arXiv subjects

Otto Akseli Hannuksela

Publications and source records attributed to Otto Akseli Hannuksela.

4 recordsLinked to original sources

arXiv2026

ler: LVK (LIGO-Virgo-KAGRA collaboration) event (compact-binary mergers) rate calculator and simulator

ler is a Python package for simulating compact-binary gravitational-wave populations and estimating detectable event rates for current and future LIGO-Virgo-KAGRA detector networks. The package provides a unified framework for unlensed and strongly lensed binary black hole, binary neutron star, and neutron star-black hole mergers. It samples source and lens populations, evaluates detector selection effects, solves lens equations for strongly lensed systems, and computes image properties such as magnifications and time delays. The framework supports multiple source-population and lens models, including SIS, SIE, and EPL plus external shear, and allows users to replace default distributions and detection criteria through modular interfaces. Computational efficiency is obtained through vectorized sampling, inverse-transform and importance-sampling strategies, multiprocessing, and just-in-time compiled routines. ler is designed for large-scale Monte Carlo studies in which rates and selected populations must be evaluated repeatedly, including forecasts for observing runs, studies of lensing candidate validation, and selection-function calculations for population inference. The package is distributed with documentation (https://ler.hemantaph.com/), validation examples, and reproducible workflows.

astro-ph.IM↗

arXiv2026

gwsnr: A Python package for efficient signal-to-noise ratio calculations of gravitational waves

$gwsnr$ is a Python package for efficient signal-to-noise ratio and detectability calculations for compact-binary gravitational-wave sources. It is designed for large simulated populations where repeated evaluation of the optimal signal-to-noise ratio, $ρ_{\rm opt}$, and the probability of detection, $P_{\rm det}$, becomes computationally expensive with direct noise-weighted inner-product calculations. The package provides multiple calculation pathways under a unified interface, including multiprocessing-based inner-product evaluation, partial-scaling interpolation for non-spinning and aligned-spin systems, JAX and MLX backends for accelerated array execution, and artificial neural network based detectability estimation for more complex waveform settings. It also supports statistical modelling of the observed signal-to-noise ratio, $ρ_{\rm obs}$, under stationary Gaussian noise assumptions, threshold estimation from injection catalogues, hybrid recalculation of marginal events near the detection boundary, and horizon-distance calculations. By combining fast numerical methods with configurable detector, waveform, and population settings, $gwsnr$ enables efficient selection-effect modelling, rate estimation, detector-sensitivity studies, and large-scale compact-binary population simulations. The package is publicly available with documentation, validation examples, and reproducible workflows.

astro-ph.IM↗

arXiv2024

On the detection and precise localisation of merging black holes events through strong gravitational lensing

To unlock the full spectrum of astrophysical and cosmological applications of gravitational-wave detections, it is essential to localise the associated black-hole mergers to high precision inside their host galaxies. One possible method to achieve this is to compare the properties of multiple detections of gravitationally-lensed binary black-hole merger events with the properties of strong gravitational lens systems located in the joint sky localisation of the gravitational-wave detections. In this work, we simulate the population of binary black-hole mergers lensed by galaxy-scale lenses and detectable by LIGO-Virgo-Kagra in the coming decade and the population of galaxy-scale strong lenses that will be detected by Euclid. We use these simulations to investigate the prospects for localising strongly lensed binary black-hole mergers inside the lensed galaxies of 'Euclid-like' galaxy-scale strong lenses. We find that for 20-50% of strongly lensed gravitational wave events the lens system is detectable with Euclid, if the event falls in its survey footprint. Of these, we expect to correctly identify the strongly-lensed host galaxy as likely (with posterior probability) host galaxy - based on Bayesian evidence ranking of candidate hosts - for 34.6-21.9% of quadruply-lensed gravitational-wave events when given an a-priori 1-5 deg^2 gravitational-wave-only sky localisation. For triply and doubly lensed gravitational-wave events, this becomes 29.8-14.9% and 16.4-6.6% respectively. If successfully identified, however, the localisation can be better than a fraction of the host-galaxy size, i.e. of order milli-arcseconds. A first detection in the coming decade, however, probably requires dedicated deep and high-resolution follow-ups and continued upgrades in the current and planned gravitational-wave detectors.

astro-ph.HE↗

arXiv2021

Identification of Lensed Gravitational Waves with Deep Learning

Similar to light, gravitational waves (GWs) can be lensed. Such lensing phenomena can magnify the waves, create multiple images observable as repeated events, and superpose several waveforms together, inducing potentially discernible patterns on the waves. In particular, when the lens is small, $\lesssim 10^5 M_\odot$, it can produce lensed images with time delays shorter than the typical gravitational-wave signal length that conspire together to form ``beating patterns''. We present a proof-of-principle study utilizing deep learning for identification of such a lensing signature. We bring the excellence of state-of-the-art deep learning models at recognizing foreground objects from background noises to identifying lensed GWs from noise present spectrograms. We assume the lens mass is around $10^3 M_\odot$ -- $10^5 M_\odot$, which can produce the order of millisecond time delays between two images of lensed GWs. We discuss the feasibility of distinguishing lensed GWs from unlensed ones and estimating physical and lensing parameters. Suggested method may be of interest to the study of more complicated lensing configurations for which we do not have accurate waveform templates.

gr-qc↗