Efficient automated detection of flux-transient, reoccurring flux-variable, and moving objects is increasingly important for large-scale astronomical surveys. braai is a convolutional-neural-network, deep-learning real/bogus classifier designed to separate genuine astrophysical events and objects from false positive, or bogus, detections in the data of the Zwicky Transient Facilty (ZTF), a new robotic time-domain survey currently in operation at the Palomar Observatory in California, USA. braai demonstrates a state-of-the-art performance as quantified by its low false negative and false positive rates.

For details, please see Duev et al. 2019, MNRAS, 489 (3), 3582-3590.

arXiv:1907.11259

todo: plots

See this jupyter notebook

See this jupyter notebook, or

See this jupyter notebook, or

Build and launch the app container:

# without GPU support:
docker build --rm -t braai:cpu -f Dockerfile .
# with GPU support (requires nvidia-docker):
docker build --rm -t braai:gpu -f gpu.Dockerfile .

# run:
# without GPU support:
docker run -it --rm --name braai -v /path/to/store/data:/data braai:cpu
# with GPU support (requires nvidia-docker) exposing the first GPU:
docker run --runtime=nvidia -e NVIDIA_VISIBLE_DEVICES=0 -it --rm --name braai -v /path/to/store/data:/data braai:gpu

Train braai:

python /app/braai.py --t_stamp 20190614_003916 --model VGG6 --epochs 200 --patience 50 --batch_size 64 --verbose