We report for the first time a method-independent geometrical expression for the angular resolution of an arbitrary network of interferometric gravitational wave (GW) detectors when the arrival-time of a GW is unknown. We discuss the implications of
our results on how to improve angular resolutions of a GW network and on improvements of localization methods. An example of an improvement to the null-stream localization method for GWs of unknown waveforms is demonstrated.
The planned Laser Interferometer Space Antenna (LISA) is expected to detect gravitational wave signals from ~100 extreme-mass-ratio inspirals (EMRIs) of stellar-mass compact objects into massive black holes. The long duration and large parameter spac
e of EMRI signals makes data analysis for these signals a challenging problem. One approach to EMRI data analysis is to use time-frequency methods. This consists of two steps: (i) searching for tracks from EMRI sources in a time-frequency spectrogram, and (ii) extracting parameter estimates from the tracks. In this paper we discuss the results of applying these techniques to the latest round of the Mock LISA Data Challenge, Round 1B. This analysis included three new techniques not used in previous analyses: (i) a new Chirp-based Algorithm for Track Search for track detection; (ii) estimation of the inclination of the source to the line of sight; (iii) a Metropolis-Hastings Monte Carlo over the parameter space in order to find the best fit to the tracks.
Extreme-mass-ratio inspirals (EMRIs) of ~ 1-10 solar-mass compact objects into ~ million solar-mass massive black holes can serve as excellent probes of strong-field general relativity. The Laser Interferometer Space Antenna (LISA) is expected to det
ect gravitational wave signals from apprxomiately one hundred EMRIs per year, but the data analysis of EMRI signals poses a unique set of challenges due to their long duration and the extensive parameter space of possible signals. One possible approach is to carry out a search for EMRI tracks in the time-frequency domain. We have applied a time-frequency search to the data from the Mock LISA Data Challenge (MLDC) with promising results. Our analysis used the Hierarchical Algorithm for Clusters and Ridges to identify tracks in the time-frequency spectrogram corresponding to EMRI sources. We then estimated the EMRI source parameters from these tracks. In these proceedings, we discuss the results of this analysis of the MLDC round 1.3 data.
