COSMOS2020: A Panchromatic View of the Universe to z ∼ 10 from Two Complementary Catalogs

The Cosmic Evolution Survey (COSMOS) has become a cornerstone of extragalactic astronomy. Since the last public catalog in 2015, a wealth of new imaging and spectroscopic data have been collected in the COSMOS field. This paper describes the collection, processing, and analysis of these new imaging data to produce a new reference photometric redshift catalog. Source detection and multiwavelength photometry are performed for 1.7 million sources across the 2 deg2 of the COSMOS field, ∼966,000 of which are measured with all available broadband data using both traditional aperture photometric methods and a new profile-fitting photometric extraction tool, The Farmer, which we have developed. A detailed comparison of the two resulting photometric catalogs is presented. Photometric redshifts are computed for all sources in each catalog utilizing two independent photometric redshift codes. Finally, a comparison is made between the performance of the photometric methodologies and of the redshift codes to demonstrate an exceptional degree of self-consistency in the resulting photometric redshifts. The i < 21 sources have subpercent photometric redshift accuracy and even the faintest sources at 25 < i < 27 reach a precision of 5%. Finally, these results are discussed in the context of previous, current, and future surveys in the COSMOS field. Compared to COSMOS2015, it reaches the same photometric redshift precision at almost one magnitude deeper. Both photometric catalogs and their photometric redshift solutions and physical parameters will be made available through the usual astronomical archive systems (ESO Phase 3, IPAC-IRSA, and CDS).

Mapping Obscuration to Reionization with ALMA (MORA): 2 mm Efficiently Selects the Highest-redshift Obscured Galaxies

We present the characteristics of 2 mm selected sources from the largest Atacama Large Millimeter/submillimeter Array (ALMA) blank-field contiguous survey conducted to date, the Mapping Obscuration to Reionization with ALMA (MORA) survey covering 184 arcmin2 at 2 mm. Twelve of 13 detections above 5σ are attributed to emission from galaxies, 11 of which are dominated by cold dust emission. These sources have a median redshift of 〈 z 2 mm 〉 = 3.6 − 0.3 + 0.4 primarily based on optical/near-infrared photometric redshifts with some spectroscopic redshifts, with 77% ± 11% of sources at z > 3 and 38% ± 12% of sources at z > 4. This implies that 2 mm selection is an efficient method for identifying the highest-redshift dusty star-forming galaxies (DSFGs). Lower-redshift DSFGs (z < 3) are far more numerous than those at z > 3 yet are likely to drop out at 2 mm. MORA shows that DSFGs with star formation rates in excess of 300 M ⊙ yr−1 and a relative rarity of ∼10−5 Mpc−3 contribute ∼30% to the integrated star formation rate density at 3 < z < 6. The volume density of 2 mm selected DSFGs is consistent with predictions from some cosmological simulations and is similar to the volume density of their hypothesized descendants: massive, quiescent galaxies at z > 2. Analysis of MORA sources’ spectral energy distributions hint at steeper empirically measured dust emissivity indices than reported in typical literature studies, with 〈 β 〉 = 2.2 − 0.4 + 0.5 . The MORA survey represents an important step in taking census of obscured star formation in the universe’s first few billion years, but larger area 2 mm surveys are needed to more fully characterize this rare population and push to the detection of the universe’s first dusty galaxies.

Estimation of Photometric Redshifts. I. Machine-learning Inference for Pan-STARRS1 Galaxies Using Neural Networks

We present a new machine-learning model for estimating photometric redshifts with improved accuracy for galaxies in Pan-STARRS1 data release 1. Depending on the estimation range of redshifts, this model based on neural networks can handle the difficulty for inferring photometric redshifts. Moreover, to reduce bias induced by the new model's ability to deal with estimation difficulty, it exploits the power of ensemble learning. We extensively examine the mapping between input features and target redshift spaces to which the model is validly applicable to discover the strength and weaknesses of the trained model. Because our trained model is well calibrated, our model produces reliable confidence information about objects with non-catastrophic estimation. While our model is highly accurate for most test examples residing in the input space, where training samples are densely populated, its accuracy quickly diminishes for sparse samples and unobserved objects (i.e., unseen samples) in training. We report that out-of-distribution (OOD) samples for our model contain both physically OOD objects (i.e., stars and quasars) and galaxies with observed properties not represented by training data. The code for our model is available at https://github.com/GooLee0123/MBRNN for other uses of the model and retraining the model with different data.

