Science Score: 67.0%
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Repository
Waveform anomaly detector
Basic Info
Statistics
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 1
Metadata Files
README.md
Waveform AnomaLy DetectOr (WALDO)
WALDO is a deep learning data quality tool developed to flag possible anomalous Gravitational Waves (GW) from Numerical Relativity (NR) catalogs. We use a U-Net architecture to learn the waveform features of a dataset. These waveforms are timeseries $h_{lm}(t)$ of modes $(l,m)$ from the spin-weighted spherical harmonics decomposition of the GW strain $h(t,\vec x)$,
$$h{lm}(t) = \int d\Omega h(t, \vec x)_{-2}Y{lm}^*(\theta, \phi) .$$
WALDO computes the mismatch between $h{lm}(t)$ and its prediction $\bar h{lm}(t)$ to compose a histogram. We can identify anomalous waveforms by isolating 1% of the highest measurement values. Below, the anomaly search associated with the radiation field $\psi{32} = \ddot h{32}$ from the dataset.
Installation
To install WALDO, we can use the pip command:
pip install grav-waldo
Content
The project is composed of three main codes: * wfdset.py: for pre-processing NR dataset; * unet.py: the neural network; * waldo.py: for mismatch evaluation and anomaly search.
Check the tutorials in docs.
Reference
WALDO's paper: Deep learning waveform anomaly detector for numerical relativity catalogs.
Owner
- Login: tiberioap
- Kind: user
- Repositories: 1
- Profile: https://github.com/tiberioap
Citation (CITATION.cff)
cff-version: 0.1.0
message: "If you use this software, please cite it as below."
authors:
- family-names: Pereira
given-names: Tiberio
orcid: https://orcid.org/0000-0003-1856-6881
title: "Waveform AnomaLy detectOr (WALDO)"
version: First-release
doi: 10.5281/zenodo.7127963
date-released: 2022-09-28
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Dependencies
- h5py *
- matplotlib *
- numpy *
- scipy *
- tensorflow *