3,717 research outputs found
High Performance Data Acquisition and Analysis Routines for the Nab Experiment
Probes of the Standard Model of particle physics are pushing further and further into the so-called “precision frontier”. In order to reach the precision goals of these experiments, a combination of elegant experimental design and robust data acquisition and analysis is required. Two experiments that embody this philosophy are the Nab and Calcium-45 experiments. These experiments are probing the understanding of the weak interaction by examining the beta decay of the free neutron and Calcium-45 respectively. They both aim to measure correlation parameters in the neutron beta decay alphabet, a and b. The parameter a, the electron-neutrino correlation coefficient, is sensitive to λ, the ratio of the axial-vector and vector coupling strengths in the decay of the free neutron. This parameter λ, in tandem with a precision measurement of the neutron lifetime τ , provides a measurement of the matrix element Vud from the CKM quark mixing matrix. The CKM matrix, as a rotation matrix, must be unitary. Probes of Vud and Vus in recent years have revealed tension in this unitarity at the 2.2σ level. The measurement of a via decay of free cold neutrons serves as an additional method of extraction for Vud that is sensitive to a different set of systematic effects and as such is an excellent probe into the source of the deviation from unitarity. The parameter b, the Fierz interference term, appears as a distortion in the mea- sured electron energy spectra from beta decay. This parameter, if non-zero, would indicate the existence of Scalar and/or Tensor couplings in the Weak interaction which according to the Standard Model is purely Vector minus Axial-Vector. This is therefore a search for physics beyond the standard model, BSM, physics search. The Nab and Calcium-45 experiments probe these parameters with a combination of elegant experimental design and brute force collection and analysis of large amounts of digitized detector data. These datasets, particularly in the case of the Nab experiment, are anticipated to span multiple petabytes of data and will require high performance online analysis and precision offline analysis routines in order to reach the experimental goals. Of particular note are the requirements for better than 3 keV energy resolution and an understanding of the uncertainty in the mean timing bias for the detected particles within 300 ps. Presented in this dissertation is an overview of the experiments and their design, a description of the data acquisition systems and analysis routines that have been developed to support the experiments, and a discussion of the data analysis performed for the Calcium-45 experiment
An architecture for efficient gravitational wave parameter estimation with multimodal linear surrogate models
The recent direct observation of gravitational waves has further emphasized
the desire for fast, low-cost, and accurate methods to infer the parameters of
gravitational wave sources. Due to expense in waveform generation and data
handling, the cost of evaluating the likelihood function limits the
computational performance of these calculations. Building on recently developed
surrogate models and a novel parameter estimation pipeline, we show how to
quickly generate the likelihood function as an analytic, closed-form
expression. Using a straightforward variant of a production-scale parameter
estimation code, we demonstrate our method using surrogate models of
effective-one-body and numerical relativity waveforms. Our study is the first
time these models have been used for parameter estimation and one of the first
ever parameter estimation calculations with multi-modal numerical relativity
waveforms, which include all l <= 4 modes. Our grid-free method enables rapid
parameter estimation for any waveform with a suitable reduced-order model. The
methods described in this paper may also find use in other data analysis
studies, such as vetting coincident events or the computation of the
coalescing-compact-binary detection statistic.Comment: 10 pages, 3 figures, and 1 tabl
Scaling full seismic waveform inversions
The main goal of this research study is to scale full seismic waveform inversions using the adjoint-state method to the data volumes that are nowadays available in seismology. Practical issues hinder the routine application of this, to a certain extent theoretically well understood, method. To a large part this comes down to outdated or flat out missing tools and ways to automate the highly iterative procedure in a reliable way.
This thesis tackles these issues in three successive stages. It first introduces a modern and properly designed data processing framework sitting at the very core of all the consecutive developments. The ObsPy toolkit is a Python library providing a bridge for seismology into the scientific Python ecosystem and bestowing seismologists with effortless I/O and a powerful signal processing library, amongst other things.
The following chapter deals with a framework designed to handle the specific data management and organization issues arising in full seismic waveform inversions, the Large-scale Seismic Inversion Framework. It has been created to orchestrate the various pieces of data accruing in the course of an iterative waveform inversion.
Then, the Adaptable Seismic Data Format, a new, self-describing, and scalable data format for seismology is introduced along with the rationale why it is needed for full waveform inversions in particular and seismology in general.
Finally, these developments are put into service to construct a novel full seismic waveform inversion model for elastic subsurface structure beneath the North American continent and the Northern Atlantic well into Europe. The spectral element method is used for the forward and adjoint simulations coupled with windowed time-frequency phase misfit measurements. Later iterations use 72 events, all happening after the USArray project has commenced, resulting in approximately 150`000 three components recordings that are inverted for. 20 L-BFGS iterations yield a model that can produce complete seismograms at a period range between 30 and 120 seconds while comparing favorably to observed data
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Digital-high definition television servicing curriculum for Santa Ana Community College
The purpose of this project was to develop a semester length community college curriculum for a course in the theory and servicing of digital-high definition television for the students in the service technology field of electronics at Santa Ana Community College in Santa Ana, California. Additionally, it is designed with the current electronic service industry in mind
Space Station communications and tracking systems modeling and RF link simulation
In this final report, the effort spent on Space Station Communications and Tracking System Modeling and RF Link Simulation is described in detail. The effort is mainly divided into three parts: frequency division multiple access (FDMA) system simulation modeling and software implementation; a study on design and evaluation of a functional computerized RF link simulation/analysis system for Space Station; and a study on design and evaluation of simulation system architecture. This report documents the results of these studies. In addition, a separate User's Manual on Space Communications Simulation System (SCSS) (Version 1) documents the software developed for the Space Station FDMA communications system simulation. The final report, SCSS user's manual, and the software located in the NASA JSC system analysis division's VAX 750 computer together serve as the deliverables from LinCom for this project effort
Containing Analog Data Deluge at Edge through Frequency-Domain Compression in Collaborative Compute-in-Memory Networks
Edge computing is a promising solution for handling high-dimensional,
multispectral analog data from sensors and IoT devices for applications such as
autonomous drones. However, edge devices' limited storage and computing
resources make it challenging to perform complex predictive modeling at the
edge. Compute-in-memory (CiM) has emerged as a principal paradigm to minimize
energy for deep learning-based inference at the edge. Nevertheless, integrating
storage and processing complicates memory cells and/or memory peripherals,
essentially trading off area efficiency for energy efficiency. This paper
proposes a novel solution to improve area efficiency in deep learning inference
tasks. The proposed method employs two key strategies. Firstly, a Frequency
domain learning approach uses binarized Walsh-Hadamard Transforms, reducing the
necessary parameters for DNN (by 87% in MobileNetV2) and enabling
compute-in-SRAM, which better utilizes parallelism during inference. Secondly,
a memory-immersed collaborative digitization method is described among CiM
arrays to reduce the area overheads of conventional ADCs. This facilitates more
CiM arrays in limited footprint designs, leading to better parallelism and
reduced external memory accesses. Different networking configurations are
explored, where Flash, SA, and their hybrid digitization steps can be
implemented using the memory-immersed scheme. The results are demonstrated
using a 65 nm CMOS test chip, exhibiting significant area and energy savings
compared to a 40 nm-node 5-bit SAR ADC and 5-bit Flash ADC. By processing
analog data more efficiently, it is possible to selectively retain valuable
data from sensors and alleviate the challenges posed by the analog data deluge.Comment: arXiv admin note: text overlap with arXiv:2307.03863,
arXiv:2309.0177
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