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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Transfer function models for using empirical and physics-based simulation signal response data

In many situations, real or induced flaws such as tight cracks with known morphology cannot be manufactured in part geometry specimens or in real parts. Typically, surface fatigue cracks are manufactured in simple geometry specimens such as flat plates, dog-bone shaped flat or cylindrical specimens. If a nondestructive evaluation (NDE)technique is required to provide a reliably detectable flaw size, denoted asa90/95, for induced flaws in a part, then a direct method for qualifying the NDE procedure is to use appropriate induced flaw specimens and perform NDE procedure demonstration on the specimens. Probability of detection (POD)analysis of the empirical data may provide estimation of a90/95. This approach is described as direct POD demonstration testing, which may follow guidelines of MIL-HDBK-1823. This paper considers a case, where embedded tight cracklike induced flaws are to be detected reliably using a signal response based NDE procedure. Here, it is assumed that it is not practical to make surface or embedded induced flaw specimens in part geometry or configuration. Therefore, a direct POD demonstration testing cannot be undertaken. It is also assumed that simulation of signal response is possible for both surface and embedded induced flaws in part geometry specimens using a physics-based model. The proposed approach for NDE procedure qualification uses artificial flaws in simple geometry and part geometry specimens, and induced flaws in the same type of simple geometry specimens. Signal response data is taken on all sets of artificial and induced flaws in simple geometry and part geometry specimens. Moreover, simulated signal response data is generated for surface and embedded flaws. Thus, a case of five signal response versus flaw size datasets is considered. Three of the datasets are empirical and two datasets are physics model-based simulation datasets. A method of devising and using transfer function calculation dataset blocks to estimate the either the reliably detectable flaw size or the demonstration flaw size is provided.

Nondestructive evaluation↗
DOE OSTI2024

Evaluating the Effectiveness of a Detection and Deterrent System in Reducing Golden Eagle Fatalities at Operational Wind Facilities

The Renewable Energy Wildlife Institute (REWI) was appointed as the prime awardee of DOE award number DE-EE0007883 to lead a team of scientists, wind developers, and technology manufacturers toward the overarching goal of evaluating the effectiveness of the current DTBird system in minimizing the risk of golden eagles (Aquila chrysaetos) and other large soaring raptors from approaching the rotor-swept zone (RSZ) of operating wind turbines. As part of this goal, the team set out to 1) quantify the expected reduction in collision risk for golden eagles from operation of the detection and deterrence modules in a manner that supports the approach used by the U.S. Fish and Wildlife Service (USFWS) to assess and credit facility operators for their efforts to minimize predicted collision fatalities and 2) provide information to help improve the technology to maximize its effectiveness. DTBird is an automated detection and audio deterrent system created by the Spanish company Liquen, designed to discourage birds from entering the RSZ of spinning wind turbines. The system uses cameras to automatically detect airborne targets of interest, records each such event in an online database, and triggers a warning signal (loud sound) if the tracked object has moved close to the turbine. If the object moves even closer to the RSZ, a more aggressive dissuasion signal is broadcast. To meet our objectives, the team conducted a two-year experiment at the Goodnoe Hills wind facility in Washington state, in which 14 turbines were outfitted with DTBird units. Daily, each DTBird-equipped turbine was randomly assigned to a control or treatment group. Treatment turbines operated with DTBird running as intended—broadcasting warning or deterrent signals when DTBird detected a target within range. On control turbines, no sound signals were broadcast if a moving target triggered the DTBird system. The team also flew unmanned aerial vehicles (UAVs) designed to coarsely mimic the general size, weight, and coloration of golden eagles in programmed flight transects across DTBird detection ranges to quantify DTBird’s ability to detect intended targets and to evaluate factors that influence the probability of detection and DTBird’s response distances. Additionally, the team evaluated the behavioral responses of in situ eagles exposed to spinning turbines alone (visual and sound influences) versus spinning turbines plus broadcasted DTBird audio deterrents, to estimate the effectiveness of deterrence by the DTBird system. The data and results from these investigations were combined with those from a pilot study conducted at the Manzana Wind Power Project in California to better evaluate DTBird’s effectiveness across different landscapes.

