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Reactor Monte Carlo simulations present numerous challenges including input file generation and processing output files for relevant results. Creating an input file for a heterogeneous core is further complicated when using depleted fuel. Performing perturbations to geometry and material characteristics exacerbates the challenge due to human errors if the input files are manually generated. The difficulty of generating input files is often encountered during the process of performing a reactor physics benchmark evaluation. These challenges were encountered in this study when performing a reactor physics benchmark evaluation for the Experimental Breeder Reactor II (EBR-II), a sodium cooled fast reactor. The EBR-II Monte Carlo input file uses more than 1,000 unique material specifications and ~8,000 regions. To address the input file generation challenge, a computer application was designed to automate the input file generation process. The application utilizes user input from a graphical user interface in conjunction with spreadsheets containing material and geometric data to generate Monte Carlo input files. The application, called MICKA, was used to generate input files to support the reactor physics benchmark evaluation for EBR-II. This paper describes the reactor configuration and provides information about the application design and capabilities for addressing challenges often encountered in reactor physics benchmark evaluations.
This is an auspicious moment in experimental particle physics - there are large data samples at the Tevatron and a new energy regime being explored at the Large Hadron Collider with ever larger data samples. The coincidence of these two events suggests that we will soon be able to address the question, what lies beyond the standard model? Particle physics's current understanding of the universe is embodied in it. The model has been tested to extreme precision - better than a part in ten thousand - but we suspect that it is only an approximation, and that physics beyond this standard model will appear in the data of the Tevatron and LHC in the near future. This brief review touches on the status of searches for new physics at the time of the conference.
The production cross section of inclusive isolated photons has been measured by the ALICE experiment at the CERN LHC in pp collisions at centre-of-momentum energy of $\mathbf {\sqrt{\textit{s}}=13}$ TeV collected during the LHC Run 2 data-taking period. The measurement is performed by combining the measurements of the electromagnetic calorimeter EMCal and the central tracking detectors ITS and TPC, covering a pseudorapidity range of and a transverse momentum range of |η γ | < 0.67 and a transverse momentum range 7 < p$^{γ}_{T}$ < 200 GeV/c. The result extends to lower p$^{γ}_{T}$ and x$^{γ}_{T}$ = 2p$^{γ}_{T}$/$\sqrt{s}$ ranges, the lowest x$^{γ}_{T}$ of any isolated photon measurements to date, extending significantly those measured by the ATLAS and CMS experiments towards lower p$^{γ}_{T}$ at the same collision energy with a small overlap between the measurements. The measurement is compared with next-to-leading order perturbative QCD calculations and the results from the ATLAS and CMS experiments as well as with measurements at other collision energies. The measurement and theory prediction are in agreement with each other within the experimental and theoretical uncertainties.
The properties of antihydrogen are expected to be identical to those of hydrogen, and any differences would constitute a profound challenge to the fundamental theories of physics. The most commonly discussed antiatom-based tests of these theories are searches for antihydrogen-hydrogen spectral differences (tests of CPT (charge-parity-time) invariance) or gravitational differences (tests of the weak equivalence principle). Here we, the ALPHA Collaboration, report a different and somewhat unusual test of CPT and of quantum anomaly cancellation. A retrospective analysis of the influence of electric fields on antihydrogen atoms released from the ALPHA trap finds a mean axial deflection of 4.1±3.4 mm for an average axial electric field of 0.51 V mm –1 . Combined with extensive numerical modelling, this measurement leads to a bound on the charge Qe of antihydrogen of Q=(–1.3±1.1±0.4) × 10 –8 . Here, e is the unit charge, and the errors are from statistics and systematic effects.
Atomic physics has played an important role throughout the history of experimental plasma physics. For example, accurate knowledge of atomic properties has been crucial for understanding the plasma energy balance and for diagnostic development. With the shift in magnetic fusion research toward high-temperature burning plasmas like those expected to be produced in the ITER tokamak, the atomic physics of tungsten has become important. Tungsten will be a constituent of ITER plasmas because of its use as a plasma-facing material able to withstand high heat loads with lower tritium retention than other possible materials. Already, ITER diagnostics are being developed based on using tungsten radiation. In particular, the ITER Core Imaging X-ray Spectrometer (CIXS), which is designed to measure the core ion temperature and bulk plasma motion, is being based on the x-ray emission of neonlike tungsten ions (W{sup 64+}). In addition, tungsten emission will at ITER be measured by extreme ultraviolet (EUV) and optical spectrometers to determine its concentration in the plasma and to assess power loss and tungsten sputtering rates. On present-day tokamaks tungsten measurements are therefore being performed in preparation of ITER. Tungsten has very complex spectra and most are still unknown. The WOLFRAM project at Livermore aims to produce data for tungsten in various spectral bands: Lshell x-ray emission for CIXS development, soft x-ray and EUV M- and N-shell tungsten emission for understanding the edge radiation from ITER plasmas as well as from contemporary tokamaks, and O-shell emission for developing spectral diagnostics of the ITER divertor.
