Towards increasing the performance of FTICR-MS with signal detection at frequency multiples: Signal theory and numerical study
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Wu, Qinghao (ORCID:000000031665567X)↗
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Results for “SIGNAL DETECTION”
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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↗
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.↗
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↗
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↗
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 ↗
Seismic Signal Detection on International Monitoring System 3-Component Stations using PhaseNet
In this report we discuss training a deep learning seismic signal detection model on 3-component stations from the International Monitoring System (IMS) using the PhaseNet architecture. Using 14 years of associated signals from the International Data Centre’s (IDC) Late Event Bulletin (LEB), we auto-curated training data consisting of signal windows containing associated arrivals, and noise windows that contain no LEB-associated signals. We trained several models using different waveform window durations (30 seconds and 100 seconds), with and without bandpass filtering. We evaluated the effectiveness of our models using associated signals from the Unconstrained Global Event Bulletin (UGEB) and found that several of our models outperformed the signal detections from the IDC’s Selected Event List 3 (SEL3) arrival table. The SEL3 bulletin evaluated on the UGEB dataset with 100-second waveform windows registered a precision and recall of .15 and .48, respectively, versus .19 and .59 for our filtered-data model. For the 30-second waveform window dataset, the SEL3 bulletin achieved a precision and recall of .31 and .47, respectively, versus .32 and .60 for our filtered-data model. Finally, our models detected signals from all source-to-receiver distances, suggesting it is feasible to use a single PhaseNet model for the IMS network.
58 GEOSCIENCES↗
Time-frequency energy density precipitation method for time-of-flight extraction of narrowband Lamb wave detection signals
The time-of-flight of the Lamb wave provides an important basis for defect evaluation in metal plates and is the input signal for Lamb wave tomographic imaging. However, the time-of-flight can be difficult to acquire because of the Lamb wave dispersion characteristics. This work proposes a time-frequency energy density precipitation method to accurately extract the time-of-flight of narrowband Lamb wave detection signals in metal plates. In the proposed method, a discrete short-time Fourier transform is performed on the narrowband Lamb wave detection signals to obtain the corresponding discrete time-frequency energy density distribution. The energy density values at the center frequency for all discrete time points are then calculated by linear interpolation. Next, the time-domain energy density curve focused on that center frequency is precipitated by least squares fitting of the calculated energy density values. Finally, the peak times of the energy density curve obtained relative to the initial pulse signal are extracted as the time-of-flight for the narrowband Lamb wave detection signals. An experimental platform is established for time-of-flight extraction of narrowband Lamb wave detection signals, and sensitivity analysis of the proposed time-frequency energy density precipitation method is performed in terms of propagation distance, dispersion characteristics, center frequency, and plate thickness. For comparison, the widely used Hilbert–Huang transform method is also implemented for time-of-flight extraction. The results show that the time-frequency energy density precipitation method can accurately extract the time-of-flight with relative error of <1% and thus can act as a universal time-of-flight extraction method for narrowband Lamb wave detection signals.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Uncertainty Analysis in Signal Detection
Presentation discussed uncertainty analysis in signal detection and the inverse problem for time of arrival (ToA).