17 WIND ENERGY↗
DOE OSTI2026

Teleseismic Network Association with GENIE

In this report we investigate adapting the Graph Neural Interpretation Engine (GENIE), an associator developed for three-component dense monitoring networks, to regional to teleseismic association using a sparse network of array stations. We expand GENIE’s input features to include first-P detection time, azimuth, and slowness estimates. Additionally, we include a probability of detection (PDET) term which measures a station’s likelihood of detecting an event. To assess each feature’s relative importance, we train four models, each using an increasing set of node features and find that the PDET models perform the best. We define two measures of event complexity which demonstrate that all GENIE model versions perform better than the standard backprojection stack.

47 OTHER INSTRUMENTATION↗
DOE OSTI2017

Solar Activity from 2006 to 2014 and Short-term Forecasts of Solar Proton Events Using the ESPERTA Model

To evaluate the solar energetic proton (SEP) forecast model of Laurenza et al., here termed ESPERTA, we computed the input parameters (soft X-ray (SXR) fluence and ∼1 MHz radio fluence) for all ≥M2 SXR flares from 2006 to 2014. This database is outside the 1995–2005 interval on which ESPERTA was developed. To assess the difference in the general level of activity between these two intervals, we compared the occurrence frequencies of SXR flares and SEP events for the first six years of cycles 23 (1996 September–2002 September) and 24 (2008 December–2014 December). We found a reduction of SXR flares and SEP events of 40% and 46%, respectively, in the latter period. Moreover, the numbers of ≥M2 flares with high values of SXR and ∼1 MHz fluences (>0.1 J m{sup −2} and >6 × 10{sup 5} sfu × minute, respectively) are both reduced by ∼30%. A somewhat larger percentage decrease of these two parameters (∼40% versus ∼30%) is obtained for the 2006–2014 interval in comparison with 1995–2005. Despite these differences, ESPERTA performance was comparable for the two intervals. For the 2006–2014 interval, ESPERTA had a probability of detection (POD) of 59% (19/32) and a false alarm rate (FAR) of 30% (8/27), versus a POD = 63% (47/75) and an FAR = 42% (34/81) for the original 1995–2005 data set. In addition, for the 2006–2014 interval the median (average) warning time was estimated to be ∼2 hr (∼7 hr), versus ∼6 hr (∼9 hr), for the 1995–2005 data set.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1991

Early focus development effort, ultrasonic inspection of fixed housing metal-to-adhesive bondline

An ultrasonic technique was developed for the fixed housing metal-to-adhesive bondline that will support the Flight 15 time frame and subsequent motors. The technique has the capability to detect a 1.0 inch diameter unbond with a 90 percent probability of detection (POD) at a 95 percent confidence level. The technique and support equipment will perform within the working envelope dictated by a stacked motor configuration.

Hartmann, John K.↗
NASA NTRS2006

Optical Microscopy Techniques to Inspect for Metallic Whiskers

Metal surface finishes of tin, zinc and cadmium are often applied to electronic components, mechanical hardware and other structures. These finishes sometimes unpredictably may form metal whiskers over periods that can take from hours to months or even many years. The metal whiskers are crystalline structures commonly having uniform cross sectional area along their entire length. Typical whisker dimensions are nominally on the order of only a few microns (um) across while their lengths can extend from a few microns to several millimeters. Metal whiskers pose a reliability hazard to electronic systems primarily as an electrical shorting hazard. The extremely narrow dimensions of metal whiskers can make observation with optical techniques very challenging. The videos herein were compiled to demonstrate the complexities associated with optical microscope inspection of electronic and mechanical components and assemblies for the presence or absence of metal whiskers. The importance of magnification, light source and angle of illumination play critical roles in being able to detect metal whiskers when present. Furthermore, it is demonstrated how improper techniques can easily obscure detection. It is hoped that these videos will improve the probability of detecting metal whiskers with optical inspection techniques.

