Machine-learned dark matter subhalo candidates in the 4FGL-DR2: search for the perturber of the GD-1 stream

The detection of dark matter subhalos without a stellar component in the Galactic halo remains a challenge. We use supervised machine learning to identify high-latitude gamma-ray sources with dark matter-like spectra among unassociated gamma-ray sources in the 4FGL-DR2. Out of 843 4FGL-DR2 unassociated sources at |b| ≥ 10°, we select 73 dark matter subhalo candidates. Of the 69 covered by the Neil Gehrels Swift Observatory (Swift), 17 show at least one X-ray source within the 95% LAT error ellipse and 52 where we identify no new sources. This latest inventory of dark subhalos candidates allows us to investigate the possible dark matter substructure responsible for the perturbation in the GD-1 stellar stream. In particular, we examine the possibility that the alleged GD-1 dark subhalo may appear as a 4FGL-DR2 gamma-ray source from dark matter annihilation into Standard Model particles.

A performance analysis of the North American Lightning Detection Network using CN Tower lightning data

The North American Lightning Detection Network (NALDN) is a commercial lightning detection network operated by Vaisala Inc., and is composed of the U.S. National Lightning Detection Network (NLDN) and the Environment Canada owned Canadian Lightning Detection Network (CLDN). The CN Tower is one of the best sites in the world to observe the lightning phenomenon and provides an excellent opportunity to evaluate the performance of the NALDN in the Toronto area. Using CN Tower lightning data acquired during 2005, the performance characteristics of the NALDN were thoroughly evaluated, including the flash detection efficiency (DE), stroke DE, absolute location error, peak current estimation and location accuracy model (50%, 90% and 99% error ellipses) error. Although a similar test was performed using rocket-triggered lightning in Florida at Camp Blanding, this test evaluated a completely different region of the NALDN. Moreover, rocket-triggered lightning artificially initiates a lightning discharge, whereas lightning events to the CN Tower occur naturally and are similar to discharges that occur to tall structures or objects at high altitude or mountainous areas. Excluding two flashes understood to be composed of M-components, the NALDN detected 7 out of 7 flashes recorded at the CN Tower, resulting in a 100% flash DE. Furthermore, the NALDN detected 22 out of 39 strokes recorded at the CN Tower, resulting in a stroke DE of 56%. Relative to the CN Tower, the NALDN was found to have a median absolute location error of 0.356 km and a mean error of 0.390 km for the 22 strokes it detected. It was also demonstrated that the NALDN stroke location error seems to have a large bias towards the north of the CN Tower and a slight bias towards the east, with 19 of the 22 strokes predicted north-east of the CN Tower. The 50%, 90% and 99% error ellipses provided by the NALDN were also evaluated. It was found that 73% (16 out of the 22) detected strokes were enclosed by the 50% error ellipse, 91% (20 out of the 22) detected strokes were enclosed by the 90% error ellipse and 95% (21 out of the 22) detected strokes were enclosed by the 99% error ellipse. The minimum value for the 50% error ellipse axes is set at 0.4 km by Vaisala, and 21 out of the 22 detected strokes had a semi-major axis length of 0.4 km, suggesting that the median location error for CN Tower strokes is 0.4 or less. The 0.356 km median location error obtained for the 22 detected strokes appears to support this.

A performance analysis of the North American Lightning Detection Network using CN Tower lightning data