97 MATHEMATICS AND COMPUTING↗
Reducing radiation dose to the female breast during CT coronary angiography: A simulation study comparing breast shielding, angular tube current modulation, reduced kV, and partial angle protocols using an unknown-location signal-detectability metric
Purpose: The authors compared the performance of five protocols intended to reduce dose to the breast during computed tomography (CT) coronary angiography scans using a model observer unknown-location signal-detectability metric.Methods: The authors simulated CT images of an anthropomorphic female thorax phantom for a 120 kV reference protocol and five “dose reduction” protocols intended to reduce dose to the breast: 120 kV partial angle (posteriorly centered), 120 kV tube-current modulated (TCM), 120 kV with shielded breasts, 80 kV, and 80 kV partial angle (posteriorly centered). Two image quality tasks were investigated: the detection and localization of 4-mm, 3.25 mg/ml and 1-mm, 6.0 mg/ml iodine contrast signals randomly located in the heart region. For each protocol, the authors plotted the signal detectability, as quantified by the area under the exponentially transformed free response characteristic curve estimator (A-caret{sub FE}), as well as noise and contrast-to-noise ratio (CNR) versus breast and lung dose. In addition, the authors quantified each protocol's dose performance as the percent difference in dose relative to the reference protocol achieved while maintaining equivalent A-caret{sub FE}.Results: For the 4-mm signal-size task, the 80 kV full scan and 80 kV partial angle protocols decreased dose to the breast (80.5% and 85.3%, respectively) and lung (80.5% and 76.7%, respectively) with A-caret{sub FE} = 0.96, but also resulted in an approximate three-fold increase in image noise. The 120 kV partial protocol reduced dose to the breast (17.6%) at the expense of increased lung dose (25.3%). The TCM algorithm decreased dose to the breast (6.0%) and lung (10.4%). Breast shielding increased breast dose (67.8%) and lung dose (103.4%). The 80 kV and 80 kV partial protocols demonstrated greater dose reductions for the 4-mm task than for the 1-mm task, and the shielded protocol showed a larger increase in dose for the 4-mm task than for the 1-mm task. In general, the CNR curves indicate a similar relative ranking of protocol performance as the corresponding A-caret{sub FE} curves, however, the CNR metric overestimated the performance of the shielded protocol for both tasks, leading to corresponding underestimates in the relative dose increases compared to those obtained when using the A-caret{sub FE} metric.Conclusions: The 80 kV and 80 kV partial angle protocols demonstrated the greatest reduction to breast and lung dose, however, the subsequent increase in image noise may be deemed clinically unacceptable. Tube output for these protocols can be adjusted to achieve a more desirable noise level with lesser breast dose savings. Breast shielding increased breast and lung dose when maintaining equivalent A-caret{sub FE}. The results demonstrated that comparisons of dose performance depend on both the image quality metric and the specific task, and that CNR may not be a reliable metric of signal detectability.
61 RADIATION PROTECTION AND DOSIMETRY↗
Dynamic Networks Experiment 2: Measuring Associator Sensitivity to Signal Detection Errors
Using the Dynamic Networks Experiment 2 (DNE2) human-analyst event bulletin picks as a baseline signal detection dataset, we generate 47 additional datasets by gradually reducing their accuracy and completeness by randomly removing DNE2 picks, changing the initial phase labels from P to S and vice-versa, and injecting noise detections to simulate real-world signal detection algorithms.
58 GEOSCIENCES↗
IDC Re-Engineering Phase 2 Iteration E2. Draft Component Interface Specification: Signal Detection Control
This report contains a draft Component Interface Specification (CIS) for an application controlling signal detection calculations. It is an example of the contents and level of detail of information in a CIS. The classes, interfaces, and data types in this report reflect current understanding of system concepts and potential implementation patterns but should not be interpreted as containing the finalized interface specifications, classes, etc. The primary purpose of this report is to discuss the potential OSD APIs represented in class diagrams for the Waveform, QC Mask, and Signal Detection Repository interfaces.
42 ENGINEERING↗
Real-time signal detection for Cyclotron Radiation Emission Spectroscopy measurements using antenna arrays
Cyclotron Radiation Emission Spectroscopy (CRES) is a technique for precision measurement of the energies of charged particles, which is being developed by the Project 8 Collaboration to measure the neutrino mass using tritium beta-decay spectroscopy. Project 8 seeks to use the CRES technique to measure the neutrino mass with a sensitivity of 40 meV, requiring a large supply of tritium atoms stored in a multi-cubic meter detector volume. Antenna arrays are one potential technology compatible with an experiment of this scale, but the capability of an antenna-based CRES experiment to measure the neutrino mass depends on the efficiency of the signal detection algorithms. Here, in this paper, we develop efficiency models for three signal detection algorithms and compare them using simulations from a prototype antenna-based CRES experiment as a case-study. The algorithms include a power threshold, a matched filter template bank, and a neural network based machine learning approach, which are analyzed in terms of their average detection efficiency and relative computational cost. It is found that significant improvements in detection efficiency and, therefore, neutrino mass sensitivity are achievable, with only a moderate increase in computation cost, by utilizing either the matched filter or machine learning approach in place of a power threshold, which is the baseline signal detection algorithm used in previous CRES experiments by Project 8.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Universal Collaboration Strategies for Signal Detection: A Sparse Learning Approach
Here, this paper considers the problem of high-dimensional signal detection in a large distributed network whose nodes can collaborate with their one-hop neighboring nodes (spatial collaboration). We assume that only a small subset of nodes communicate with the fusion center (FC). We design optimal collaboration strategies which are universal for a class of deterministic signals. By establishing the equivalence between the collaboration strategy design problem and sparse principal component analysis (PCA), we solve the problem efficiently and evaluate the impact of collaboration on detection performance.