Brusse, Jay A.↗
NASA NTRS2012

Evaluation of Precipitation Detection over Various Surfaces from Passive Microwave Imagers and Sounders

During the middle part of this decade a wide variety of passive microwave imagers and sounders will be unified in the Global Precipitation Measurement (GPM) mission to provide a common basis for frequent (3 hr), global precipitation monitoring. The ability of these sensors to detect precipitation by discerning it from non-precipitating background depends upon the channels available and characteristics of the surface and atmosphere. This study quantifies the minimum detectable precipitation rate and fraction of precipitation detected for four representative instruments (TMI, GMI, AMSU-A, and AMSU-B) that will be part of the GPM constellation. Observations for these instruments were constructed from equivalent channels on the SSMIS instrument on DMSP satellites F16 and F17 and matched to precipitation data from NOAA's National Mosaic and QPE (NMQ) during 2009 over the continuous United States. A variational optimal estimation retrieval of non-precipitation surface and atmosphere parameters was used to determine the consistency between the observed brightness temperatures and these parameters, with high cost function values shown to be related to precipitation. The minimum detectable precipitation rate, defined as the lowest rate for which probability of detection exceeds 50%, and the detected fraction of precipitation, are reported for each sensor, surface type (ocean, coast, bare land, snow cover) and precipitation type (rain, mix, snow). The best sensors over ocean and bare land were GMI (0.22 mm/hr minimum threshold and 90% of precipitation detected) and AMSU (0.26 mm/hr minimum threshold and 81% of precipitation detected), respectively. Over coasts (0.74 mm/hr threshold and 12% detected) and snow-covered surfaces (0.44 mm/hr threshold and 23% detected), AMSU again performed best but with much lower detection skill, whereas TMI had no skill over these surfaces. The sounders (particularly over water) benefited from the use of re-analysis data (vs. climatology) to set the a-priori atmospheric state and all instruments benefit from the use of a conditional snow cover emissivity database over land. It is recommended that real-time sources of these data be used in the operational GPM precipitation algorithms.

GPM↗
DOE OSTI2023

Driving in the dark: Deciphering nighttime driver detection of free-ranging roadside wildlife

Wildlife-vehicle collisions are dangerous for motorists; however, few studies have addressed driver detection of roadside animals, and none have evaluated detection of free-ranging wildlife. Here, we used 24 volunteer drivers, infrared videography, a 75-km route, and free-ranging wildlife to quantify factors influencing (1) probability of wildlife detection, (2) detection distance, and (3) probability of dangerous encounters (i.e., detection distance < distance required for braking) for multiple species at night in South Carolina, USA. Detection probability of white-tailed deer (Odocoileus virginianus) was impacted by multiple driver, animal, and roadside factors. Deer detection distances increased by 20.99 m when drivers used high-beam headlights and 23.36 m when deer were moving but decreased by 0.71 m for every minute into a drive. Every encounter with wild pigs (Sus scrofa) and most encounters with small mammals were considered dangerous. Our findings suggest most drivers cannot safely detect deer, wild pigs, and small mammals at night.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1990

Frame synchronization for a channel with different data rates

In some communication links, different data rates are used at different times on the same channel and each is assigned a separate frame synchronization (sync) marker. The frame search is completed within a finite number of frame intervals once both phase-lock and bit-sync are achieved because the incoming data rate can change. The probability of failure to frame-lock, and an upper bound of probability of false frame-lock in a frame search mode are derived for a communication link with a 10 to the -5th bit error probability. The probability of failure to detect data-rate change and the probability of false detection of data-rate change in a frame-lock mode are discussed. In a frame-lock mode, the probability of failure to detect data-rate change and probability detection of data-rate change an equal to 4.96 x 10 to the -12th. A frame-lock device can be used for the Space Station Freedom Ku-band return link.

