MADGRAV: a multilevel anomaly-detection pipeline for gravitational-wave searches applied to LIGO data
gr-qc, astro-ph.IM, cs.LG
Submitted: 2026-09-30
Updated: 2026-09-30
Code: https://github.com/ginguglia/MADGRAV
Sources
- FINDCHIRP: an algorithm for detection of gravitational waves from inspiraling compact binaries
- Method for detection and reconstruction of gravitational wave transients with networks of advanced detectors
- GWTC-2.1: Deep Extended Catalog of Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run
- GWTC-3: Compact Binary Coalescences Observed by LIGO and Virgo During the Second Part of the Third Observing Run
- GWTC-4.0: Updating the Gravitational-Wave Transient Catalog with Observations from the First Part of the Fourth LIGO-Virgo-KAGRA Observing Run
- GWTC-4.0: Methods for Identifying and Characterizing Gravitational-wave Transients
- GWTC-5.0: Observations from the Second Part of the Fourth LIGO-Virgo-KAGRA Observing Run and Updates to the Gravitational-Wave Transient Catalog
- GWTC-5.0: An Introduction to Version 5.0 of the Gravitational-Wave Transient Catalog
- Analysis Framework for the Prompt Discovery of Compact Binary Mergers in Gravitational-wave Data
- The PyCBC search for gravitational waves from compact binary coalescence
- The MBTA Pipeline for Detecting Compact Binary Coalescences in the Third LIGO-Virgo Observing Run
- Search for intermediate mass black hole binaries in the third observing run of Advanced LIGO and Advanced Virgo
- GW190521: A Binary Black Hole Merger with a Total Mass of $150 ~ M_{\odot}$
- GW231123: a Binary Black Hole Merger with Total Mass 190-265 $M_{\odot}$
- Deep Neural Networks to Enable Real-time Multimessenger Astrophysics
- Matching matched filtering with deep networks in gravitational-wave astronomy
- MLGWSC-1: The first Machine Learning Gravitational-Wave Search Mock Data Challenge
- A machine-learning pipeline for real-time detection of gravitational waves from compact binary coalescences
- Detection and Classification of Supernova Gravitational Waves Signals: A Deep Learning Approach
- Anomaly Detection in Gravitational Waves data using Convolutional AutoEncoders
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