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Measurements of angular distance and momentum ratio distributions in three-jet and $${\text {Z}}$$ + two-jet final states in $${\text {p}}{\text {p}}$$ collisions
Collinear (small-angle) and large-angle, as well as soft and hard radiations are investigated in three-jet and Z + two-jet events collected in proton-proton collisions at the LHC. The normalized production cross sections are measured as a function of the ratio of transverse momenta of two jets and their angular separation. The measurements in the three-jet and Z + two-jet events are based on data collected at a center-of-mass energy of 8TeV, corresponding to an integrated luminosity of 19.8fb -1 . The Z + two-jet events are reconstructed in the dimuon decay channel of the Z boson. The three-jet measurement is extended to include $\sqrt {s}$=13TeV data corresponding to an integrated luminosity of 2.3fb -1 . The results are compared to predictions from event generators that include parton showers, multiple parton interactions, and hadronization. The collinear and soft regions are in general well described by parton showers, whereas the regions of large angular separation are often best described by calculations using higher-order matrix elements.
Deep Learning Denoising Applied to Regional Distance Seismic Data in Utah
Seismic waveform data are generally contaminated by noise from various sources. Suppressing this noise effectively so that the remaining signal of interest can be successfully exploited remains a fundamental problem for the seismological community. To date, the most common noise suppression methods have been based on frequency filtering. These methods, however, are less effective when the signal of interest and noise share similar frequency bands. Inspired by source separation studies in the field of music information retrieval (Jansson et al., 2017) and a recent study in seismology (Zhu et al., 2019), we implemented a seismic denoising method that uses a trained deep convolutional neural network (CNN) model to decompose an input waveform into a signal of interest and noise. In our approach, the CNN provides a signal mask and a noise mask for an input signal. The short-time Fourier transform (STFT) of the estimated signal is obtained by multiplying the signal mask with the STFT of the input signal. To build and test the denoiser, we used carefully compiled signal and noise datasets of seismograms recorded by the University of Utah Seismograph Stations network. Results of test runs involving more than 9000 constructed waveforms suggest that on average the denoiser improves the signal-to-noise ratios (SNRs) by ~5 db, and that most of the recovered signal waveforms have high similarity with respect to the target waveforms (average correlation coefficient of ~0.80) and suffer little distortion. Application to real data suggests that our denoiser achieves on average a factor of up to ~2-5 improvement in SNR over band-pass filtering and can suppress many types of noise that band-pass filtering cannot. For individual waveforms, the improvement can be as high as ~15 db.
Gluon pseudo-distributions at short distances
We present the results that are necessary in the ongoing lattice calculations of the gluon parton distribution functions (PDFs) within the pseudo-PDF approach. We identify the two-gluon correlator functions that contain the invariant amplitude determining the gluon PDF in the light-cone z^2 \to 0 z 2 → 0 limit, and perform one-loop calculations in the coordinate representation in an explicitly gauge-invariant form. Ultraviolet (UV) terms, which contain \ln (-z^2) ln ( − z 2 ) -dependence cancel in the reduced Ioffe-time distribution (ITD), and we obtain the matching relation between the reduced ITD and the light-cone ITD. Using a kernel form, we get a direct connection between lattice data for the reduced ITD and the normalized gluon PDF.
From Long-distance Entanglement to Building a Nationwide Quantum Internet: Report of the DOE Quantum Internet Blueprint Workshop
A Quantum Path Forward. Today, many scientific experts recognize that building and scaling quantum-protected and enhanced communication networks are among the most important technological frontiers of the 21st century. The international research community perceives the construction of a first prototype global quantum network-the Quantum Internet-to be within reach over the next decade. In February 2020, the U.S Department of Energy (DOE)'s Office of Advanced Scientific Computing Research hosted the Quantum Internet Blueprint workshop to define a potential roadmap toward building the first nationwide quantum Internet. The workshop participants included representatives from DOE national laboratories, universities, industry, and other U.S. agencies with serious interests in quantum networking. The goal was to provide an outline of the essential research needed, detail any engineering and design barriers, and suggest a path forward to move from today's limited local network experiments to a viable, secure quantum Internet.
