This work characterises the sky localization and early warning performance of networks of third generation gravitational wave detectors, consisting of different combinations of detectors with either the Einstein Telescope or Cosmic Explorer configura
tion in sites in North America, Europe and Australia. Using a Fisher matrix method which includes the effect of earth rotation, we estimate the sky localization uncertainty for $1.4text{M}odot$-$1.4text{M}odot$ binary neutron star mergers at distances $40text{Mpc}$, $200text{Mpc}$, $400text{Mpc}$, $800text{Mpc}$, $1600text{Mpc}$, and an assumed astrophysical population up to redshift of 2 to characterize its performance for binary neutron star observations. We find that, for binary neutron star mergers at $200text{Mpc}$ and a network consisting of the Einstein Telescope, Cosmic Explorer and an extra Einstein Telescope-like detector in Australia(2ET1CE), the upper limit of the size of the 90% credible region for the best localized 90% signals is $0.51text{deg}^2$. For the simulated astrophysical distribution, this upper limit is $183.58text{deg}^2$. If the Einstein Telescope-like detector in Australia is replaced with a Cosmic Explorer-like detector(1ET2CE), for $200text{Mpc}$ case, the upper limit is $0.36text{deg}^2$, while for astrophysical distribution, it is $113.55text{deg}^2$. We note that the 1ET2CE network can detect 7.2% more of the simulated astrophysical population than the 2ET1CE network. In terms of early warning performance, we find that a network of 2ET1CE and 1ET2CE networks can both provide early warnings of the order of 1 hour prior to merger with sky localization uncertainties of 30 square degrees or less. Our study concludes that the 1ET2CE network is a good compromise between binary neutron stars detection rate, sky localization and early warning capabilities.
We demonstrate the application of a convolutional neural network to the gravitational wave signals from core collapse supernovae. Using simulated time series of gravitational wave detectors, we show that based on the explosion mechanisms, a convoluti
onal neural network can be used to detect and classify the gravitational wave signals buried in noise. For the waveforms used in the training of the convolutional neural network, our results suggest that a network of advanced LIGO, advanced VIRGO and KAGRA, or a network of LIGO A+, advanced VIRGO and KAGRA is likely to detect a magnetorotational core collapse supernovae within the Large and Small Magellanic Clouds, or a Galactic event if the explosion mechanism is the neutrino-driven mechanism. By testing the convolutional neural network with waveforms not used for training, we show that the true alarm probabilities are 52% and 83% at 60 kpc for waveforms R3E1AC and R4E1FC L. For waveforms s20 and SFHx at 10 kpc, the true alarm probabilities are 70% and 93% respectively. All at false alarm probability equal to 10%.
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.
