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At least 91 records · Page 5

Seismic Excitation of the Polar Motion

The mass redistribution in the earth as a result of an earthquake faulting changes the earth's inertia tensor, and hence its rotation. Using the complete formulae developed by Chao and Gross (1987) based on the normal mode theory, we calculated the earthquake-induced polar motion excitation for the largest 11,015 earthquakes that occurred during 1977.0-1993.6. The seismic excitations in this period are found to be two orders of magnitude below the detection threshold even with today's high precision earth rotation measurements. However, it was calculated that an earthquake of only one tenth the size of the great 1960 Chile event, if happened today, could be comfortably detected in polar motion observations. Furthermore, collectively these seismic excitations have a strong statistical tendency to nudge the pole towards approx. 140 deg E, away from the actually observed polar drift direction. This non-random behavior, similarly found in other earthquake-induced changes in earth rotation and low-degree gravitational field by Chao and Gross (1987), manifests some geodynamic behavior yet to be explored.

Chao, Benjamin Fong↗

Variability in the power spectrum of solar five-minute oscillations

It is noted that two-dimensional power spectra of solar five-minute oscillations exhibit prominent ridge structures in (k, omega) space, k being the horizontal wavenumber and omega the temporal frequency. The positions of these ridges in k and omega can be used in probing temperature and velocity structures in the subphotosphere. The progress made in a program to establish whether power spectra taken on separate days show shifts in ridge locations is surveyed, noting that these may arise from different velocity and temperature patterns having been brought into the sampling region by solar rotation. Power spectra are obtained for six days of observations of Doppler velocities using the Mg I 5173 A and Fe I 5434 A spectral lines. Each data set encompasses 8 to 11 hr in time and samples a region 256 x 1024 arcsec in spatial extent, with a spatial resolution of 2 arcsec and temporal sampling of 65 s. Statistically significant shifts are detected in ridge locations between certain data sets. When analyzed in terms of eastward and westward propagating waves, the character of these displacements is seen as implying that changes have occurred in both temperature and horizontal velocity fields underlying the observing window. The velocity changes are estimated to be of the order of 100 m/s.

Hill, F.↗

Downsampling Photodetector Array with Windowing

In a photon counting detector array, each pixel in the array produces an electrical pulse when an incident photon on that pixel is detected. Detection and demodulation of an optical communication signal that modulated the intensity of the optical signal requires counting the number of photon arrivals over a given interval. As the size of photon counting photodetector arrays increases, parallel processing of all the pixels exceeds the resources available in current application-specific integrated circuit (ASIC) and gate array (GA) technology; the desire for a high fill factor in avalanche photodiode (APD) detector arrays also precludes this. Through the use of downsampling and windowing portions of the detector array, the processing is distributed between the ASIC and GA. This allows demodulation of the optical communication signal incident on a large photon counting detector array, as well as providing architecture amenable to algorithmic changes. The detector array readout ASIC functions as a parallel-to-serial converter, serializing the photodetector array output for subsequent processing. Additional downsampling functionality for each pixel is added to this ASIC. Due to the large number of pixels in the array, the readout time of the entire photodetector is greater than the time between photon arrivals; therefore, a downsampling pre-processing step is done in order to increase the time allowed for the readout to occur. Each pixel drives a small counter that is incremented at every detected photon arrival or, equivalently, the charge in a storage capacitor is incremented. At the end of a user-configurable counting period (calculated independently from the ASIC), the counters are sampled and cleared. This downsampled photon count information is then sent one counter word at a time to the GA. For a large array, processing even the downsampled pixel counts exceeds the capabilities of the GA. Windowing of the array, whereby several subsets of pixels are designated for processing, is used to further reduce the computational requirements. The grouping of the designated pixel frame as the photon count information is sent one word at a time to the GA, the aggregation of the pixels in a window can be achieved by selecting only the designated pixel counts from the serial stream of photon counts, thereby obviating the need to store the entire frame of pixel count in the gate array. The pixel count se quence from each window can then be processed, forming lower-rate pixel statistics for each window. By having this processing occur in the GA rather than in the ASIC, future changes to the processing algorithm can be readily implemented. The high-bandwidth requirements of a photon counting array combined with the properties of the optical modulation being detected by the array present a unique problem that has not been addressed by current CCD or CMOS sensor array solutions.

