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Abstract While space-borne optical and near-infrared facilities have succeeded in delivering a precise and spatially resolved picture of our Universe, their small survey area is known to underrepresent the true diversity of galaxy populations. Ground-based surveys have reached comparable depths but at lower spatial resolution, resulting in source confusion that hampers accurate photometry extractions. What once was limited to the infrared regime has now begun to challenge ground-based ultradeep surveys, affecting detection and photometry alike. Failing to address these challenges will mean forfeiting a representative view into the distant Universe. We introduceThe Farmer: an automated, reproducible profile-fitting photometry package that pairs a library of smooth parametric models fromThe Tractorwith a decision tree that determines the best-fit model in concert with neighboring sources. Photometry is measured by fitting the models on other bands leaving brightness free to vary. The resulting photometric measurements are naturally total, and no aperture corrections are required. Supporting diagnostics (e.g.,χ²) enable measurement validation. As fitting models is relatively time intensive,The Farmeris built with high-performance computing routines. We benchmarkThe Farmeron a set of realistic COSMOS-like images and find accurate photometry, number counts, and galaxy shapes.The Farmeris already being utilized to produce catalogs for several large-area deep extragalactic surveys where it has been shown to tackle some of the most challenging optical and near-infrared data available, with the promise of extending to other ultradeep surveys expected in the near future.The Farmeris available to download from GitHub (https://github.com/astroweaver/the_farmer) and Zenodo (https://doi.org/10.5281/zenodo.8205817). 

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 perform an analysis of two-point galaxy clustering and galaxy bias using Subaru Hyper-Suprime Cam (HSC) data taken jointly by the Subaru Strategic Program and the University of Hawaii in the Cosmic Evolution Survey (COSMOS) field over an area of 1.8 sq deg. The depth of the data is similar to the ongoing Hawaii Two-0 (H20) optical galaxy survey, thus the results are indicative of future constraints from tenfold area. We measure the angular autopower spectra of the galaxy overdensity in three redshift bins, defined by dropouts from the g, r, and i bands, and compare them to the theoretical expectation from concordance cosmology with linear galaxy bias. We determine the redshift distribution of each bin using a standard template-based photometric redshift method, coupled with a self-organizing map to quantify colour space coverage. We also investigate sources of systematic errors to inform the methodology and requirements for H20. The linear galaxy bias fit results are $$b_{\mathrm{gal,g}} = 3.90 \pm 0.33 (\mathrm{stat}) \substack{ +0.64 \\ -0.24 } (\mathrm{sys})$$ at redshift z ≃ 3.7, $$b_{\mathrm{gal,r}} = 8.44 \pm 0.63 (\mathrm{stat}) \substack{ +1.42 \\ -0.72 } (\mathrm{sys})$$ at z ≃ 4.7, and $$b_{\mathrm{gal,i}} = 11.94 \pm 2.24 (\mathrm{stat}) \substack{ +1.82 \\ -1.27 } (\mathrm{sys})$$ at z ≃ 5.9.