Kavli Affiliate: Hu Zhan
| First 5 Authors: Chen Qiu, Nicola R. Napolitano, Rui Li, Yuedong Fang, Crescenzo Tortora
| Summary:
Bulge-disk (B-D) decomposition is an effective diagnostic to characterize the
galaxy morphology and understand its evolution across time. So far,
high-quality data have allowed detailed B-D decomposition to redshift below
0.5, with limited excursions over small volumes at higher redshifts.
Next-generation large sky space surveys in optical, e.g. from the China Space
Station Telescope (CSST), and near-infrared, e.g. from the space EUCLID
mission, will produce a gigantic leap in these studies as they will provide
deep, high-quality photometric images over more than 15000 deg2 of the sky,
including billions of galaxies. Here, we extend the use of the Galaxy Light
profile neural Network (GaLNet) to predict 2-S’ersic model parameters,
specifically from CSST data. We simulate point-spread function (PSF) convolved
galaxies, with realistic B-D parameter distributions, on CSST mock observations
to train the new GaLNet and predict the structural parameters (e.g. magnitude,
effective radius, Sersic index, axis ratio, etc.) of both bulge and disk
components. We find that the GaLNet can achieve very good accuracy for most of
the B-D parameters down to an $r$-band magnitude of 23.5 and redshift $sim$1.
The best accuracy is obtained for magnitudes, implying accurate bulge-to-total
(B/T) estimates. To further forecast the CSST performances, we also discuss the
results of the 1-S’ersic GaLNet and show that CSST half-depth data will allow
us to derive accurate 1-component models up to $rsim$24 and redshift
z$sim$1.7.
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