The United States Department of Energy (DOE) is an executive department of the U.S. federal government that oversees U.S. national energy policy and manages the research and development of nuclear power and nuclear weapons in the United States. The DOE oversees U.S. nuclear weapons program, nuclear reactor production for the United States Navy, energy-related research, and domestic energy production and energy conservation. The DOE was created in 1977 in the aftermath of the 1973 oil crisis. It sponsors more physical sciences than any other U.S. federal agency, the majority of which is conducted through its system of National Laboratories. The DOE also directs research in genomics, with the Human Genome Project originating from a DOE initiative. The Department is headed by the Secretary of Energy, who reports directly to the president of the United States and is a member of his Cabinet. The current Secretary of Energy is Jennifer Granholm, who has served in the position since February 2021. The department's headquarters are in southwestern Washington, D.C., in the James V. Forrestal Building, with additional offices in Germantown, Maryland.S.S.S.
Metal carbides, nitrides, or carbonitrides of early transition metals, better known as MXenes, possess notable structural, electrical, and magnetic properties. Analyzing electronic structures by calculating structural stability, band structure, density of states, Bader charge transfer, and work functions utilizing first principle calculations, we revealed that titanium nitride MXenes, namely Ti2N and Ti3N2, have excess anionic electrons in their lattice voids, making them MXene electrides. Bulk Ti3N2 has competing antiferromagnetic (AFM) and ferromagnetic(FM) configurations with slightly more stable AFM configuration, while the Ti2N MXene is nonmagnetic. Although Ti3N2 favors AFM configuration with hexagonal crystal systems having 6/mmm point group symmetry, Ti3N2 does not support altermagnetism. The monolayer of the Ti3N2 MXene is a ferromagnetic electride. These unique properties of having non-nuclear interstitial anionic electrons in the electronic structure of titanium nitride MXene have not yet been reported in the literature. Density functional theory calculations show TiN is neither an electride, MXene, or magnetic.
Photometric redshifts of the source galaxies are a key source of systematic uncertainty in the Rubin Observatory Legacy Survey of Space and Time (LSST)'s galaxy clustering and weak lensing analysis, i.e. the 3 x 2pt analysis. This paper introduces a Fisher forecast code FISHERA2Z for the LSST Yr 10 (Y10) 3 x 2pt and cosmic shear analyses, utilizing a 15-parameter redshift distribution model, with one redshift bias, variance, and outlier rate per tomographic bin. FISHERA2Z employs the Core Cosmology Library CCL to compute the large-scale structure power spectrum and incorporates a four-parameter non-linear alignment model for intrinsic alignments. We evaluate the impact of marginalizing over redshift distribution parameters on weak lensing, forecast biases in cosmological parameters due to redshift errors, and assess cosmological parameter sensitivity to redshift systematic parameters using decision trees. The sensitivity study reveals that for LSST 3 x 2pt analysis, S-8 is most sensitive to the mean redshift of the fourth out of the five source tomographic bins, while other cosmological parameters possess different sensitivities. Additionally, we provide cosmological analysis forecasts based on different scenarios of spectroscopic training data sets. We find that the figures-of-merit for the cosmological results increase with the number of spectroscopic training galaxies, and with the completeness of the training set above z = 1 . 6, assuming the redshift information comes solely from the training set galaxies without other external constraints.
Hydrogen embrittlement severely degrades mechanical properties of austenitic stainless steels (γ-SS), yet the bulk deformation microstructures responsible for this phenomenon have not been directly observed. While the internal microstructure is essential to define metal plasticity, deformation, and fracture, no technique has been able to image the deep subsurface structure. In this work, we use dark-field X-ray microscopy (DFXM) to compare the structure of an annealed single crystal γ-SS sample to one with the same annealing conditions and subsequently hydrogen pre-charged in a high pressure and temperature environment. While the non-charged sample exhibited features characteristic of dislocations packed into boundaries, the pre-charged sample showed diffuse features with a distinctly broader rocking curve characteristic of a higher geometrically necessary dislocation (GND) density. Our results demonstrate the utility of DFXM to characterize the unique subsurface microstructures, offering opportunities for future studies to resolve how these features cause fracture or embrittlement.
Neural networks with sub-microsecond inference latency are required by many critical applications. Targeting such applications deployed on FPGAs, we present High Granularity Quantization (HGQ), a quantization-aware training framework that optimizes parameter bit-widths through gradient descent. Unlike conventional methods, HGQ determines the optimal bit-width for each parameter independently, making it suitable for hardware platforms supporting heterogeneous arbitrary precision arithmetic. In our experiments, HGQ shows superior performance compared to existing network compression methods, achieving orders of magnitude reduction in resource consumption and latency while maintaining the accuracy on several benchmark tasks. These improvements enable the deployment of complex models previously infeasible due to resource or latency constraints. HGQ is open-source1 and is used for developing next-generation trigger systems at the CERN ATLAS and CMS experiments for particle physics, enabling the use of advanced machine learning models for real-time data selection with sub-microsecond latency.
This Letter reports measurements of muon-neutrino disappearance and electron-neutrino appearance and the corresponding antineutrino processes between the two NOvA detectors in the NuMI neutrino beam. These measurements use a dataset with double the neutrino mode beam exposure that was previously analyzed, along with improved simulation and analysis techniques. A joint fit to these samples in the three-flavor paradigm results in the most precise single-experiment constraint on the atmospheric neutrino mass splitting, Δ m 32 2 = 2.43 1 − 0.034 + 0.036 ( − 2.47 9 − 0.036 + 0.036 ) × 10 − 3 eV 2 if the mass ordering is normal (inverted). In both orderings, a region close to maximal mixing with sin 2 θ 23 = 0.5 5 − 0.06 + 0.02 is preferred. The NOvA data show a mild preference for the normal mass ordering with a Bayes factor of 2.4 (corresponding to 70% of the posterior probability), indicating that the normal ordering is 2.4 times more probable than the inverted ordering. When incorporating a 2D Δ m 32 2 − sin 2 2 θ 13 constraint based on Daya Bay data, this preference strengthens to a Bayes factor of 6.6 (87%).