Kwon, Hyuck M.↗
NASA NTRSDate not supplied

NASA NDE Fracture Critical Detectable Flaw Sizes History and Methodology

NASA requires that NDE methods and inspectors demonstrate 90% Probability of Detection (POD) with 95% confidence for critical flaw sizes when inspecting fracture critical metallic components. NASA addresses the known variability of NDE inspector capability in two ways. The first, Special NDE, requires that every inspector demonstrate the required 90/95 POD, which is resource intensive. The second approach is Standard NDE for which conservative flaw sizes for different NDE methods are provided such that it is expected that most properly trained inspectors will exceed the 90/95 POD requirement. As such, individual POD demonstration testing is not required. The origin of NASA Standard NDE dates to the start of the Space Shuttle Program in the early 1970’s. In the first study to quantitatively assess NDE methods and inspectors, the performance of multiple inspectors was evaluated for different NDE methods using a large set of fatigue cracked specimens. A rudimentary POD analysis was performed to estimate the 90/95 POD flaw size for each inspector for each method. Additionally, the average and standard deviation of the 90/95 POD flaw size across the multiple inspectors was calculated to estimate the flaw size for which 95 percent of inspectors would provide the 90/95 POD capability. These estimated 90/95/95 POD flaw sizes evolved into the NASA Standard NDE flaw sizes still in use for structural analysis five decades later. The methodology for performing Standard NDE POD studies was never documented in NASA requirements. Furthermore, POD analysis methods have significantly evolved since this seminal study. Likewise, NDE methods have improved and there has been a push to reassess Standard NDE flaw sizes for existing methods, and to develop Standard NDE flaw sizes for new methods such as digital radiography. In this study, a Standard NDE POD methodology was developed and baselined using the historical data. This reanalysis of the historical data identified several deficiencies in the original test plan as well as an overall lack of conservatism in the estimated 90/95/95 POD flaw sizes. The results of this historical review and the new methodology are being incorporated into an update of NASA NDE POD requirements.

Nondestructive Evaluation↗
NASA NTRSDate not supplied

Transfer Function Models and Sensitivity Analysis

In many situations, real or induced flaws such as tight cracks with known morphology cannot be manufactured in part configuration specimens or in real parts. Typically, fatigue cracks are manufactured in simple geometry specimens such as flat plates, dog-bone shaped flat or round specimens. If a nondestructive evaluation (NDE) technique is required to provide a reliably detectable target flaw size denoted as α_(90/95) for induced flaws in real part, then the direct method for qualifying the NDE procedure is to use the appropriate induced flaw specimens and perform probability of detection analysis using these flaws. This can be described as direct POD demonstration testing, which may follow guidelines of MIL-HDBK-1823. This paper considers a case, where induced flaws are not available in part configuration specimens. Therefore, a direct POD demonstration study cannot be undertaken. In such situation, general practice for NDE procedure qualification is to use artificial flaws in simple geometry and part configuration specimens, and induced flaws in the chosen simple geometry specimens. Signal response data is taken on all sets of artificial and induced flaws. NDE procedure testing on induced flaw in simple geometry specimen is called NDE demonstration testing here. A transfer function NDE procedure qualification method for forward case calculates predicted induced flaw size for demonstration using a chosen target flaw size. Another transfer function method for inverse case, calculates the target flaw size using a given demonstration flaw size. The transfer function analysis assumes relationship of artificial flaw signal responses in real parts and simple geometry specimens; and induced flaw responses in simple geometry specimens to induced flaws in real parts. The signal response transfer relationships model should be defined before transfer function models can be devised. Assuming that the signal response transfer relationships model is valid, forward and inverse case transfer function methods have been devised. Because of lack of signal response data from induced flaws in real part, 90/95% POD/confidence (P/C) cannot be demonstrated directly. However, the transfer function method may be assessed using simulation to evaluate whether the resulting target flaw size or demonstration flaw size provides adequate confidence to the assumed signal response transfer relationships model. Therefore, the transfer function approach is a risk assessment approach. Both the signal response transfer relationships model and the transfer function model are important in managing risk in results provided by the transfer function NDE technique qualification or assessment. The signal response transfer relationships model needs to be validated with empirical data and then transfer function model needs to be validated for desired P/C on case-by-case basis.

Nondestructive evaluation↗
DOE OSTI2015

Multi-Level Anomaly Detection on Time-Varying Graph Data

This work presents a novel modeling and analysis framework for graph sequences which addresses the challenge of detecting and contextualizing anomalies in labelled, streaming graph data. We introduce a generalization of the BTER model of Seshadhri et al. by adding flexibility to community structure, and use this model to perform multi-scale graph anomaly detection. Specifically, probability models describing coarse subgraphs are built by aggregating probabilities at finer levels, and these closely related hierarchical models simultaneously detect deviations from expectation. This technique provides insight into a graph's structure and internal context that may shed light on a detected event. Additionally, this multi-scale analysis facilitates intuitive visualizations by allowing users to narrow focus from an anomalous graph to particular subgraphs or nodes causing the anomaly. For evaluation, two hierarchical anomaly detectors are tested against a baseline Gaussian method on a series of sampled graphs. We demonstrate that our graph statistics-based approach outperforms both a distribution-based detector and the baseline in a labeled setting with community structure, and it accurately detects anomalies in synthetic and real-world datasets at the node, subgraph, and graph levels. To illustrate the accessibility of information made possible via this technique, the anomaly detector and an associated interactive visualization tool are tested on NCAA football data, where teams and conferences that moved within the league are identified with perfect recall, and precision greater than 0.786.

