English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

Photometric redshift estimation with a convolutional neural network: NetZ

MPS-Authors
/persons/resource/persons230163

Schuldt,  S.
Physical Cosmology, MPI for Astrophysics, Max Planck Society;

/persons/resource/persons196332

Suyu,  S. H.
Physical Cosmology, MPI for Astrophysics, Max Planck Society;

/persons/resource/persons256736

Canameras,  R.
Physical Cosmology, MPI for Astrophysics, Max Planck Society;

/persons/resource/persons16154

Taubenberger,  S.
Stellar Astrophysics, MPI for Astrophysics, Max Planck Society;

External Resource
No external resources are shared
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

Schuldt, S., Suyu, S. H., Canameras, R., Taubenberger, S., Meinhardt, T., Leal-Taixé, L., et al. (2021). Photometric redshift estimation with a convolutional neural network: NetZ. Astronomy and Astrophysics, 651: A55. doi:10.1051/0004-6361/202039945.


Cite as: http://hdl.handle.net/21.11116/0000-0009-5F90-A
Abstract
Galaxy redshifts are a key characteristic for nearly all extragalactic studies. Since spectroscopic redshifts require additional telescope and human resources, millions of galaxies are known without spectroscopic redshifts. Therefore, it is crucial to have methods for estimating the redshift of a galaxy based on its photometric properties, the so-called photo-z. We have developed NetZ, a new method using a convolutional neural network (CNN) to predict the photo-z based on galaxy images, in contrast to previous methods that often used only the integrated photometry of galaxies without their images. We use data from the Hyper Suprime-Cam Subaru Strategic Program (HSC SSP) in five different filters as the training data. The network over the whole redshift range between 0 and 4 performs well overall and especially in the high-z range, where it fares better than other methods on the same data. We obtained a precision |zpred − zref| of σ = 0.12 (68% confidence interval) with a CNN working for all galaxy types averaged over all galaxies in the redshift range of 0 to ∼4. We carried out a comparison with a network trained on point-like sources, highlighting the importance of morphological information for our redshift estimation. By limiting the scope to smaller redshift ranges or to luminous red galaxies, we find a further notable improvement. We have published more than 34 million new photo-z values predicted with NetZ. This shows that the new method is very simple and swift in application, and, importantly, it covers a wide redshift range that is limited only by the available training data. It is broadly applicable, particularly with regard to upcoming surveys such as the Rubin Observatory Legacy Survey of Space and Time, which will provide images of billions of galaxies with similar image quality as HSC. Our HSC photo-z estimates are also beneficial to the Euclid survey, given the overlap in the footprints of the HSC and Euclid.