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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 We present the Texas Euclid Survey for Lyα(TESLA), a spectroscopic survey in the 10 deg²of the Euclid North Ecliptic Pole (NEP) field. Using TESLA, we study how the physical properties of Lyαemitters (LAEs) correlate with Lyαemission to understand the escape of Lyαemission from galaxies at redshifts of 2–3.5. We present an analysis of 43 LAEs performed in the NEP field using early data from the TESLA survey. We use Subaru Hyper Suprime-Cam imaging in thegrizybands, Spitzer/IRAC channels 1 and 2 from the Hawaii 20 deg²(H20) survey, and spectra acquired by the Visible Integral-Field Replicable Unit Spectrograph (VIRUS) on the Hobby–Eberly Telescope. We perform spectral energy distribution (SED) fitting to compute the galaxy properties of 43 LAEs, and study correlations between stellar mass, star formation rate (SFR), and dust to the Lyαrest-frame equivalent width (W_Lyα). We uncover marginal (1σsignificance) correlations between stellar mass andW_Lyα, and SFR andW_Lyα, with a Spearman correlation coefficient of −0. ${34}_{-.14}^{+.17}$and −0. ${37}_{-.14}^{+.16}$, respectively. We show that theW_Lyαdistribution of the 43 LAEs is consistent with being drawn from an exponential distribution with an e-folding scale ofW₀= 150 Å. Once complete the TESLA survey will enable the study of ≳50,000 LAEs to explore more correlations between galaxy properties andW_Lyα. The large sample size will allow the construction of a predictive model forW_Lyαas a function of SED-derived galaxy properties, which could be used to improve Lyα-based constraints on reionization.