anomaly detection↗
NASA NTRS1989

Adaptive narrow-band interference rejection in a DS spread-spectrum intercept receiver using transform domain signal processing techniques

An intercept receiver which uses a transform-domain-processing filter is described. This receiver detects direct-sequence BPSK spread-spectrum signals in the presence of narrowband interference by employing adaptive narrowband interference rejection techniques. The improvement in the system performance over that of conventional detection techniques is shown by presenting the results of experimental measurements of probability of detection versus false alarm for an enhanced total power detector. Also presented are certain results corresponding to detection of the spectral lines generated at twice the carrier frequency, wherein the goal is often not just signal detection, but also carrier frequency estimation. The receiver uses one of two transform-domain-processing techniques for adaptive narrowband interference rejection. In the first technique, the narrowband interference is detected and excised in the transform domain by using an adaptive notch filter. In the second technique, the interference is suppressed using soft-limiting in the transform domain.

Gevargiz, John↗
NASA NTRS2006

Convective Weather Forecast Quality Metrics for Air Traffic Management Decision-Making

Since numerical weather prediction models are unable to accurately forecast the severity and the location of the storm cells several hours into the future when compared with observation data, there has been a growing interest in probabilistic description of convective weather. The classical approach for generating uncertainty bounds consists of integrating the state equations and covariance propagation equations forward in time. This step is readily recognized as the process update step of the Kalman Filter algorithm. The second well known method, known as the Monte Carlo method, consists of generating output samples by driving the forecast algorithm with input samples selected from distributions. The statistical properties of the distributions of the output samples are then used for defining the uncertainty bounds of the output variables. This method is computationally expensive for a complex model compared to the covariance propagation method. The main advantage of the Monte Carlo method is that a complex non-linear model can be easily handled. Recently, a few different methods for probabilistic forecasting have appeared in the literature. A method for computing probability of convection in a region using forecast data is described in Ref. 5. Probability at a grid location is computed as the fraction of grid points, within a box of specified dimensions around the grid location, with forecast convection precipitation exceeding a specified threshold. The main limitation of this method is that the results are dependent on the chosen dimensions of the box. The examples presented Ref. 5 show that this process is equivalent to low-pass filtering of the forecast data with a finite support spatial filter. References 6 and 7 describe the technique for computing percentage coverage within a 92 x 92 square-kilometer box and assigning the value to the center 4 x 4 square-kilometer box. This technique is same as that described in Ref. 5. Characterizing the forecast, following the process described in Refs. 5 through 7, in terms of percentage coverage or confidence level is notionally sound compared to characterizing in terms of probabilities because the probability of the forecast being correct can only be determined using actual observations. References 5 through 7 only use the forecast data and not the observations. The method for computing the probability of detection, false alarm ratio and several forecast quality metrics (Skill Scores) using both the forecast and observation data are given in Ref. 2. This paper extends the statistical verification method in Ref. 2 to determine co-occurrence probabilities. The method consists of computing the probability that a severe weather cell (grid location) is detected in the observation data in the neighborhood of the severe weather cell in the forecast data. Probabilities of occurrence at the grid location and in its neighborhood with higher severity, and with lower severity in the observation data compared to that in the forecast data are examined. The method proposed in Refs. 5 through 7 is used for computing the probability that a certain number of cells in the neighborhood of severe weather cells in the forecast data are seen as severe weather cells in the observation data. Finally, the probability of existence of gaps in the observation data in the neighborhood of severe weather cells in forecast data is computed. Gaps are defined as openings between severe weather cells through which an aircraft can safely fly to its intended destination. The rest of the paper is organized as follows. Section II summarizes the statistical verification method described in Ref. 2. The extension of this method for computing the co-occurrence probabilities in discussed in Section HI. Numerical examples using NCWF forecast data and NCWD observation data are presented in Section III to elucidate the characteristics of the co-occurrence probabilities. This section also discusses the procedure for computing throbabilities that the severity of convection in the observation data will be higher or lower in the neighborhood of grid locations compared to that indicated at the grid locations in the forecast data. The probability of coverage of neighborhood grid cells is also described via examples in this section. Section IV discusses the gap detection algorithm and presents a numerical example to illustrate the method. The locations of the detected gaps in the observation data are used along with the locations of convective weather cells in the forecast data to determine the probability of existence of gaps in the neighborhood of these cells. Finally, the paper is concluded in Section V.

