Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userJoint searches between gravitational-wave interferometers and high-energy neutrino telescopes: science reach and analysis strategies
99
0
0.0
(
0
)
Added by
Veronique Van Elewyck
Publication date
2009
fields
Physics
and research's language is
English
Authors
V. Van Elewyck
Ask ChatGPT about the research
No Arabic abstract
Many of the astrophysical sources and violent phenomena observed in our Universe are potential emitters of gravitational waves (GWs) and high-energy neutrinos (HENs). A network of GW detectors such as LIGO and Virgo can determine the direction/time of GW bursts while the IceCube and ANTARES neutrino telescopes can also provide accurate directional information for HEN events. Requiring the consistency between both, totally independent, detection channels shall enable new searches for cosmic events arriving from potential common sources, of which many extra-galactic objects.
rate research
Read More
We present the baseline multimessenger analysis method for the joint observations of gravitational waves (GW) and high-energy neutrinos (HEN), together with a detailed analysis of the expected science reach of the joint search. The analysis method combines data from GW and HEN detectors, and uses the blue-luminosity-weighted distribution of galaxies. We derive expected GW+HEN source rate upper limits for a wide range of source parameters covering several emission models. Using published sensitivities of externally triggered searches, we derive joint upper limit estimates both for the ongoing analysis with the initial LIGO-Virgo GW detectors with the partial IceCube detector (22 strings) HEN detector and for projected results to advanced LIGO-Virgo detectors with the completed IceCube (86 strings). We discuss the constraints these upper limits impose on some existing GW+HEN emission models.
Compact binary systems emit gravitational radiation which is potentially detectable by current Earth bound detectors. Extracting these signals from the instruments background noise is a complex problem and the computational cost of most current searches depends on the complexity of the source model. Deep learning may be capable of finding signals where current algorithms hit computational limits. Here we restrict our analysis to signals from non-spinning binary black holes and systematically test different strategies by which training data is presented to the networks. To assess the impact of the training strategies, we re-analyze the first published networks and directly compare them to an equivalent matched-filter search. We find that the deep learning algorithms can generalize low signal-to-noise ratio (SNR) signals to high SNR ones but not vice versa. As such, it is not beneficial to provide high SNR signals during training, and fastest convergence is achieved when low SNR samples are provided early on. During testing we found that the networks are sometimes unable to recover any signals when a false alarm probability $<10^{-3}$ is required. We resolve this restriction by applying a modification we call unbounded Softmax replacement (USR) after training. With this alteration we find that the machine learning search retains $geq 97.5%$ of the sensitivity of the matched-filter search down to a false-alarm rate of 1 per month.
With the detection of a binary neutron star system and its corresponding electromagnetic counterparts, a new window of transient astronomy has opened. Due to the size of the error regions, which can span hundreds to thousands of square degrees, there are significant benefits to optimizing tilings for these large sky areas. The rich science promised by gravitational-wave astronomy has led to the proposal for a variety of tiling and time allocation schemes, and for the first time, we make a systematic comparison of some of these methods. We find that differences of a factor of 2 or more in efficiency are possible, depending on the algorithm employed. For this reason, for future surveys searching for electromagnetic counterparts, care should be taken when selecting tiling, time allocation, and scheduling algorithms to maximize the probability of counterpart detection.
During the LIGO and Virgo joint science runs in 2009-2010, gravitational wave (GW) data from three interferometer detectors were analyzed within minutes to select GW candidate events and infer their apparent sky positions. Target coordinates were transmitted to several telescopes for follow-up observations aimed at the detection of an associated optical transient. Images were obtained for eight such GW candidates. We present the methods used to analyze the image data as well as the transient search results. No optical transient was identified with a convincing association with any of these candidates, and none of the GW triggers showed strong evidence for being astrophysical in nature. We compare the sensitivities of these observations to several model light curves from possible sources of interest, and discuss prospects for future joint GW-optical observations of this type.
The most sensitive method for detecting neutrinos at the very highest energies is the lunar Cherenkov technique, which employs the Moon as a target volume, using conventional radio telescopes to monitor it for nanosecond-scale pulses of Cherenkov radiation from particle cascades in its regolith. Multiple-antenna radio telescopes are difficult to effectively combine into a single detector for this purpose, while single antennas are more susceptible to false events from radio interference, which must be reliably excluded for a credible detection to be made. We describe our progress in excluding such interference in our observations with the single-antenna Parkes radio telescope, and our most recent experiment (taking place the week before the ICRC) using it in conjunction with the Australia Telescope Compact Array, exploiting the advantages of both types of telescope.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