Using a Neural Network Classifier to Select Galaxies with the Most Accurate Photometric Redshifts

The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will produce several billion photometric redshifts (photo-z's), enabling cosmological analyses to select a subset of galaxies with the most accurate photo-z. We perform initial redshift fits on Subaru Strategic Program galaxies with deep grizy photometry using Trees for Photo-Z (TPZ) before applying a custom neural network classifier (NNC) tuned to select galaxies with (z phot − z spec)/(1 + z spec) < 0.10. We consider four cases of training and test sets ranging from an idealized case to using data augmentation to increase the representation of dim galaxies in the training set. Selections made using the NNC yield significant further improvements in outlier fraction and photo-z scatter (σ z ) over those made with typical photo-z uncertainties. As an example, when selecting the best third of the galaxy sample, the NNC achieves a 35% improvement in outlier rate and a 23% improvement in σ z compared to using uncertainties from TPZ. For cosmology and galaxy evolution studies, this method can be tuned to retain a particular sample size or to achieve a desired photo-z accuracy; our results show that it is possible to retain more than a third of an LSST-like galaxy sample while reducing σ z by a factor of 2 compared to the full sample, with one-fifth as many photo-z outliers. For surveys like LSST that are not limited by shot noise, this method enables a larger number of tomographic redshift bins and hence a significant increase in the total signal to noise of galaxy angular power spectra.

Discovery of 178 Giant Radio Galaxies in 1059 deg2 of the Rapid ASKAP Continuum Survey at 888 MHz

We report the results of a visual inspection of images of the Rapid ASKAP Continuum Survey (RACS) in search of extended radio galaxies (ERG) that reach or exceed linear sizes on the order of one Megaparsec. We searched a contiguous area of 1059 deg2 from RAJ = 20h20m to 06h20m, and −50∘<DecJ<−40∘, which is covered by deep multi-band optical images of the Dark Energy Survey (DES) and in which previously only three ERGs larger than 1 Mpc had been reported. For over 1800 radio galaxy candidates inspected, our search in optical and infrared images resulted in hosts for 1440 ERG, for which spectroscopic and photometric redshifts from various references were used to convert their largest angular size (LAS) to projected linear size (LLS). This resulted in 178 newly discovered giant radio sources (GRS) with LLS >1 Mpc, of which 18 exceed 2 Mpc and the largest one is 3.4 Mpc. Their redshifts range from 0.02 to ∼2.0, but only 10 of the 178 new GRS have spectroscopic redshifts. For the 146 host galaxies, the median r-band magnitude and redshift are 20.9 and 0.64, while for the 32 quasars or candidates these are 19.7 and 0.75. Merging the six most recent large compilations of GRS results in 458 GRS larger than 1 Mpc, so we were able to increase this number by ∼39% to 636.

Modelling type 1 quasar colours in the era of Rubin and Euclid

ABSTRACT We construct a parametric SED model which is able to reproduce the average observed SDSS–UKIDSS–WISE quasar colours to within one-tenth of a magnitude across a wide range of redshift (0 &lt; z &lt; 5) and luminosity (−22 &gt; Mi &gt; −29). This model is shown to provide accurate predictions for the colours of known quasars which are less luminous than those used to calibrate the model parameters, and also those at higher redshifts z &gt; 5. Using a single parameter, the model encapsulates an up-to-date understanding of the intra-population variance in the rest-frame ultraviolet and optical emission lines of luminous quasars. At fixed redshift, there are systematic changes in the average quasar colours with apparent i-band magnitude, which we find to be well explained by the contribution from the host galaxy and our parametrization of the emission-line properties. By including redshift as an additional free parameter, the model could be used to provide photometric redshifts for individual objects. For the population as a whole we find that the average emission line and host-galaxy contributions can be well described by simple functions of luminosity which account for the observed changes in the average quasar colours across 18.1 &lt; iAB &lt; 21.5. We use these trends to provide predictions for quasar colours at the luminosities and redshifts which will be probed by the Rubin Observatory LSST and ESA-Euclid wide survey. The model code is applicable to a wide range of upcoming photometric and spectroscopic surveys, and is made publicly available.

LSSTC AGN Data Challenge 2021

We provide a code and data repository that can be used to facilitate planning for AGN science with the upcoming Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST). For this purpose, we have produced a common exploratory dataset that can be used to develop tools for parameterization of AGN light curves, AGN selection, and AGN photometric redshifts

Photometric Redshifts With Machine Learning, Lights and Shadows on a Complex Data Science Use Case

The importance of the current role of data-driven science is constantly increasing within Astrophysics, due to the huge amount of multi-wavelength data collected every day, characterized by complex and high-volume information requiring efficient and, as much as possible, automated exploration tools. Furthermore, to accomplish main and legacy science objectives of future or incoming large and deep survey projects, such as James Webb Space Telescope (JWST), James Webb Space Telescope (LSST), and Euclid, a crucial role is played by an accurate estimation of photometric redshifts, whose knowledge would permit the detection and analysis of extended and peculiar sources by disentangling low-z from high-z sources and would contribute to solve the modern cosmological discrepancies. The recent photometric redshift data challenges, organized within several survey projects, like LSST and Euclid, pushed the exploitation of the observed multi-wavelength and multi-dimensional data or ad hoc simulated data to improve and optimize the photometric redshifts prediction and statistical characterization based on both Spectral Energy Distribution (SED) template fitting and machine learning methodologies. They also provided a new impetus in the investigation of hybrid and deep learning techniques, aimed at conjugating the positive peculiarities of different methodologies, thus optimizing the estimation accuracy and maximizing the photometric range coverage, which are particularly important in the high-z regime, where the spectroscopic ground truth is poorly available. In such a context, we summarize what was learned and proposed in more than a decade of research.