Navy Condition-Based Monitoring Project Update: Distances Overview [Slides]
A project update is provided, and various spaces, metrics, and measures are discussed.
Aerial Crosspolarized NQR-NMR: Buried Explosive Detection From a Safe Distance
Nuclear quadrupole resonance is a non-destructive detection and inspection technique with potential as a non-destructive test (NDT) tool. Establishment of the capability opens the door to its use in furthering the mission of the labs. There are many possible uses of the capability: explosive detection and stress/strain detection in epoxies are two of the more obvious and are the main results of this work. Enhancement of the signal-to-noise ratio (SNR) and improvements in the acquisition time of the experiment were key focuses of this work. These were achieved by combing special spin-lock pulse sequences with cross-polarization (CP) schemes to improve the signals with shorter acquisition times. A novel rotating magnetic field device was created to facilitate CP in the field. Implementation of these schemes provided a significant reduction in SNR/time.
LUNA Condition-Based Monitoring Update: Mahalanobis Distance for Excess Load and External Leak Cases [Slides]
Abstract not provided.
LUNA Condition Based Monitoring Update: Mahalanobis distance for Individual Damage Types [Slides]
Abstract not provided.
LUNA Condition Based Monitoring Update: Using Minimum of Mahalanobis Distances for Multi-Class Classification [Slides]
Abstract not provided.
Deep Learning Denoising Applied to Regional Distance Seismic Data in Utah.
Abstract not provided.
Applying Compression Distance Metrics to Seismic Data in Support of Global Nuclear Explosion Monitoring.
Abstract not provided.
Distances to Prompt Effects for a Nuclear Device
In 2010, nuclear weapon effects experts at Sandia National Laboratories (SNL) were asked to provide a quick reference document containing estimated prompt nuclear effects. This report is an update to the 2010 document that includes updated model assumptions. This report addresses only the prompt effects associated with a nuclear detonation (e.g., blast, thermal fluence, and prompt ionizing radiation). The potential medium- and longer-term health effects associated with nuclear fallout are not considered in this report because, in part, of the impracticality of making generic estimates given the high dependency of fallout predictions on the local meteorological conditions at the time of the event. The results included in this report also do not consider the urban environment (e.g., shielding by or collapse of structures) which may affect the extent of prompt effects. It is important to note that any operational recommendations made using the estimates in this report are limited by the generic assumptions considered in the analysis and should not replace analyses made for a specific scenario/device. Furthermore, nuclear effects experts (John Hogan, SNL, and Byron Ristvet, Defense Threat Reduction Agency (DTRA)) have indicated that the accuracy of effects predictions below 0.5 kilotons (kT) or 500 tons nuclear yield have greater uncertainty because of the limited data available for the prompt effects in this regime. The Specialized Hazard Assessment Response Capability (SHARC) effects prediction tool was used for these analyses. Specifically, the NUKE model within SHARC 2021 Version 10.2 was used. NUKE models only the prompt effects following a nuclear detonation. The algorithms for predicting range-to-output data contained within the NUKE model are primarily based on nuclear test effects data. Probits have been derived from nuclear test data and the U.S. Environmental Protection Agency (EPA) protective action guides. Probits relate the probability of a hazard (e.g., fatality or injury) caused by a given insult (e.g., overpressure, thermal fluence, dose level). Several probits have been built into SHARC to determine the fatality and injury associated with a given level of insult. Some of these probits differ with varying yield. Such probits were used to develop the tables and plots in this report.
Deep learning denoising applied to regional distance seismic data in Utah.
Abstract not provided.
Analysis to support revised distance between bulk liquid hydrogen systems and exposures.
Abstract not provided.
Long-Distance End-to-End Quantum State Transfer in a Transmon Qubit Network Connected Via Optical Photons.
Abstract not provided.
Parallel, Portable Algorithms for Distance-2 Maximal Independent Set and Graph Coarsening.
Abstract not provided.