Shock waves can achieve extreme states of pressure and temperature, of particular interest because those conditions can result in non-equilibrium material dynamics that evolve on ultrafast timescales. Examples of such phenomena include shock-induced chemistry and phase transitions. Traditional plate impact methods lack the necessary time and space resolution needed to observe the onset of ultrafast nanoscale phenomena. Sub-picosecond time scale and nanometer spatial scale shock compression and diagnostics methods have been developed to surmount such difficulties. In conclusion, this paper reviews a number of nanoscale shock wave generation methods, as well as the diagnostics that are applicable at these restrictive time and spatial scales.
While the spectrum of pure hydrogen plasmas at low densities seems well understood, at higher densities this is not the case. In this domain the line broadening can be extreme and the lines overlap, making the determination of the continuum problematic. The surfaces of white dwarf stars (WDs) fall into this high-density category. Observationally, we find a systematic offset between the masses of WDs derived using the gravitational-redshift method and those using the spectral line-shape fitting method (Falcon et al. 2010, ApJ, 712,585). Given the straightforward nature of the gravitational-redshift method, this discrepancy points toward incompleteness in the modeling of the broadening of hydrogen lines. The resulting uncertainty in mass hinders the many uses of WDs in astronomy and physics: for example, as Galactic chronometers (Winget et al. 1987, ApJL, 315, 77), tracers of stellar evolution, and probes of dense Coulomb plasmas (Winget et al. 2009, ApJL, 693, 6). This has motivated recent attempts to improve the theoretical line profiles used in white dwarf atmosphere models by independent groups, including us. Laboratory benchmarks are essential for assessing the viability of these new theories (Falcon et al. 2013, HEDP, 9, 82). Therefore, our goal is to make laboratory benchmark measurements of atomic line shapes in H – eventually He and C and O as well--for comparison with the lines observed in the photospheres of white dwarf stars.
Within the next decade, experimental High Energy Physics (HEP) will enter a new era of scientific discovery through a set of targeted programs recommended by the Particle Physics Project Prioritization Panel (P5), including the upcoming High Luminosity Large Hadron Collider (LHC) HL-LHC upgrade and the Deep Underground Neutrino Experiment (DUNE). These efforts in the Energy and Intensity Frontiers will require an unprecedented amount of computational capacity on many fronts including Monte Carlo (MC) detector simulation. In order to alleviate this impending computational bottleneck, the Celeritas MC particle transport code is designed to leverage the new generation of heterogeneous computer architectures, including the exascale computing power of U.S. Department of Energy (DOE) Leadership Computing Facilities (LCFs), to model targeted HEP detector problems at the full fidelity of Geant4. This paper presents the planned roadmap for Celeritas, including its proposed code architecture, physics capabilities, and strategies for integrating it with existing and future experimental HEP computing workflows.
The Continuous Electron Beam Accelerator Facility (CEBAF) and associated experimental equipment at Jefferson Lab comprise a unique facility for experimental nuclear physics. Furthermore, this facility is presently being upgraded, which will enable a new experimental program with substantial discovery potential to address important topics in nuclear, hadronic, and electroweak physics. Further in the future, it is envisioned that the Laboratory will evolve into an electron-ion colliding beam facility.
