69 research outputs found

    Search for Eccentric Black Hole Coalescences during the Third Observing Run of LIGO and Virgo

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    Despite the growing number of confident binary black hole coalescences observed through gravitational waves so far, the astrophysical origin of these binaries remains uncertain. Orbital eccentricity is one of the clearest tracers of binary formation channels. Identifying binary eccentricity, however, remains challenging due to the limited availability of gravitational waveforms that include effects of eccentricity. Here, we present observational results for a waveform-independent search sensitive to eccentric black hole coalescences, covering the third observing run (O3) of the LIGO and Virgo detectors. We identified no new high-significance candidates beyond those that were already identified with searches focusing on quasi-circular binaries. We determine the sensitivity of our search to high-mass (total mass M>70M>70 MM_\odot) binaries covering eccentricities up to 0.3 at 15 Hz orbital frequency, and use this to compare model predictions to search results. Assuming all detections are indeed quasi-circular, for our fiducial population model, we place an upper limit for the merger rate density of high-mass binaries with eccentricities 0<e0.30 < e \leq 0.3 at 0.330.33 Gpc3^{-3} yr1^{-1} at 90\% confidence level.Comment: 24 pages, 5 figure

    Observation of gravitational waves from the coalescence of a 2.5–4.5 M ⊙ compact object and a neutron star

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    We report the observation of a coalescing compact binary with component masses 2.5–4.5 M ⊙ and 1.2–2.0 M ⊙ (all measurements quoted at the 90% credible level). The gravitational-wave signal GW230529_181500 was observed during the fourth observing run of the LIGO–Virgo–KAGRA detector network on 2023 May 29 by the LIGO Livingston observatory. The primary component of the source has a mass less than 5 M ⊙ at 99% credibility. We cannot definitively determine from gravitational-wave data alone whether either component of the source is a neutron star or a black hole. However, given existing estimates of the maximum neutron star mass, we find the most probable interpretation of the source to be the coalescence of a neutron star with a black hole that has a mass between the most massive neutron stars and the least massive black holes observed in the Galaxy. We provisionally estimate a merger rate density of 55−47+127Gpc−3yr−1 for compact binary coalescences with properties similar to the source of GW230529_181500; assuming that the source is a neutron star–black hole merger, GW230529_181500-like sources may make up the majority of neutron star–black hole coalescences. The discovery of this system implies an increase in the expected rate of neutron star–black hole mergers with electromagnetic counterparts and provides further evidence for compact objects existing within the purported lower mass gap

    Search for eccentric black hole coalescences during the third observing run of LIGO and Virgo

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    Despite the growing number of confident binary black hole coalescences observed through gravitational waves so far, the astrophysical origin of these binaries remains uncertain. Orbital eccentricity is one of the clearest tracers of binary formation channels. Identifying binary eccentricity, however, remains challenging due to the limited availability of gravitational waveforms that include effects of eccentricity. Here, we present observational results for a waveform-independent search sensitive to eccentric black hole coalescences, covering the third observing run (O3) of the LIGO and Virgo detectors. We identified no new high-significance candidates beyond those that were already identified with searches focusing on quasi-circular binaries. We determine the sensitivity of our search to high-mass (total mass M&gt;70 M⊙) binaries covering eccentricities up to 0.3 at 15 Hz orbital frequency, and use this to compare model predictions to search results. Assuming all detections are indeed quasi-circular, for our fiducial population model, we place an upper limit for the merger rate density of high-mass binaries with eccentricities 0&lt;e≤0.3 at 0.33 Gpc−3 yr−1 at 90\% confidence level

    Ultralight vector dark matter search using data from the KAGRA O3GK run

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    Among the various candidates for dark matter (DM), ultralight vector DM can be probed by laser interferometric gravitational wave detectors through the measurement of oscillating length changes in the arm cavities. In this context, KAGRA has a unique feature due to differing compositions of its mirrors, enhancing the signal of vector DM in the length change in the auxiliary channels. Here we present the result of a search for U(1)B−L gauge boson DM using the KAGRA data from auxiliary length channels during the first joint observation run together with GEO600. By applying our search pipeline, which takes into account the stochastic nature of ultralight DM, upper bounds on the coupling strength between the U(1)B−L gauge boson and ordinary matter are obtained for a range of DM masses. While our constraints are less stringent than those derived from previous experiments, this study demonstrates the applicability of our method to the lower-mass vector DM search, which is made difficult in this measurement by the short observation time compared to the auto-correlation time scale of DM

    Improving FBF neurofuzzy approximator by optimised input space covering

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    Simulation and design of a photovoltaic roof for automotive applications

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    This paper presents a software tool that has been developed to model, investigate and simulate the behavior of a photovoltaic generator to be installed on a generic non-planar surface. The innovative aspect of the tool is the possibility to consider any kind of surfaces, also curved and moving ones. The tool has been developed in the Matlab environment and allows the user to analyze any kind of surface simply starting from a CAD file. The tool is very versatile and can be used to analyze the possibility to exploit various available surfaces in order to produce photovoltaic energy in many different applications. As a practical example, the case of using solar cells on the roof of a car has been considered. Results of the simulations are provided in the paper. © 2012 IEEE

    Development of an On-board unit for the monitoring and management of an electric fleet

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    In this paper an on-board unit for the monitoring and management of a small electric fleet is presented. The device includes a communication module and allows to provide data about the vehicles, sending information coming from various sensors to a remote server. Besides locating vehicles on a map and showing their real-time status, the device can also explore the functions of subsystems including energy modules, cells, and powertrain in Electric Vehicles (EV) by showing the instantaneous data and even trend charts. When an error occurs, the system will come with warning and maintenance suggestions; the control center may send short messages to the driver and system data are available for download for advanced engineering analysis. The system foresees a web application and data can be accessed anywhere and anytime thanks to a browser application and a database developed on purpose. The device has been initially tested on a laboratory available vehicle and it will be subsequently installed on the municipal fleet of electric vehicles and boats operating on the small island of Ventotene in Italy in order to investigate the behavior of the main components. The paper illustrates the main hardware characteristics of the system and provides some experimental results. © 2012 IEEE

    Improving accuracy of electric load short-term forecasting by using MoG neural networks

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    Improving the prediction accuracy in electric load forecasting is an important goal to be pursued in order to optimize the management of economic and environmental resources. We propose in this paper a customized prediction approach, which relies on the chaotic behavior of the electric load time series and on the spectral characteristics of its prediction error. The proposed predictor is based on a twofold prediction scheme using Mixture of Gaussian neural networks
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