A Morphological Classification Model to Identify Unresolved PanSTARRS1 Sources: Application in the ZTF Real-Time Pipeline. (arXiv:1902.01935v1 [astro-ph.IM])
<a href="http://arxiv.org/find/astro-ph/1/au:+Tachibana_Y/0/1/0/all/0/1">Yutaro Tachibana</a> (Tokyo Institute of Technology), <a href="http://arxiv.org/find/astro-ph/1/au:+Miller_A/0/1/0/all/0/1">A. A. Miller</a> (Northwestern/CIERA)
In the era of large photometric surveys, the importance of automated and
accurate classification is rapidly increasing. Specifically, the separation of
resolved and unresolved sources in astronomical imaging is a critical initial
step for a wide array of studies, ranging from Galactic science to large scale
structure and cosmology. Here, we present our method to construct a large, deep
catalog of point sources utilizing Pan-STARRS1 (PS1) 3$pi$ survey data, which
consists of $sim$3$times10^9$ sources with $mlesssim23.5,$mag. We develop a
supervised machine-learning methodology, using the random forest (RF)
algorithm, to construct the PS1 morphology model. We train the model using
$sim$5$times10^4$ PS1 sources with HST COSMOS morphological classifications
and assess its performance using $sim$4$times10^6$ sources with Sloan Digital
Sky Survey (SDSS) spectra and $sim$2$times10^8$ textit{Gaia} sources. We
construct 11 “white flux” features, which combine PS1 flux and shape
measurements across 5 filters, to increase the signal-to-noise ratio relative
to any individual filter. The RF model is compared to 3 alternative models,
including the SDSS and PS1 photometric classification models, and we find that
the RF model performs best. By number the PS1 catalog is dominated by faint
sources ($mgtrsim21,$mag), and in this regime the RF model significantly
outperforms the SDSS and PS1 models. For time-domain surveys, identifying
unresolved sources is crucial for inferring the Galactic or extragalactic
origin of new transients. We have classified $sim$1.5$times10^9$ sources
using the RF model, and these results are used within the Zwicky Transient
Facility real-time pipeline to automatically reject stellar sources from the
extragalactic alert stream.
In the era of large photometric surveys, the importance of automated and
accurate classification is rapidly increasing. Specifically, the separation of
resolved and unresolved sources in astronomical imaging is a critical initial
step for a wide array of studies, ranging from Galactic science to large scale
structure and cosmology. Here, we present our method to construct a large, deep
catalog of point sources utilizing Pan-STARRS1 (PS1) 3$pi$ survey data, which
consists of $sim$3$times10^9$ sources with $mlesssim23.5,$mag. We develop a
supervised machine-learning methodology, using the random forest (RF)
algorithm, to construct the PS1 morphology model. We train the model using
$sim$5$times10^4$ PS1 sources with HST COSMOS morphological classifications
and assess its performance using $sim$4$times10^6$ sources with Sloan Digital
Sky Survey (SDSS) spectra and $sim$2$times10^8$ textit{Gaia} sources. We
construct 11 “white flux” features, which combine PS1 flux and shape
measurements across 5 filters, to increase the signal-to-noise ratio relative
to any individual filter. The RF model is compared to 3 alternative models,
including the SDSS and PS1 photometric classification models, and we find that
the RF model performs best. By number the PS1 catalog is dominated by faint
sources ($mgtrsim21,$mag), and in this regime the RF model significantly
outperforms the SDSS and PS1 models. For time-domain surveys, identifying
unresolved sources is crucial for inferring the Galactic or extragalactic
origin of new transients. We have classified $sim$1.5$times10^9$ sources
using the RF model, and these results are used within the Zwicky Transient
Facility real-time pipeline to automatically reject stellar sources from the
extragalactic alert stream.
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