Despite its importance for stockpile stewardship or nuclear forensics, data on fission is fragmentary: most experiments have only been performed on stable actinide nuclei and are often incomplete, and theoretical simulations often contain many parameters hard to constrain, which results in large uncertainties. Consequently, nuclear libraries have large gaps for important isotopes. The goal of this project was to prepare to take advantage of the unprecedented yields of radioactive isotopes at the upcoming DOE Facility for Rare Ion Beams and to leverage recent progress in the field of machine learning to develop a comprehensive program of fission studies that could address some of the most pressing problems in nuclear fission over the next several decades. Our project leveraged synergies between experimental nuclear physics, nuclear theory, and data science expertise at LLNL. On the experimental front, our main objective was to develop in-house expertise for inverse kinematics reactions with relativistic beams as well as to field and test new detectors to perform correlated measurements of fission properties. On the theoretical side, the objective was to develop a novel, high-fidelity and scalable approach of fissionfragment calculations based on emulating computationally expensive, quantum-mechanical calculations of nuclear properties with deep neural networks.
More than three decades ago, the European Muon Collaboration published a surprising result on the spin structure of the proton: the spins of its three quark components account for only a small part of the spin of the proton. Furthermore, ever since, theoretical and experimental progress has been made in understanding the origins of the proton spin. In this review, we discuss what has been learned so far, what is still missing and what could be learned from the upcoming experiments, including the Jefferson Lab 12GeV upgrade and the proposed Electron-Ion Collider. In particular, we focus on first-principles calculations and experimental measurements of the total gluon helicity ΔG, and the quark and gluon orbital angular momenta.
Jet flavour tagging enables the identification of jets originating from heavy-flavour quarks in proton–proton collisions at the Large Hadron Collider, playing a critical role in its physics programmes. This paper presents GN2, a transformer-based flavour tagging algorithm deployed by the ATLAS Collaboration that represents a different methodology compared to previous approaches. Designed to classify jets based on the flavour of their constituent particles, GN2 processes low-level tracking information in an end-to-end architecture and incorporates physics-informed auxiliary training objectives to enhance both interpretability and performance. Its performance is validated in both simulation and collision data. The measured c-jet (light-jet) rejection in data is improved by a factor of 3.5 (1.8) for a 70% b-jet tagging efficiency, compared to the previous algorithm. GN2 provides substantial benefits for physics analyses involving heavy-flavour jets, such as measurements of Higgs boson pair production and the couplings of bottom and charm quarks to the Higgs boson, and demonstrates the impact of advanced machine learning methods in experimental particle physics.
The research at Yale funded by the DOE Office of High Energy Physics, with grant title Yale High Energy Physics Research is centered at all the frontier areas of elementary particle physics, both experimental and theoretical. Among the most compelling are the continued exciting study of the energy frontier at the LHC, the physics of neutrinos and neutrino mixing, and the properties of dark energy in the universe. These activities offer promise for providing the answers to some of the most fundamental questions in science, such as the origin of mass and the baryon asymmetry in the Universe, and the cause of its accelerated expansion. In neutrino physics Profs. Fleming and Heeger are leading several experiments using reactor and accelerator neutrinos and participate in research and development towards precision neutrino detection. In flavor physics, Prof. Demers contributes to the physics selection and design of the mu2e Experiment. In collider physics Profs. Baker, Demers, and Tipton’s efforts span searches and measurements for the Higgs, exotics, and planned upgrades. In astrophysics Profs. Baltay and Padmanabhan are exploring the Cosmic Frontier. Prof. Baltay plays a leading role in La Silla/QUEST and the Dark Energy Spectroscopic Survey (DESI). Prof. Padmanabhan uses large galaxy surveys to constrain dark energy through critical participation in the Baryon Oscillation Spectroscopic Survey (BOSS) and in DESI. In theoretical research, Prof. Goldberger has made contributions to many fields including electroweak symmetry breaking, relativistic and non-relativistic conformal field theories, cosmology and inflation, and general relativity. At Yale, we enjoy a close collaboration and shared resources between different experimental groups, and between theory and experimental communities.
As part of the Scientific Discovery through Advanced Computing (SciDAC) program, the Quantum Chromodynamics Nuclear Tomography (QuantOM) project aims to analyze data from Deep Inelastic Scattering (DIS) experiments conducted at Jefferson Lab and the upcoming Electron Ion Collider. The DIS data analysis is performed on an event-level by combining the input from theoretical and experimental nuclear physics into a single, composable workflow. The optimization itself (I.e. fitting the experimental data with theoretical predictions) is carried out by a machine / deep learning algorithm. The size of the acquired DIS data as well as the complexity of the workflow itself require that the analysis is performed across multiple GPUs on high performance computing systems, such as Polaris at Argonne National Laboratory. This presentation discusses the novelties and challenges that came along with parallelizing this workflow. Recent results are compared to common distributed training techniques.