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Results for “SIGNAL DETECTION”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Halo-independent tests of dark matter direct detection signals: local DM density, LHC, and thermal freeze-out

From an assumed signal in a Dark Matter (DM) direct detection experiment a lower bound on the product of the DM-nucleon scattering cross section and the local DM density is derived, which is independent of the local DM velocity distribution. This can be combined with astrophysical determinations of the local DM density. Within a given particle physics model the bound also allows a robust comparison of a direct detection signal with limits from the LHC. Furthermore, the bound can be used to formulate a condition which has to be fulfilled if the particle responsible for the direct detection signal is a thermal relic, regardless of whether it constitutes all DM or only part of it. We illustrate the arguments by adopting a simplified DM model with a Z{sup ′} mediator and assuming a signal in a future xenon direct detection experiment.

43 PARTICLE ACCELERATORS↗
DOE OSTI2019

Multi-component background learning automates signal detection for spectroscopic data

Abstract Automated experimentation has yielded data acquisition rates that supersede human processing capabilities. Artificial Intelligence offers new possibilities for automating data interpretation to generate large, high-quality datasets. Background subtraction is a long-standing challenge, particularly in settings where multiple sources of the background signal coexist, and automatic extraction of signals of interest from measured signals accelerates data interpretation. Herein, we present an unsupervised probabilistic learning approach that analyzes large data collections to identify multiple background sources and establish the probability that any given data point contains a signal of interest. The approach is demonstrated on X-ray diffraction and Raman spectroscopy data and is suitable to any type of data where the signal of interest is a positive addition to the background signals. While the model can incorporate prior knowledge, it does not require knowledge of the signals since the shapes of the background signals, the noise levels, and the signal of interest are simultaneously learned via a probabilistic matrix factorization framework. Automated identification of interpretable signals by unsupervised probabilistic learning avoids the injection of human bias and expedites signal extraction in large datasets, a transformative capability with many applications in the physical sciences and beyond.

Ament, Sebastian E.↗
DOE OSTI2015

A halo-independent lower bound on the dark matter capture rate in the Sun from a direct detection signal

We show that a positive signal in a dark matter (DM) direct detection experiment can be used to place a lower bound on the DM capture rate in the Sun, independent of the DM halo. For a given particle physics model and DM mass we obtain a lower bound on the capture rate independent of the local DM density, velocity distribution, galactic escape velocity, as well as the scattering cross section. We illustrate this lower bound on the capture rate by assuming that upcoming direct detection experiments will soon obtain a significant signal. When comparing the lower bound on the capture rate with limits on the high-energy neutrino flux from the Sun from neutrino telescopes, we can place upper limits on the branching fraction of DM annihilation channels leading to neutrinos. With current data from IceCube and Super-Kamiokande non-trivial limits can be obtained for spin-dependent interactions and direct annihilations into neutrinos. In some cases also annihilations into ττ or bb start getting constrained. For spin-independent interactions current constraints are weak, but they may become interesting for data from future neutrino telescopes.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1964

Stability in signal detection problems.

Stability in signal detection and measurement based on statistical inference made from values of functional on sample space

STATISTICAL COMMUNICATION THEORY↗
DOE OSTI2014

TH-C-17A-01: Imaging Sensor Comparison for Real-Time Cherenkov Signal Detection From Tissue for Treatment Verification

Purpose: To identify the optimum imaging sensor for a clinical system that would provide real-time imaging of the surface beam profile on patients as novel visual information to radiation therapy technologists, and more rapidly collect clinical data for large-scale studies of Cherenkov applications in radiotherapy. Methods: Four camera types, CMOS, CCD, ICCD and EMICCD, were tested to determine proficiency in the detection of Cherenkov signal in the clinical radiotherapy setting, and subsequent maximum supportable frame rate. Where possible, time-gating between the trigger signal from the LINAC and the intensifiers was implemented to detect signal with room lighting conditions comparable to patient treatment scenarios. A solid water phantom was imaged by the EM-ICCD and ICCD to evaluate the minimum number of accumulations-on-chip required for adequate Cherenkov detection, defined as >200% electron counts per pixel over background signal. Additionally, an ICCD and EM-ICCD were used clinically to image patients undergoing whole-breast radiation therapy, to understand the impact of the resolution limitation of the EM-ICCD. Results: The intensifier-coupled cameras performed best at imaging Cherenkov signal, even with room lights on, which is essential for patient comfort. The tested EM-ICCD was able to support single-shot imaging and frame rates of 30 fps, however, the current maximum resolution of 512 × 512 pixels was restricting. The ICCD used in current clinical trials was limited to 4.7 fps at a 1024 × 1024 resolution. An intensifier with higher quantum efficiency at the entrance photocathode in the red wavelengths (30% QE vs current 7%) promises 16 fps at the same resolution at lower cost than the EM-ICCD. Conclusion: The ICCD with the better red wavelength QE intensifier was determined to be the best suited commercial-off-the-shelf camera to detect real-time Cherenkov signal and provide the best potential for real-time display of radiation dose on the skin during treatment. Funding is from grants from the NIH numbers R01CA109558 and R21EB017559.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1994

Signal detection amid noise with known statistics

Conventional methods of signal detection are based on the comparison of a local signal-to-noise ratio S/N to some threshold value. We present a new method that compares instead the histogram of the data with the one expected for background noise alone. Signals are detected from discrepancies between the two. We expect it to be applicable in the detection of signals from any data whose background statistics is known. To illustrate the method, we apply it to the case of photon-limited imaging data where the underlying background is Poissonian. Numerical simulations are used to check the efficiency of our method, finding that for signals with low S/N (less than 5), it detects signals with a higher degree of reliability than the conventional method.

Zepka, A. F.↗
DOE OSTI2014

Note: On-line weak signal detection via adaptive stochastic resonance

We design an instrument with a novel embedded adaptive stochastic resonance (SR) algorithm that consists of a SR module and a digital zero crossing detection module for on-line weak signal detection in digital signal processing applications. The two modules are responsible for noise filtering and adaptive parameter configuration, respectively. The on-line weak signal detection can be stably achieved in seconds. The prototype instrument exhibits an advance of 20 dB averaged signal-to-noise ratio and 5 times averaged adjust R-square as compared to the input noisy signal, in considering different driving frequencies and noise levels.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1977

Walsh transforms and signal detection

The detection of signals using Walsh power spectral estimates is analyzed. In addition, a generalization of this method of estimation is evaluated. The conclusion is that Walsh transforms are not suitable tools for the detection of weak signals in noise.

Welch, L. R.↗