The North American Lightning Detection Network (NALDN) is a commercial lightning detection network operated by Vaisala Inc., and is composed of the U.S. National Lightning Detection Network (NLDN) and the Environment Canada owned Canadian Lightning Detection Network (CLDN). The CN Tower is one of the best sites in the world to observe the lightning phenomenon and provides an excellent opportunity to evaluate the performance of the NALDN in the Toronto area. Using CN Tower lightning data acquired during 2005, the performance characteristics of the NALDN were thoroughly evaluated, including the flash detection efficiency (DE), stroke DE, absolute location error, peak current estimation and location accuracy model (50%, 90% and 99% error ellipses) error. Although a similar test was performed using rocket-triggered lightning in Florida at Camp Blanding, this test evaluated a completely different region of the NALDN. Moreover, rocket-triggered lightning artificially initiates a lightning discharge, whereas lightning events to the CN Tower occur naturally and are similar to discharges that occur to tall structures or objects at high altitude or mountainous areas. Excluding two flashes understood to be composed of M-components, the NALDN detected 7 out of 7 flashes recorded at the CN Tower, resulting in a 100% flash DE. Furthermore, the NALDN detected 22 out of 39 strokes recorded at the CN Tower, resulting in a stroke DE of 56%. Relative to the CN Tower, the NALDN was found to have a median absolute location error of 0.356 km and a mean error of 0.390 km for the 22 strokes it detected. It was also demonstrated that the NALDN stroke location error seems to have a large bias towards the north of the CN Tower and a slight bias towards the east, with 19 of the 22 strokes predicted north-east of the CN Tower. The 50%, 90% and 99% error ellipses provided by the NALDN were also evaluated. It was found that 73% (16 out of the 22) detected strokes were enclosed by the 50% error ellipse, 91% (20 out of the 22) detected strokes were enclosed by the 90% error ellipse and 95% (21 out of the 22) detected strokes were enclosed by the 99% error ellipse. The minimum value for the 50% error ellipse axes is set at 0.4 km by Vaisala, and 21 out of the 22 detected strokes had a semi-major axis length of 0.4 km, suggesting that the median location error for CN Tower strokes is 0.4 or less. The 0.356 km median location error obtained for the 22 detected strokes appears to support this.

An improved bootstrap method introducing error ellipse for numerical analysis of fatigue life parameters

Purpose The purpose of this paper is to introduce error ellipse into the bootstrap method to improve the reliability of small samples and the credibility of the S-N curve. Design/methodology/approach Based on the bootstrap method and the reliability of the original samples, two error ellipse models are proposed. The error ellipse model reasonably predicts that the discrete law of expanded virtual samples obeys two-dimensional normal distribution. Findings By comparing parameters obtained by the bootstrap method, improved bootstrap method (normal distribution) and error ellipse methods, it is found that the error ellipse method achieves the expansion of sampling range and shortens the confidence interval, which improves the accuracy of the estimation of parameters with small samples. Through case analysis, it is proved that the tangent error ellipse method is feasible, and the series of S-N curves is reasonable by the tangent error ellipse method. Originality/value The error ellipse methods can lay a technical foundation for life prediction of products and have a progressive significance for the quality evaluation of products.

The August 17, 1914 Bilimbaev earthquake: location based on instrumental data

The parameters of the hypocenter of the Bilimbaev earthquake that occurred on August 17, 1914, are located. We performed earthquake location based on our compilation of all available seismological bulletins of the time that included data from the ISC-GEM (International Seismological Centre–Global Earthquake Model) project, the EuroSeismos project, and the Geophysical Survey of the Russian Academy of Sciences. The location was performed using a modified generalized beamforming method based on the ak135 travel time model and regional model by the Ural region. The new epicenter is removed from the epicenters, previous-ly determined from macroseismic and partly instrumental data, at a distance not exceeding 28 km. The previously calculated earthquake epicenters are in the region of the error ellipse of the new epicenter. The depth values indicated earlier also lie in the range of possible depths of the focus of the specified hypocenter. Thus, all the epicenters, including the new one, are relevant and with equal probability can be considered as the true epicenter of the earthquake

Improved Planning of Energy Recovery in Water Systems Using a New Analytic Approach to PAT Performance Curves

The use of pumps working as turbines (PATs) to improve the energy efficiency of water networks has been studied in the last years. This recovery system is justified due to a low investment contrasting with the capacity to take advantage in certain points with low and medium recoverable heads. Analyses of water systems using simulation software and/or optimization algorithms need the characteristic curves (head and efficiency) of the machines, which should be known with minor error by the water managers. The knowledge of the best efficiency point (BEP) as a turbine is one of the major limitations when the user wants to choose PATs. In this sense, the present research defines new approach equations to estimate the BEP of the PAT, as well as to predict the characteristic curves, comparing the results with the rest of the published methods. The comparison demonstrated that the new proposal reduced the error indexes, improved the R2 and increased the accuracy of the error ellipse using an experimental database of 181 different PATs.