Patawaran, Ferze D.↗

An Automated Detection Methodology for Dry Well-Mixed Layers

The intense surface heating over arid land surfaces produces dry well-mixed layers (WML) via dry convection. These layers are characterized by nearly constant potential temperature and low, nearly constant water vapor mixing ratio. To further the study of dry WMLs, we created a detection methodology and supporting software to automate the identification and characterization of dry WMLs from multiple data sources including rawinsondes, remote sensing platforms, and model products. The software is a modular code written in Python, an open source language. Radiosondes from a network of synoptic stations in North Africa were used to develop and test the WML detection process. The detection involves an iterative decision tree that ingests a vertical profile from an input data file, performs a quality check for sufficient data density, and then searches upward through the column for successive points where the simultaneous changes in water vapor mixing ratio and potential temperature are less than the specified maxima. If points in the vertical profile meet the dry WML identification criteria, statistics are generated detailing the characteristics of each layer in the profile. At the end of the vertical profile analysis, there is an option to plot analyzed profiles in a variety of file formats. Initial results show that the detection methodology can be successfully applied across a wide variety of input data and North African environments and for all seasons. It is sensitive enough to identify dry WMLs from other types of isentropic phenomena such as subsidence layers and distinguish the current day’s dry WML from previous days.

Stephen D. Nicholls↗

Fade margin calculation for channels impaired by Rician fading

Excess path loss due to multipath severely restricts the performance of power limited mobile networks such as those using satellite-aided links. To reduce multipath related losses, the higher elevation angle of the spacecraft can be exploited by utilizing mobile antennas which reduce the strength of the multipath reflections in favor of the line-of-sight signal. The presence of a strong and stable path in a fading link will change the envelope statistics of the received waveform from Rayleigh to a more favorable Rician distribution. It is determined that the excess path loss, or fade margin, of a Rician channel when coherent detection of binary phase shift keying (BPSK) or quaternary phase shift keying (QPSK) signals is considered. The results are presented parametrically such that they can be applied to a wide range of propagation characteristics from heavy fading to nonfading situations. Furthermore, similar results are also given for the case where only limited coverage is provided.

Davarian, F.↗

Evaluation of Decision Trees for Cloud Detection from AVHRR Data

Automated cloud detection and tracking is an important step in assessing changes in radiation budgets associated with global climate change via remote sensing. Data products based on satellite imagery are available to the scientific community for studying trends in the Earth's atmosphere. The data products include pixel-based cloud masks that assign cloud-cover classifications to pixels. Many cloud-mask algorithms have the form of decision trees. The decision trees employ sequential tests that scientists designed based on empirical astrophysics studies and simulations. Limitations of existing cloud masks restrict our ability to accurately track changes in cloud patterns over time. In a previous study we compared automatically learned decision trees to cloud masks included in Advanced Very High Resolution Radiometer (AVHRR) data products from the year 2000. In this paper we report the replication of the study for five-year data, and for a gold standard based on surface observations performed by scientists at weather stations in the British Islands. For our sample data, the accuracy of automatically learned decision trees was greater than the accuracy of the cloud masks p < 0.001.