97 MATHEMATICS AND COMPUTING↗
System and method for ultrafast optical signal detecting via a synchronously coupled anamorphic light pulse encoded laterally
In one general embodiment, a method for ultrafast optical signal detecting is provided. In operation, a first optical input signal is propagated through a first wave guiding layer of a waveguide. Additionally, a second optical input signal is propagated through a second wave guiding layer of the waveguide. Furthermore, an optical control signal is applied to a top of the waveguide, the optical control signal being oriented diagonally relative to the top of the waveguide such that the application is used to influence at least a portion of the first optical input signal propagating through the first wave guiding layer of the waveguide. In addition, the first and the second optical input signals output from the waveguide are combined. Further, the combined optical signals output from the waveguide are detected. In another general embodiment, a system for ultrafast optical signal recording is provided comprising a waveguide including a plurality of wave guiding layers, an optical control source positioned to propagate an optical control signal towards the waveguide in a diagonal orientation relative to a top of the waveguide, at least one optical input source positioned to input an optical input signal into at least a first and a second wave guiding layer of the waveguide, and a detector for detecting at least one interference pattern output from the waveguide, where at least one of the interference patterns results from a combination of the optical input signals input into the first and the second wave guiding layer. Furthermore, propagation of the optical control signal is used to influence at least a portion of the optical input signal propagating through the first wave guiding layer of the waveguide.
47 OTHER INSTRUMENTATION↗
AI ensemble for signal detection of higher order gravitational wave modes of quasi-circular, spinning, non-precessing binary black hole mergers
We introduce spatiotemporal-graph models that concurrently process data from the twin advanced LIGO detectors and the advanced Virgo detector. We trained these AI classifiers with 2.4 million IMRPhenomXPHM waveforms that describe quasi-circular, spinning, non-precessing binary black hole mergers with component masses m{1,2}∈[3M⊙,50M⊙], and individual spins sz{1,2}∈[−0.9,0.9]; and which include the (ℓ,|m|)={(2,2),(2,1),(3,3),(3,2),(4,4)} modes, and mode mixing effects in the ℓ=3,|m|=2 harmonics. We trained these AI classifiers within 22 hours using distributed training over 96 NVIDIA V100 GPUs in the Summit supercomputer. We then used transfer learning to create AI predictors that estimate the total mass of potential binary black holes identified by all AI classifiers in the ensemble. We used this ensemble, 3 classifiers for signal detection and 2 total mass predictors, to process a year-long test set in which we injected 300,000 signals. This year-long test set was processed within 5.19 minutes using 1024 NVIDIA A100 GPUs in the Polaris supercomputer (for AI inference) and 128 CPU nodes in the ThetaKNL supercomputer (for post-processing of noise triggers), housed at the Argonne Leadership Computing Facility. These studies indicate that our AI ensemble provides state-of-the-art signal detection accuracy, and reports 2 misclassifications for every year of searched data. This is the first AI ensemble designed to search for and find higher order gravitational wave mode signals.
Tian, Minyang↗
Passband Signal Detection at the Edge
Algorithms for radio frequency (RF) spectrum awareness need to be compatible with edge hardware to be practical for many applications. We developed a signal detection and classification model for the ZCU111 RF System-on-a-Chip (RFSoC) that operates on the fast Fourier transform of passband RF data. The system can detect and classify multiple signals of interest and display the predictions in real-time. The model consists of a modified ConvNeXt backbone and YOLOv3 head to operate on the Deep Learning Processing Unit on the RFSoC. We gathered datasets for training and testing by using a software defined radio to transmit example signals of Wi-Fi 802.11 b/g, Wi-Fi 802.11 n, FM Radio, LTE and LTE-M. By leveraging multiple inputs on the RFSoC frontend, the datasets span up to 4 GHz of bandwidth. The models showed high performance in classification accuracy, center frequency error, bandwidth error, and detection accuracy for both single and multi-signal datasets.
42 ENGINEERING↗