Search for gravitational-lensing signatures in the full third observing run of the LIGO-Virgo network

Gravitational lensing by massive objects along the line of sight to the source causes distortions of gravitational wave-signals; such distortions may reveal information about fundamental physics, cosmology and astrophysics. In this work, we have extended the search for lensing signatures to all binary black hole events from the third observing run of the LIGO--Virgo network. We search for repeated signals from strong lensing by 1) performing targeted searches for subthreshold signals, 2) calculating the degree of overlap amongst the intrinsic parameters and sky location of pairs of signals, 3) comparing the similarities of the spectrograms amongst pairs of signals, and 4) performing dual-signal Bayesian analysis that takes into account selection effects and astrophysical knowledge. We also search for distortions to the gravitational waveform caused by 1) frequency-independent phase shifts in strongly lensed images, and 2) frequency-dependent modulation of the amplitude and phase due to point masses. None of these searches yields significant evidence for lensing. Finally, we use the non-detection of gravitational-wave lensing to constrain the lensing rate based on the latest merger-rate estimates and the fraction of dark matter composed of compact objects

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

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

Optimal sensor fusion method for active vibration isolation systems in ground-based gravitational-wave detectors

Sensor fusion is a technique used to combine sensors with different noise characteristics into a super sensor that has superior noise performance. To achieve sensor fusion, complementary filters are used in current gravitational-wave detectors to combine relative displacement sensors and inertial sensors for active seismic isolation. Complementary filters are a set of digital filters, which have transfer functions that are summed to unity. Currently, complementary filters are shaped and tuned manually rather than optimized, which can be suboptimal and hard to reproduce for future detectors. In this paper, an optimization-based method called H-infinity synthesis is proposed for synthesizing optimal complementary filters according to the sensor noises themselves. The complementary filter design problem is converted into an optimization problem that seeks minimization of an objective function equivalent to the maximum difference between the super sensor noise and the lower bound in logarithmic scale. The method is exemplified by synthesizing complementary filters for sensor fusion of 1) a relative displacement sensor and an inertial sensor, 2) a relative displacement sensor coupled with seismic noise and an inertial sensor, and 3) hypothetical displacement sensor and inertial sensor, which have slightly different noise characteristics compared to the typical ones. In all cases, the method produces complementary filters that suppress the super sensor noise equally close to the lower bound at all frequencies in logarithmic scale. The synthesized filters contain features that better suppress the sensor noises compared to the pre-designed complementary filters. Overall, the proposed method allows the synthesis of optimal complementary filters according to the sensor noises themselves and is a better and versatile method for solving sensor fusion problems