Cover Image

LearningMatch: Siamese Neural Network Learns the Match Manifold

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Green, Susanna, Lundgren, Andrew, Morice-Atkinson, Xan
Format:	Preprint
Published:	2025
Subjects:	General Relativity and Quantum Cosmology Instrumentation and Methods for Astrophysics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

TemplateGeNN: Neural Networks used to accelerate Gravitational Wave Template Bank Generation
by: Green, Susanna, et al.
Published: (2025)

Revisiting the evidence for precession in GW200129 with machine learning noise mitigation
by: Macas, Ronaldas, et al.
Published: (2023)

A field-level reaction for screened modified gravity
by: Saadeh, Daniela, et al.
Published: (2025)

A field-level emulator for modified gravity
by: Saadeh, Daniela, et al.
Published: (2024)

Measuring the rate of glitches in interferometric gravitational wave detectors with a hierarchical Bayesian model
by: Ashton, Gregory, et al.
Published: (2026)

Efficient Reconstruction of Matched-Filter Signal-to-Noise Ratio Time Series from Nearby Templates for Compact Binary Coalescences Searches
by: Murakami, Yasuhiro, et al.
Published: (2025)

Inference with finite time series II: the window strikes back
by: Talbot, Colm, et al.
Published: (2025)

How Many Times Should We Matched Filter Gravitational Wave Data? A Comparison of GstLAL's Online and Offline Performance
by: Joshi, Prathamesh, et al.
Published: (2025)

Neural Network Analysis of S2-Star Dynamics: Extended mass
by: Galikyan, N., et al.
Published: (2024)

PINNGraPE: Physics Informed Neural Network for Gravitational wave Parameter Estimation
by: Smith, Leigh, et al.
Published: (2025)

Real-Time Detection of Unmodelled Gravitational-Wave Transients Using Convolutional Neural Networks
by: Skliris, Vasileios, et al.
Published: (2020)

Inferring Neutron-Star Properties from Post-merger Gravitational-wave Spectra with Neural Networks
by: Pesios, Dimitrios, et al.
Published: (2026)

Large-kernel Convolutional Neural Networks for Wide Parameter-Space Searches of Continuous Gravitational Waves
by: Joshi, Prasanna Mohan, et al.
Published: (2024)

Searches for Compact Binary Coalescence Events Using Neural Networks in LIGO/Virgo Third Observation Period
by: Menéndez-Vázquez, Alexis, et al.
Published: (2024)

Deep Neural Emulation of the Supermassive Black-hole Binary Population
by: Laal, Nima, et al.
Published: (2024)

Microseismic Noise Mitigation with Machine Learning for Advanced LIGO
by: Reissel, Christina, et al.
Published: (2025)

Machine Learning to assess astrophysical origin of gravitational waves triggers
by: Mobilia, Lorenzo, et al.
Published: (2025)

A Deep Learning Framework for Amplitude Generation of Generic EMRIs
by: Zeng, Yan-bo, et al.
Published: (2026)

Identifying Black Holes Through Space Telescopes and Deep Learning
by: Fang, Yeqi, et al.
Published: (2024)

Identifying and Mitigating Machine Learning Biases for the Gravitational Wave Detection Problem
by: Nagarajan, Narenraju, et al.
Published: (2025)

Binary Neutron Star Merger Search Pipeline Powered by Deep Learning
by: McLeod, Alistair, et al.
Published: (2024)

Novel Deep Learning Approach to Detecting Binary Black Hole Mergers
by: Beveridge, Damon, et al.
Published: (2023)

Deep Learning Search for Gravitational Waves from Compact Binary Coalescence
by: Mobilia, Lorenzo, et al.
Published: (2026)

Detection and Mitigation of Glitches in LISA Data: A Machine Learning Approach
by: Houba, Niklas, et al.
Published: (2024)

Accelerating Stochastic Gravitational Wave Backgrounds Parameter Estimation in Pulsar Timing Arrays with Flow Matching
by: Liang, Bo, et al.
Published: (2024)

Evaluating Deep Learning Models for Multiclass Classification of LIGO Gravitational-Wave Glitches
by: Manoharan, Rudhresh, et al.
Published: (2026)

Detection and Prediction of Future Massive Black Hole Mergers with Machine Learning and Truncated Waveforms
by: Houba, Niklas, et al.
Published: (2024)

PyMerger: Detecting Binary Black Hole merger from Einstein Telescope Using Deep Learning
by: Alhassan, Wathela, et al.
Published: (2023)

Robustness of Deep Learning Models to Precession in Gravitational-Wave Searches for Intermediate-Mass Black Hole Binaries
by: Meijer, Quirijn, et al.
Published: (2024)

Machine Learning based Glitch Veto for inspiral binary merger signals using Linear Chirp Transform
by: Arutkeerthi, N., et al.
Published: (2024)

Classifying the unknown: discovering novel gravitational-wave detector glitches using similarity learning
by: Coughlin, S B, et al.
Published: (2019)

Neural density estimation for Galactic Binaries in LISA data analysis
by: Korsakova, Natalia, et al.
Published: (2024)

BinaryGFH-v2: Improved method to search for gravitational waves from sub-solar-mass, ultra-compact binaries using the Generalized Frequency-Hough Transform
by: Miller, Andrew L., et al.
Published: (2025)

Searching for continuous gravitational waves from highly deformed compact objects with DECIGO
by: Miller, Andrew L., et al.
Published: (2025)

Assessing Matched Filtering for Core-Collapse Supernova Gravitational-Wave Detection
by: Andresen, Haakon, et al.
Published: (2024)

Semi-Supervised Learning for Lensed Quasar Detection
by: Sweeney, David, et al.
Published: (2025)

Enabling multi-messenger astronomy with continuous gravitational waves: early warning and sky localization of binary neutron stars in Einstein Telescope
by: Miller, Andrew L., et al.
Published: (2023)

Pattern-recognition techniques to search for gravitational waves from inspiraling, dark-dressed primordial black holes
by: Sethi, Charchit Kumar, et al.
Published: (2025)

Analysing one- and two-bit data to reduce memory requirements for F-statistic-based gravitational wave searches
by: Clearwater, Patrick, et al.
Published: (2024)

Likelihood for a Network of Gravitational-Wave Detectors with Correlated Noise
by: Cireddu, Francesco, et al.
Published: (2023)