DeepStreaks: identifying fast-moving objects in the Zwicky Transient Facility data with deep learning. (arXiv:1904.05920v1 [astro-ph.IM])

DeepStreaks: identifying fast-moving objects in the Zwicky Transient Facility data with deep learning. (arXiv:1904.05920v1 [astro-ph.IM])
<a href="http://arxiv.org/find/astro-ph/1/au:+Duev_D/0/1/0/all/0/1">Dmitry A. Duev</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Mahabal_A/0/1/0/all/0/1">Ashish Mahabal</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Ye_Q/0/1/0/all/0/1">Quanzhi Ye</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Tirumala_K/0/1/0/all/0/1">Kushal Tirumala</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Belicki_J/0/1/0/all/0/1">Justin Belicki</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Dekany_R/0/1/0/all/0/1">Richard Dekany</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Frederick_S/0/1/0/all/0/1">Sara Frederick</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Graham_M/0/1/0/all/0/1">Matthew J. Graham</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Laher_R/0/1/0/all/0/1">Russ R. Laher</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Masci_F/0/1/0/all/0/1">Frank J. Masci</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Prince_T/0/1/0/all/0/1">Thomas A. Prince</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Riddle_R/0/1/0/all/0/1">Reed Riddle</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Rosnet_P/0/1/0/all/0/1">Philippe Rosnet</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Soumagnac_M/0/1/0/all/0/1">Maayane T. Soumagnac</a>

We present DeepStreaks, a convolutional-neural-network, deep-learning system
designed to efficiently identify streaking fast-moving near-Earth objects that
are detected in the data of the Zwicky Transient Facility (ZTF), a wide-field,
time-domain survey using a dedicated 47 sq. deg camera attached to the Samuel
Oschin 48-inch Telescope at the Palomar Observatory in California, United
States. The system demonstrates a 96-98% true positive rate, depending on the
night, while keeping the false positive rate below 1%. The sensitivity of
DeepStreaks is quantified by the performance on the test data sets as well as
using known near-Earth objects observed by ZTF. The system is deployed and
adapted for usage within the ZTF Solar-System framework and has significantly
reduced human involvement in the streak identification process, from several
hours to typically under 10 minutes per day.

We present DeepStreaks, a convolutional-neural-network, deep-learning system
designed to efficiently identify streaking fast-moving near-Earth objects that
are detected in the data of the Zwicky Transient Facility (ZTF), a wide-field,
time-domain survey using a dedicated 47 sq. deg camera attached to the Samuel
Oschin 48-inch Telescope at the Palomar Observatory in California, United
States. The system demonstrates a 96-98% true positive rate, depending on the
night, while keeping the false positive rate below 1%. The sensitivity of
DeepStreaks is quantified by the performance on the test data sets as well as
using known near-Earth objects observed by ZTF. The system is deployed and
adapted for usage within the ZTF Solar-System framework and has significantly
reduced human involvement in the streak identification process, from several
hours to typically under 10 minutes per day.

http://arxiv.org/icons/sfx.gif

Comments are closed.