AVHRR (ADVANCED VERY HIGH RESOLUTION RADIOMETER)↗

Granger causal inference for climate change attribution

Abstract Climate change detection and attribution (D&A) is concerned with determining the extent to which anthropogenic activities have influenced specific aspects of the global climate system. D&A fits within the broader field of causal inference, the collection of statistical methods that identify cause and effect relationships. There are a wide variety of methods for making attribution statements, each of which require different types of input data and focus on different types of weather and climate events and each of which are conditional to varying extents. Some methods are based on Pearl causality (direct experimental interference) while others leverage Granger (predictive) causality, and the causal framing provides important context for how the resulting attribution conclusion should be interpreted. However, while Granger-causal attribution analyses have become more common, there is no clear statement of their strengths and weaknesses relative to Pearl-causal attribution and no clear consensus on where and when Granger-causal perspectives are appropriate. In this prospective paper, we provide a formal definition for Granger-based approaches to trend and event attribution and a clear comparison with more traditional methods for assessing the human influence on extreme weather and climate events. Broadly speaking, Granger-causal attribution statements can be constructed quickly from observations and do not require computationally-intesive dynamical experiments. These analyses also enable rapid attribution, which is useful in the aftermath of a severe weather event, and provide multiple lines of evidence for anthropogenic climate change when paired with Pearl-causal attribution. Confidence in attribution statements is increased when different methodologies arrive at similar conclusions. Moving forward, we encourage the D&A community to embrace hybrid approaches to climate change attribution that leverage the strengths of both Granger and Pearl causality.

Risser, Mark D. (ORCID:0000000319561783)↗

Evidence for detection of 1-10 MeV emission from the Taurus region in 1971 August

Results are reported for three observations of the Crab Nebula with a balloon-borne actively collimated NaI detector over the energy range from 0.2 to 10 MeV. Possible changes in instrument performance and environmental background levels during the balloon flights are investigated, and fits for source strength are made over four broad energy channels. X-ray power-law spectra and pulsed-flux fractions are determined for both the Crab Nebula and its pulsar, NP 0532. A statistically significant flux greatly in excess of the power law was detected above 1 MeV during one flight; this flux, which has declined by at least a factor of 4 or 5 at the times of more recent observations, is assumed to be real and is attributed to the nebula.

Gruber, D. E.↗

MSL Telecom Automated Anomaly Detection

The Mars Science Laboratory (MSL) Telecom Operations Team at the Jet Propulsion Laboratory (JPL) has implemented a machine learning system in order to automate the anomaly detection process as a part of daily operations. Machine learning enables reliable detection of anomalies in Telecom-related telemetry and automated reporting of Telecom subsystem status, resulting in an 90% reduction in team workload and improved anomaly detection reliability. At present, machine learning methods are used to detect: 1. Anomalous long-term trends in telemetry data 2. Anomalous time-domain evolution of telemetry values Both types of anomalies pose their own unique challenges that are addressed in different ways. In the first case, long term trending of daily minima, maximum, and mean telemetry values in temperatures, currents, voltages, and radio frequency (RF) power levels is used in addition to hard threshold safety checks to look for changes in long-term equipment health and performance. Long-term trending methods allow for ordinary seasonal variations in these quantities caused by temperature changes over the course of the Martian year while allowing operators to determine whether current performance remains in line with historical values from previous years. Changes in long-term trends can provide important insights into the health and status of the rover's on-board systems as well as valuable early warning if subtle degradation begins to take hold. But while trending of daily statistics is valuable, it does not detect anomalies in the short-term time evolution of data over the course of minutes or hours during a day, and this task is handled with short-term shape analysis. Principal components analysis (PCA) has been found to provide robust detection of short-term anomalies, and several examples of the use of PCA to detect actual anomalous events will be provided here. In using PCA, we use both the percentage of explained variance and also a log likelihood test on the PCA expansion coefficients to flag telemetry data for human review. Previous work in the field of spacecraft anomaly detection includes [1] for MSL and [2] for some other JPL missions.

