SLICK: Strong Lensing Identification of Candidates Kindred in gravitational wave data
arXiv:2403.02994 · doi:10.1093/mnras/stae2408
Abstract
By the end of the next decade, we hope to have detected strongly lensed gravitational waves by galaxies or clusters. Although there exist optimal methods for identifying lensed signal, it is shown that machine learning (ML) algorithms can give comparable performance but are orders of magnitude faster than non-ML methods. We present the SLICK pipeline which comprises a parallel network based on deep learning. We analyse the Q-transform maps (QT maps) and the Sine-Gaussian maps (SGP-maps) generated for the binary black hole signals injected in Gaussian as well as real noise. We compare our network performance with the previous work and find that the efficiency of our model is higher by a factor of 5 at a false positive rate of 0.001. Further, we show that including SGP maps with QT maps data results in a better performance than analysing QT maps alone. When combined with sky localisation constraints, we hope to get unprecedented accuracy in the predictions than previously possible. We also evaluate our model on the real events detected by the LIGO--Virgo collaboration and find that our network correctly classifies all of them, consistent with non-detection of lensing.
10 pages and 5 figures
References in corpus (30)
- Array Programming with NumPy
- Deep Learning in Neural Networks: An Overview
- GW170817: Observation of Gravitational Waves from a Binary Neutron Star Inspiral
- Multi-messenger Observations of a Binary Neutron Star Merger
- Gravitational Waves and Gamma-rays from a Binary Neutron Star Merger: GW170817 and GRB 170817A
- GWTC-3: Compact Binary Coalescences Observed by LIGO and Virgo During the Second Part of the Third Observing Run
- Open data from the third observing run of LIGO, Virgo, KAGRA and GEO
- Search for gravitational lensing signatures in LIGO-Virgo binary black hole events
- Accelerated gravitational-wave parameter estimation with reduced order modeling
- Please repeat: Strong lensing of gravitational waves as a probe of compact binary and galaxy populations
- Probing Dark Low-mass Halos and Primordial Black Holes with Frequency-dependent Gravitational Lensing Dispersions of Gravitational Waves
- Finding the origin of noise transients in LIGO data with machine learning
- A fast and precise methodology to search for and analyse strongly lensed gravitational-wave events
- Constraints on compact dark matter from gravitational wave microlensing
- Observability of lensing of gravitational waves from massive black hole binaries with LISA
- High-quality strong lens candidates in the final Kilo Degree survey footprint
- Bayesian statistical framework for identifying strongly lensed gravitational-wave signals
- Follow-up Analyses to the O3 LIGO-Virgo-KAGRA Lensing Searches
- Constraining cosmological parameters in FLRW metric with lensed GW+EM signals
- Rapid Identification of Strongly Lensed Gravitational-Wave Events with Machine Learning
- Lensing or luck? False alarm probabilities for gravitational lensing of gravitational waves
- Denoising Gravitational Waves with Enhanced Deep Recurrent Denoising Auto-Encoders
- Improved statistic to identify strongly lensed gravitational wave events
- Multimessenger tests of the weak equivalence principle from GW170817 and its electromagnetic counterparts
- Cosmography using strongly lensed gravitational waves from binary black holes
- Probing wave-optics effects and low-mass dark matter halos with lensing of gravitational waves from massive black holes
- Ordering the confusion: A study of the impact of lens models on gravitational-wave lensing detection capabilities
- Identification of Galaxy-Galaxy Strong Lens Candidates in the DECam Local Volume Exploration Survey Using Machine Learning
- Identifying strongly lensed gravitational waves through their phase consistency
- SiGMa-Net: Deep learning network to distinguish binary black hole signals from short-duration noise transients
Cited by in corpus (5)
- Applications of machine learning in gravitational wave research with current interferometric detectors
- Parameter estimation of microlensed gravitational waves with Conditional Variational Autoencoders
- False positives for gravitational lensing: the gravitational-wave perspective
- Identification of Strongly Lensed Gravitational Wave Events Using Squeeze-and-Excitation Multilayer Perceptron Data-efficient Image Transformer
- Mock Catalogs of Strongly Lensed Gravitational Waves via A Halo Model Approach with Ground-based Detectors