correlation shape variables have also been used to tag boosted objects produced in high energy collisions. The opportunities to study EEC at the future Electron-Ion Collider are also highlighted.
Scientific foundation models hold great promise for advancing nuclear and particle physics by improving analysis precision and accelerating discovery. Yet, progress in this field is often limited by the lack of openly available large scale datasets, as well as standardized evaluation tasks and metrics. Furthermore, the specialized knowledge and software typically required to process particle physics data pose significant barriers to interdisciplinary collaboration with the broader machine learning community. This work introduces a large, openly accessible dataset of 10 million simulated proton-proton collisions, designed to support self-supervised training of foundation models. To facilitate ease of use, the dataset is provided in a common NumPy format. In addition, it includes 70,000 labeled examples spanning three well defined downstream tasks: track finding, particle identification, and noise tagging, to enable systematic evaluation of the foundation model's adaptability. The simulated data are generated using the Pythia Monte Carlo event generator at a center of mass energy of $\sqrt{s}$ = 200 GeV and processed with Geant4 to include realistic detector conditions and signal emulation in the sPHENIX Time Projection Chamber at the Relativistic Heavy Ion Collider, located at Brookhaven National Laboratory. This dataset resource establishes a common ground for interdisciplinary research, enabling machine learning scientists and physicists alike to explore scaling behaviors, assess transferability, and accelerate progress toward foundation models in nuclear and high energy physics. The complete simulation and reconstruction chain is reproducible with the sPHENIX software stack. All data and code locations are provided under Data Accessibility.
The Standard Model of Particle Physics is currently the best theory to describe the most basic building blocks of the Universe. However, it does not explain the complete picture. We need physics beyond the Standard Model.
Electron Ion Collider (EIC) is a particle accelerator facility planned for construction at Brookhaven National Laboratory on Long Island, New York by the United States Department of Energy. EIC will provide capabilities of colliding beams of polarized electrons with polarized beams of proton and light ions. EIC will be one of the largest and most sophisticated new accelerator facilities worldwide, and the only new large-scale accelerator facility planned for construction in the United States in the next few decades. The versatility, resolving power and intensity of EIC will present many new opportunities to address some of the crucial and fundamental open scientific questions in particle physics. This document provides an overview of the science case of EIC from the perspective of the high energy physics community.
Liquid argon time projection chambers (LArTPCs) offer millimeter-scale 3D images of particle trajectories, enabling precision studies of neutrino oscillation, detection of supernova and solar neutrinos, searches for exotic dark matter, and proton decay. Current approaches utilize supervised machine learning models, requiring extensive simulations of particle physics and detector response that can introduce bias. Self-supervised learning (SSL), a machine learning approach that learns useful representations of unlabeled data from the data itself, has significantly advanced how large datasets are utilized for representation learning; however, its potential for applications to sensory data in high precision particle physics experiments remains largely unexplored. We introduce the Point-based liquid argon masked autoencoder (PoLAr-MAE), a self-supervised framework that learns physically meaningful representations directly from unlabeled LArTPC images. PoLAr-MAE achieves remarkable data efficiency for a point-level segmentation task, outperforming fully supervised methods in low data regimes. Linear classifiers on model outputs demonstrate robust performance across multiple downstream tasks. Our results position sensor-level SSL as a practical foundation model strategy for LArTPCs.
Quantum information science is a growing field that promises to take computing into a new age of higher performance and larger scale computing as well as being capable of solving problems classical computers are incapable of solving. The outstanding issue in practical quantum computing today is scaling up the system while maintaining interconnectivity of the qubits and low error rates in qubit operations to be able to implement error correction and fault-tolerant operations. Trapped ion qubits offer long coherence times that allow error correction. However, error correction algorithms require large numbers of qubits to work properly. We can potentially create many thousands (or more) of qubits with long coherence states in a storage ring. For example, a circular radio-frequency quadrupole, which acts as a large circular ion trap and could enable larger scale quantum computing. Such a Storage Ring Quantum Computer (SRQC) would be a scalable and fault tolerant quantum information system, composed of qubits with very long coherence lifetimes. With computing demands potentially outpacing the supply of high-performance systems, quantum computing could bring innovation and scientific advances to particle physics and other DOE supported programs. Increased support of R$\&$D in large scale ion trap quantum computers would allow the timely exploration of this exciting new scalable quantum computer. The R$\&$D program could start immediately at existing facilities and would include the design and construction of a prototype SRQC. We invite feedback from and collaboration with the particle physics and quantum information science communities.
The cosmological constant presents one of the most fascinating and confounding problems in physics. A straightforward, seemingly robust prediction of quantum mechanics and general relativity is that the vacuum energy gravitates. Therefore, the cosmological constant should be enormous. It is minuscule. Since there is no understanding of why the cosmological constant is so small, it is important to test this idea in many different situations. In particular, given the span of distances in astronomy and particle physics, it is vital to test the gravitation of vacuum energy on as many distance scales as we can. Rydberg atoms open up a new set of distances for exploration. It is satisfying to measure the cosmological constant with an atom, but its main significance is extending measurements to microscopic distances. Here, too, there is no evidence of the gravitation of the vacuum. At scales of a micron and less, we place a limit of 7 GeV on the scale of gravitating vacuum energy, well below the scale of 100 GeV of the Standard Model of particle physics.
The International Muon Collider Collaboration (IMCC) [1] was established in 2020 following the recommendations of the European Strategy for Particle Physics (ESPP) and the implementation of the European Strategy for Particle Physics-Accelerator R&D Roadmap by the Laboratory Directors Group [2], hereinafter referred to as the the European LDG roadmap. The Muon Collider Study (MuC) covers the accelerator complex, detectors and physics for a future muon collider. In 2023, European Commission support was obtained for a design study of a muon collider (MuCol) [3]. This project started on 1st March 2023, with work-packages aligned with the overall muon collider studies. In preparation of and during the 2021-22 U.S. Snowmass process, the muon collider project parameters, technical studies and physics performance studies were performed and presented in great detail. Recently, the P5 panel [4] in the U.S. recommended a muon collider R&D, proposed to join the IMCC and envisages that the U.S. should prepare to host a muon collider, calling this their "muon shot". In the past, the U.S. Muon Accelerator Programme (MAP) [5] has been instrumental in studies of concepts and technologies for a muon collider.
Quantum chromodynamics (QCD) is the theory of strong interactions of quarks and gluons collectively called partons, the basic constituents of all nuclear matter. Its non-abelian character manifests in nature in the form of two remarkable properties: color confinement and asymptotic freedom. At high energies, perturbation theory can result in the growth and dominance of very gluon densities at small-x. If left uncontrolled, this growth can result in gluons eternally growing violating a number of mathematical bounds. The resolution to this problem lies by balancing gluon emissions by recombinating gluons at high energies: phenomena of gluon saturation. High energy nuclear and particle physics experiments have spent the past decades quantifying the structure of protons and nuclei in terms of their fundamental constituents confirming predicted extraordinary behavior of matter at extreme density and pressure conditions. In the process they have also measured seemingly unexpected phenomena. We will give a state of the art review of the underlying theoretical and experimental tools and measurements pertinent to gluon saturation physics. We will argue for the need of high energy electron-proton/ion colliders such as the proposed EIC (USA) and LHeC (Europe) to consolidate our knowledge of QCD knowledge in the small x kinematic domains.