Mukai, Ryan↗

Characterization of Climate Change and Variability with GPS

We compared zonal mean specific humidity derived from the 21 June-4 July 1995 Global Positioning System (GPS)/MET occultation observations with that derived from the European Center for Medium-Range Weather Forecasts (ECMWF) global analyses. The GPS/MET results indicate a drier troposphere, especially near the subtropical tradewind inversion. A small, moist bias in the GPS/MET upper northern-hemisphere troposphere compared to ECMWF may be due to a small radiosonde temperature bias. A diagram shows the difference (g/kg) between the GPS/MET zonal mean specific humidity and that for June-August derived from 1963-1973 radiosondes. Although the observing period is short, GPS and ECMWF results both indicate a significantly wetter boundary layer at most latitudes consistent with decadal trends observed in radiosonde data. GPS/MET results exhibit higher tropical convective available potential energy (CAPE), suggesting a more vigorous tropical Hadley circulation. Drier, free troposphere air in the descending branches of the Hadley circulation is due in part to a moist radiosonde bias but may also reflect some negative moisture feedback. Using 1992-1997 ground GPS observations and recent advancements in GPS technology, we removed an apparent altimetric drift (-1.2 +/- 0.4 mm/yr) due to columnar water vapor from the Topography (Ocean) Experiment (TOPEX) microwave radiometer, which brought the TOPEX mean sea level change estimates into better agreement with historical tide gauge records, suggesting global mean sea level is rising at a rate of 1.5-2.0 mm/yr. We can also discern a statistically significant increase of 0.2 +/- 0.1 kg/square m/yr in mean columnar water vapor over the ocean from 1992-1997. Optimal fingerprinting can be used for the detection and attribution of tropospheric warming due to an anthropogenic greenhouse. Optimal fingerprinting distinguishes between different types of signals according to their spatial and temporal patterns, while minimizing the influence of natural climate variability. S. Leroy concludes that the signal-to-noise ratio of global warming detection increases by unity approximately every 10 years if a single oceanic region is chosen. Less time for detection is likely when many global regions are considered simultaneously. GPS occultation constellations allow the possibility of detecting small changes in upper air temperature with inconsequential calibration errors, making occultation an ideal data type for global warming detection studies. Our initial study of a 22-GHz satellite-satellite occultation system predicts upper troposphere moisture sensitivities of 3-5 ppmv and 1-2 percent in the middle and lower troposphere. Additional information contained in original.

Kursinski, R.↗

Passive Microwave Arctic Sea Ice Melt Onset Dates From the Advanced Horizontal Range Algorithm 1979 - 2022

The onset of the summer melt season is a key stage of the Arctic sea ice seasonal cycle and is an indicator of climate change. Surface melting of the bare or snow-covered sea ice is detected using passive microwave satellite observations. The data set presented here is a 44 year record of Arctic sea ice annual melt onset (MO) dates for 1979–2022 produced using an updated version of the Advanced Horizontal Range Algorithm (AHRA). This data product contains annual maps of the sea ice MO date and a set of descriptive statistics summarizing the data. This paper describes a new update of the AHRA methodology, now AHRA V5, including key changes to the algorithm starting date and sea ice mask methodology to improve estimates of early-season MO dates especially near the sea ice periphery. AHRA V5 data are suitable for monitoring trends in Arctic and regional sea ice MO dates and for process studies of atmosphere-sea ice interactions during the early spring and summer months.

Angela C. Bliss↗

Neuro-behavioral Consequences of Low Dose Radiation Social Isolation and Sex Differences in the Longevity MCAT Mouse Model

The physiological responses to spaceflight elicit wide-ranging consequences and resemble aspects of aging on Earth. Previous studies have shown that oxidative damage via reactive oxygen species (ROS), contributes to aging-related pathologies. Our study uses 1-year old C57BL/6NJ male and female mice (astronaut-relevant age) that underwent exposure to 0.5 gray of gamma radiation together with social isolation and were euthanized 12 weeks after. We used the longevity MCAT mouse model in which human catalase is overexpressed in the mitochondria, for ROS quenching. We aimed to determine whether in older mice quenching ROS, will mitigate the neuro-behavioral consequences of low dose ionizing radiation and/or social isolation and whether the outcomes will differ in males and females. We have performed five mission relevant behavioral tests which focused on performance, memory, physical stance, and stress. We have detected both sex and radiation effects; the older females look physically better are faster and perform better almost in all behavioral tests compared to their male counterparts. On the other hand, they are more sensitive to low dose radiation in many cases, in some cases this effect was indeed mitigated in the MCAT mice, pointing out to the importance of ROS in response to radiation stress and social isolation. We have measured plasma (7- and 90-days post radiation), cytokines, corticosterone and hippocampal cytokine and microglial activation at the end of the experiment. We saw significant changes in the plasma markers due to radiation, sex, and genotype in both short and long post radiation period and detected long term sex and radiation effects in the brain. Our focus is now on applying advanced statistical modeling to corelate the behavioral tests with our recent molecular findings to look for specific biomarkers that could predict behavioral deficits.

