hagabbar/matching_cnn_paper

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hagabbar/matching_cnn_paper

hagabbar

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Matching matched filtering with deep learning in gravitational-wave astronomy

VItamin

Hyper-fast gravitational wave posterior parameter estimation using variational inference. Code used for "Bayesian parameter estimation" paper. Note: This is being repackaged and moving to another repository soon (hagabbar/vitamin_b).

vitamin_c

This will be the first official public release of the VItamin code base. VItamin is a python package for producing fast gravitational wave posterior samples.

cnn_matchfiltering

Pipeline used for classifying BBH, NSBH, and BNS signals in LIGO data. Code used for "Matching Matched Filtering" paper.

Glasgow_Machine_Learning_Course-2019

A hands-on machine learning course for physics and astronomy PhD students at the University of Glasgow. Courses designed by John Armstrong and Hunter Gabbard.

Jupyter Notebook

cINNamon

Invertible neural network for gravitational wave parameter estimation

GenNet

Returns posterior estimates on GW waveform parameters given a GW waveform buried in noise.

VItamin_technical_paper

This is a technical follow-up, to the "OG" VItamin paper.

pycbc_detection_statistic

Attempt during Fulbright Fellowship to make neural network GW classification pipeline.

ligo_ml

General LIGO machine learning repository

VItamin_paper

Estimating Bayesian parameter estimation using conditional variational autoencoders.

VAE_tutorial

Tutorial on variational autoencoders that I've heavily adapted from Google TensorFlow website.

Jupyter Notebook

OzGrav_demo

Demonstration of VItamin code for OzGrav

Pangeo-for-AI-assisted-Climate-Tipping-point-Modelling

This repository contains the code snippets and hybrid models from the DARPA AI-Assisted Climate Tipping Point Modeling (ACTM) program.

Jupyter Notebook

pca_aux_cop

Pipeline to search for couplings and/or trends between different degrees of freedom within the LIGO detectors. Fulbright Fellowship detector characterization project.

Jupyter Notebook