Pix2Prof: fast extraction of sequential information from galaxy imagery via a deep natural language ‘captioning’ model. (arXiv:2010.00622v2 [astro-ph.IM] UPDATED)
<a href="http://arxiv.org/find/astro-ph/1/au:+Smith_M/0/1/0/all/0/1">Michael J. Smith</a> (Hertfordshire), <a href="http://arxiv.org/find/astro-ph/1/au:+Arora_N/0/1/0/all/0/1">Nikhil Arora</a> (Queen's), <a href="http://arxiv.org/find/astro-ph/1/au:+Stone_C/0/1/0/all/0/1">Connor Stone</a> (Queen's), <a href="http://arxiv.org/find/astro-ph/1/au:+Courteau_S/0/1/0/all/0/1">Stéphane Courteau</a> (Queen's), <a href="http://arxiv.org/find/astro-ph/1/au:+Geach_J/0/1/0/all/0/1">James E. Geach</a> (Hertfordshire)
We present ‘Pix2Prof’, a deep learning model that can eliminate any manual
steps taken when extracting galaxy profiles. We argue that a galaxy profile of
any sort is conceptually similar to a natural language image caption. This idea
allows us to leverage image captioning methods from the field of natural
language processing, and so we design Pix2Prof as a float sequence ‘captioning’
model suitable for galaxy profile inference. We demonstrate the technique by
approximating a galaxy surface brightness (SB) profile fitting method that
contains several manual steps. Pix2Prof processes $sim$1 image per second on
an Intel Xeon E5 2650 v3 CPU, improving on the speed of the manual interactive
method by more than two orders of magnitude. Crucially, Pix2Prof requires no
manual interaction, and since galaxy profile estimation is an embarrassingly
parallel problem, we can further increase the throughput by running many
Pix2Prof instances simultaneously. In perspective, Pix2Prof would take under an
hour to infer profiles for $10^5$ galaxies on a single NVIDIA DGX-2 system. A
single human expert would take approximately two years to complete the same
task. Automated methodology such as this will accelerate the analysis of the
next generation of large area sky surveys expected to yield hundreds of
millions of targets. In such instances, all manual approaches — even those
involving a large number of experts — will be impractical.
We present ‘Pix2Prof’, a deep learning model that can eliminate any manual
steps taken when extracting galaxy profiles. We argue that a galaxy profile of
any sort is conceptually similar to a natural language image caption. This idea
allows us to leverage image captioning methods from the field of natural
language processing, and so we design Pix2Prof as a float sequence ‘captioning’
model suitable for galaxy profile inference. We demonstrate the technique by
approximating a galaxy surface brightness (SB) profile fitting method that
contains several manual steps. Pix2Prof processes $sim$1 image per second on
an Intel Xeon E5 2650 v3 CPU, improving on the speed of the manual interactive
method by more than two orders of magnitude. Crucially, Pix2Prof requires no
manual interaction, and since galaxy profile estimation is an embarrassingly
parallel problem, we can further increase the throughput by running many
Pix2Prof instances simultaneously. In perspective, Pix2Prof would take under an
hour to infer profiles for $10^5$ galaxies on a single NVIDIA DGX-2 system. A
single human expert would take approximately two years to complete the same
task. Automated methodology such as this will accelerate the analysis of the
next generation of large area sky surveys expected to yield hundreds of
millions of targets. In such instances, all manual approaches — even those
involving a large number of experts — will be impractical.
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