radiation↗

Impact of Glass Irradiation on Laser-induced Breakdown Spectroscopy Diagnostics in the Visible and NIR Range

Increased absorption of optical materials arising from the exposure to ionizing radiation must be accounted for to accurately analyze the laser-induced breakdown spectroscopy (LIBS) data retrieved from high-radiation environments. We evaluate this effect on a specifc example that mimics the diagnostics placed within a gas-cooled fast reactor coolant stream. Analysis is performed on the LIBS data measured with 1% Xe gas in an ambient He environment and the measured optical absorption from the gamma- and neutron-irradiated low-OH fused silica and sapphire glasses. No significant change in the number of shots required to reach a 3σ detection level was observed up to 10 Mrad gamma dose and exposure to a 1.7×10 17 n/cm 2 neutron fluence. In contrast, the spectral dependence of absorption results in either a systematic overestimation or underestimation of line intensity ratios, depending on the line of interest selected for analysis. Moreover, if lines from different spectral regions are used to create Boltzmann plots, this attenuation also leads to statistically significant changes in the calculated temperatures for calculations using Xe II lines only, lowering them from 8000±610 K to 6800±810 K for the case of exposure to the 1.7×10 17 n/cm 2 flux, and broadening the temperature range required for a 95% confidence interval. In the case of measuring the Xe spectrum, these effects may be mitigated using only the longer-wavelength spectral region, where radiation-attenuation is relatively small, or through analysis using the iterative Saha-Boltzmann method.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

The dark energy survey: detection of weak lensing magnification of supernovae and constraints on dark matter haloes

The residuals of the distance moduli of Type Ia supernovae (SNe Ia) relative to a Hubble diagram fit contain information about the inhomogeneity of the Universe, due to weak lensing magnification by foreground matter. By correlating the residuals of the Dark Energy Survey Year 5 SN Ia sample (DES-SN5YR) with extragalactic foregrounds from the DES Y3 Gold catalogue, we detect the presence of lensing at |$6.0 \sigma$| significance. This is the first detection with a significance level above |$5\sigma$|⁠. Constraints on the effective mass-to-light ratios and radial profiles of dark matter haloes surrounding individual galaxies are also obtained. We show that the scatter of SNe Ia around the Hubble diagram is reduced by modifying the standardization of the distance moduli to include an easily calculable de-lensing (i.e. environmental) term. We use the de-lensed distance moduli to recompute cosmological parameters derived from SN Ia, finding in Flat wcold dark matter a difference of |$\Delta \Omega _{\rm M} = +0.036$| and |$\Delta w = -0.056$| compared to the unmodified distance moduli, a change of |$\sim 0.3\sigma$|⁠. We argue that our modelling of SN Ia lensing will lower systematics on future surveys with higher statistical power. We use the observed dispersion of lensing in DES-SN5YR to constrain |$\sigma _8$|⁠, but caution that the fit is sensitive to uncertainties at small scales. Nevertheless, our detection of SN Ia lensing opens a new pathway to study matter inhomogeneity that complements galaxy–galaxy lensing surveys and has unrelated systematics.

79 ASTRONOMY AND ASTROPHYSICS↗

Probing and Tuning Strain‐Localized Exciton Emission in 2D Material Bubbles at Room Temperature

