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Abstract We present a new method based on information theory to find the optimal number of bands required to measure the physical properties of galaxies with desired accuracy. As a proof of concept, using the recently updated COSMOS catalog (COSMOS2020), we identify the most relevant wave bands for measuring the physical properties of galaxies in a Hawaii Two-0- (H20) and UVISTA-like survey for a sample ofi< 25 AB mag galaxies. We find that with the availablei-band fluxes,r,u, IRAC/ch2, andzbands provide most of the information regarding the redshift with importance decreasing fromrband tozband. We also find that for the same sample, IRAC/ch2,Y,r, andubands are the most relevant bands in stellar-mass measurements with decreasing order of importance. Investigating the intercorrelation between the bands, we train a model to predict UVISTA observations in near-IR from H20-like observations. We find that magnitudes in theYJHbands can be simulated/predicted with an accuracy of 1σmag scatter ≲0.2 for galaxies brighter than 24 AB mag in near-IR bands. One should note that these conclusions depend on the selection criteria of the sample. For any new sample of galaxies with a different selection, these results should be remeasured. Our results suggest that in the presence of a limited number of bands, a machine-learning model trained over the population of observed galaxies with extensive spectral coverage outperforms template fitting. Such a machine-learning model maximally comprises the information acquired over available extensive surveys and breaks degeneracies in the parameter space of template fitting inevitable in the presence of a few bands. 

ABSTRACT Although photometric redshifts (photo-z’s) are crucial ingredients for current and upcoming large-scale surveys, the high-quality spectroscopic redshifts currently available to train, validate, and test them are substantially non-representative in both magnitude and colour. We investigate the nature and structure of this bias by tracking how objects from a heterogeneous training sample contribute to photo-z predictions as a function of magnitude and colour, and illustrate that the underlying redshift distribution at fixed colour can evolve strongly as a function of magnitude. We then test the robustness of the galaxy–galaxy lensing signal in 120 deg2 of HSC–SSP DR1 data to spectroscopic completeness and photo-z biases, and find that their impacts are sub-dominant to current statistical uncertainties. Our methodology provides a framework to investigate how spectroscopic incompleteness can impact photo-z-based weak lensing predictions in future surveys such as LSST and WFIRST.