Chatterji, Gano B.↗
DOE OSTI2022

Environmentally Adaptive, Multiband Software-Defined-Radar for Monitoring of Item of Interest in a Dynamically Cluttered Room

High-resolution multi-frequency radars when networked together, form a robust and seamless monitoring system for high-valued items. The multi-look-direction radar network investigated by LLNL and DSI, operates at 77 GHz and 5.8GHz, and is capable of detection at centimeter resolution movement of objects of interest. This dual frequency radar (unlike optical methods) can operate under various environments with conditions such a smoke, dust, and other natural or manmade obscurations. In addition, the multiband network radar is based on low-cost FCC approved EM specifications and can be scaled with the dimensions of the room and objects under consideration by user-defined inputs. Furthermore, the underlying radar signal processing based on software-defined radar engine is adaptive to its operational environment, where operators can input the size of the facility and object of interest and the distance between the object and radar. Finally, this multi-frequency radar employs a host of change detection and machine learning algorithms to reach performance levels in terms of high probability of detection and low false alarm rates while the monitoring state of the object can be reported remotely.

42 ENGINEERING↗
NASA NTRS1973

An analysis of the facsimile-camera response to radiant point sources

In addition to imaging the surrounding terrain, planetary lander cameras may also be used to survey the stars to aid in locating the lander site. The response of the facsimile camera, which was selected for the Viking lander missions to Mars, to a radiant point source is formulated and shown to result in a statistical rather than deterministic signal. The signal statistics are derived and magnitudes are evaluated for the brighter visual and red stars. The probability of detecting the resultant statistical signals in photosensor and preamplifier noise and the associated probability of false alarms are also determined.

Huck, F. O.↗
NASA NTRS2022

A Survey of NASA Standard Nondestructive Evaluation (NDE)

This document provides a comprehensive review to trace the evolution of NASA’s Standard nondestructive evaluation (NDE) flaw sizes provided in NASA-STD-5009B for fracture-critical metallic spaceflight hardware. A NASA Standard NDE flaw size is considered to be conservative such that most inspectors, trained and certified in the specific method, are expected to provide the required 90/95 probability of detection (POD) for that flaw size. As such, individual certified inspectors are not required to perform POD demonstration testing to be allowed to inspect fracture-critical hardware for that specific method.

Probability of Detection↗
DOE OSTI2016

Optical and X-ray early follow-up of ANTARES neutrino alerts

High-energy neutrinos could be produced in the interaction of charged cosmic rays with matter or radiation surrounding astrophysical sources. Even with the recent detection of extraterrestrial high-energy neutrinos by the IceCube experiment, no astrophysical neutrino source has yet been discovered. Transient sources, such as gamma-ray bursts, core-collapse supernovae, or active galactic nuclei are promising candidates. Multi-messenger programs offer a unique opportunity to detect these transient sources. By combining the information provided by the ANTARES neutrino telescope with information coming from other observatories, the probability of detecting a source is enhanced, allowing the possibility of identifying a neutrino progenitor from a single detected event. A method based on optical and X-ray follow-ups of high-energy neutrino alerts has been developed within the ANTARES collaboration. This method does not require any assumptions on the relation between neutrino and photon spectra other than time-correlation. This program, denoted as TAToO, triggers a network of robotic optical telescopes (TAROT and ROTSE) and the Swift-XRT with a delay of only a few seconds after a neutrino detection, and is therefore well-suited to search for fast transient sources. To identify an optical or X-ray counterpart to a neutrino signal, the images provided by the follow-up observations are analysed with dedicated pipelines. A total of 42 alerts with optical and 7 alerts with X-ray images taken with a maximum delay of 24 hours after the neutrino trigger have been analysed. No optical or X-ray counterparts associated to the neutrino triggers have been found, and upper limits on transient source magnitudes have been derived. The probability to reject the gamma-ray burst origin hypothesis has been computed for each alert.

79 ASTRONOMY AND ASTROPHYSICS↗