%0 Journal Article %1 journals/corr/abs-2004-09524 %A Khan, Asad %A Huerta, E. A. %A Das, Arnav %D 2020 %J CoRR %K dblp %T Physics-inspired deep learning to characterize the signal manifold of quasi-circular, spinning, non-precessing binary black hole mergers. %U http://dblp.uni-trier.de/db/journals/corr/corr2004.html#abs-2004-09524 %V abs/2004.09524

@article{journals/corr/abs-2004-09524, added-at = {2020-04-29T00:00:00.000+0200}, author = {Khan, Asad and Huerta, E. A. and Das, Arnav}, biburl = {https://www.bibsonomy.org/bibtex/2d103d1189572f1343c391f9a841f112f/dblp}, ee = {https://arxiv.org/abs/2004.09524}, interhash = {7d4271237dfbb8e433d5ce29b917fb2a}, intrahash = {d103d1189572f1343c391f9a841f112f}, journal = {CoRR}, keywords = {dblp}, timestamp = {2020-04-30T11:38:12.000+0200}, title = {Physics-inspired deep learning to characterize the signal manifold of quasi-circular, spinning, non-precessing binary black hole mergers.}, url = {http://dblp.uni-trier.de/db/journals/corr/corr2004.html#abs-2004-09524}, volume = {abs/2004.09524}, year = 2020 }