COSMOS2020: Manifold learning to estimate physical parameters in large galaxy surveys
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COSMOS2020 : Manifold learning to estimate physical parameters in large galaxy surveys. / Davidzon, I.; Jegatheesan, K.; Ilbert, O.; de la Torre, S.; Leslie, S. K.; Laigle, C.; Hemmati, S.; Masters, D. C.; Blanquez-Sese, D.; Kauffmann, O. B.; Magdis, G. E.; McCracken, H. J.; Mobasher, B.; Moneti, A.; Sanders, D. B.; Shuntov, M.; Toft, S.; Weaver, J. R.; Malek, K.
In: Astronomy & Astrophysics, Vol. 665, A34, 06.09.2022.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - COSMOS2020
T2 - Manifold learning to estimate physical parameters in large galaxy surveys
AU - Davidzon, I.
AU - Jegatheesan, K.
AU - Ilbert, O.
AU - de la Torre, S.
AU - Leslie, S. K.
AU - Laigle, C.
AU - Hemmati, S.
AU - Masters, D. C.
AU - Blanquez-Sese, D.
AU - Kauffmann, O. B.
AU - Magdis, G. E.
AU - McCracken, H. J.
AU - Mobasher, B.
AU - Moneti, A.
AU - Sanders, D. B.
AU - Shuntov, M.
AU - Toft, S.
AU - Weaver, J. R.
AU - Malek, K.
PY - 2022/9/6
Y1 - 2022/9/6
N2 - We present a novel method for estimating galaxy physical properties from spectral energy distributions (SEDs) as an alternative to template fitting techniques and based on self-organizing maps (SOMs) to learn the high-dimensional manifold of a photometric galaxy catalog. The method has previously been tested with hydrodynamical simulations in Davidzon et al. (2019, MNRAS, 489, 4817), however, here it is applied to real data for the first time. It is crucial for its implementation to build the SOM with a high-quality panchromatic data set, thus we selected "COSMOS2020" galaxy catalog for this purpose. After the training and calibration steps with COSMOS2020, other galaxies can be processed through SOMs to obtain an estimate of their stellar mass and star formation rate (SFR). Both quantities resulted in a good agreement with independent measurements derived from more extended photometric baseline and, in addition, their combination (i.e., the SFR vs. stellar mass diagram) shows a main sequence of star-forming galaxies that is consistent with the findings of previous studies. We discuss the advantages of this method compared to traditional SED fitting, highlighting the impact of replacing the usual synthetic templates with a collection of empirical SEDs built by the SOM in a "data-driven" way. Such an approach also allows, even for extremely large data sets, for an efficient visual inspection to identify photometric errors or peculiar galaxy types. While also considering the computational speed of this new estimator, we argue that it will play a valuable role in the analysis of oncoming large-area surveys such as Euclid of the Legacy Survey of Space and Time at the Vera C. Rubin Telescope.
AB - We present a novel method for estimating galaxy physical properties from spectral energy distributions (SEDs) as an alternative to template fitting techniques and based on self-organizing maps (SOMs) to learn the high-dimensional manifold of a photometric galaxy catalog. The method has previously been tested with hydrodynamical simulations in Davidzon et al. (2019, MNRAS, 489, 4817), however, here it is applied to real data for the first time. It is crucial for its implementation to build the SOM with a high-quality panchromatic data set, thus we selected "COSMOS2020" galaxy catalog for this purpose. After the training and calibration steps with COSMOS2020, other galaxies can be processed through SOMs to obtain an estimate of their stellar mass and star formation rate (SFR). Both quantities resulted in a good agreement with independent measurements derived from more extended photometric baseline and, in addition, their combination (i.e., the SFR vs. stellar mass diagram) shows a main sequence of star-forming galaxies that is consistent with the findings of previous studies. We discuss the advantages of this method compared to traditional SED fitting, highlighting the impact of replacing the usual synthetic templates with a collection of empirical SEDs built by the SOM in a "data-driven" way. Such an approach also allows, even for extremely large data sets, for an efficient visual inspection to identify photometric errors or peculiar galaxy types. While also considering the computational speed of this new estimator, we argue that it will play a valuable role in the analysis of oncoming large-area surveys such as Euclid of the Legacy Survey of Space and Time at the Vera C. Rubin Telescope.
KW - galaxies
KW - fundamental parameters
KW - star formation
KW - stellar content
KW - methods
KW - observational
KW - astronomical databases
KW - miscellaneous
KW - STAR-FORMATION RATES
KW - SPECTRAL ENERGY-DISTRIBUTIONS
KW - STELLAR POPULATION SYNTHESIS
KW - NEAR-INFRARED SURVEY
KW - PHOTOMETRIC REDSHIFTS
KW - FORMING GALAXIES
KW - H-ALPHA
KW - COMPLETE CALIBRATION
KW - MASSIVE GALAXIES
KW - GALACTIC STELLAR
U2 - 10.1051/0004-6361/202243249
DO - 10.1051/0004-6361/202243249
M3 - Journal article
VL - 665
JO - Astronomy & Astrophysics
JF - Astronomy & Astrophysics
SN - 0004-6361
M1 - A34
ER -
ID: 319117047