In monolayer transition metal dichalcogenides bubbles-nanoscale deformations typically exhibiting a dome-like shape-Excitons are confined by the strain effect, which exhibits extraordinary emission properties, such as single photon generation, enhanced light emission, and spectrally tunable excitonic states. While the strain profiles of these bubbles are extensively studied, this work provides an approach 1) to directly visualize the associated exciton properties in bubbles formed in WSe2 monolayer, revealing an intrinsic emission wavelength shift of ≈40 nm, and 2) actively modify local strain, enabling further exciton emission tuning over a range of 50 nm. These are achieved by emission mapping and nanoindentation using a dielectric near-field probe, which enables the detection of local emission spectra and emission lifetimes within individual bubbles. Statistical analysis of 67 bubbles uncovers an emission wavelength distribution centered around 780 nm. Furthermore, saturation behavior in the power-dependent studies and the associated lifetime change reveal the localized nature of the strain-induced states. These findings provide direct insights into the strain-localized emission dynamics in bubbles and establish a robust framework for non-destructive, reversible, and predictable nanoscale emission control, presenting a potential avenue for developing next-generation tunable quantum optical sources.

2D materials bubbles↗

ThunderSecure: deploying real-time intrusion detection for 100G research networks by leveraging stream-based features and one-class classification network

Nowadays, data generated by large-scale scientific experiments are on the scale of petabytes per month. These data are transferred through dedicated high-bandwidth networks (40/100G) across distributed sites for processing, storage, and analysis. Like general purpose networks, research networks experience intrusions. However, monitoring anomalies in such high-speed network traffics is challenging given current cyber-infrastructure. Moreover, traditional network intrusion detection systems (NIDS) are signature based. However, anomaly patterns are difficult to define and that rulesets are often not updated frequently enough to reflect the changes of attack behaviors. We present ThunderSecure, a high-throughput, unsupervised learning-based intrusions detection system for 100G research networks. ThunderSecure implements an efficient packet processing and detection pipeline using multi-cores and GPUs. It extracts statistical and temporal features from real-time network data streams and feeds them to a one-class anomaly detection network. A baseline of normal distribution will be created based on the training observation. Testing traffic deviated from the learned profile will be marked as anomalies. We trained ThunderSecure on hundreds of billions of science data packets mirrored from two 100G network connections at Fermi National Accelerator Laboratory. The detection performance was evaluated on traffic captured from the same research network days and weeks after the training with different types of attack flows injected. Results show that ThunderSecure can recognize science data traffic captured long after the training and made nearly certain detection on the segment of the streams where anomalous flows were injected.

100G research network↗

Online Detection of Inter-Turn Winding Faults in Single-Phase Distribution Transformers Using Smart Meter Data

Turn-to-turn faults between primary windings due to insulation degradation are a major cause of distribution transformer failure, and occur due to high levels of stress such as overloading and overheating. An additional consequence of these faults is an increased voltage on the transformer secondary due to effective change in turns ratio. This paper develops a novel method for early detection of insulation degradation and subsequent inter-turn winding failure by monitoring the transformer secondary voltage. The algorithm is based on a cumulative sum (CUSUM) statistic and compares voltages on neighbouring transformers to flag degrading assets. Results obtained from simulation as well as experimental data show that smart meter measurements can be utilized to achieve very high detection accuracy while keeping costs low. Here, the paper also demonstrates the validity of the algorithm in the presence of measurement noise, residential solar power injection etc.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Distributed Monitoring of the R(sup 2) Statistic for Linear Regression

The problem of monitoring a multivariate linear regression model is relevant in studying the evolving relationship between a set of input variables (features) and one or more dependent target variables. This problem becomes challenging for large scale data in a distributed computing environment when only a subset of instances is available at individual nodes and the local data changes frequently. Data centralization and periodic model recomputation can add high overhead to tasks like anomaly detection in such dynamic settings. Therefore, the goal is to develop techniques for monitoring and updating the model over the union of all nodes data in a communication-efficient fashion. Correctness guarantees on such techniques are also often highly desirable, especially in safety-critical application scenarios. In this paper we develop DReMo a distributed algorithm with very low resource overhead, for monitoring the quality of a regression model in terms of its coefficient of determination (R2 statistic). When the nodes collectively determine that R2 has dropped below a fixed threshold, the linear regression model is recomputed via a network-wide convergecast and the updated model is broadcast back to all nodes. We show empirically, using both synthetic and real data, that our proposed method is highly communication-efficient and scalable, and also provide theoretical guarantees on correctness.

Bhaduri, Kanishka↗