A Ks-band-selected catalogue of objects in the ALHAMBRA survey

The original ALHAMBRA catalogue contained over 400,000 galaxies selected using a synthetic F814W image, to the magnitude limit AB(F814W)$\approx$24.5. Given the photometric redshift depth of the ALHAMBRA multiband data (=0.86) and the approximately $I$-band selection, there is a noticeable bias against red objects at moderate redshift. We avoid this bias by creating a new catalogue selected in the $K_s$ band. This newly obtained catalogue is certainly shallower in terms of apparent magnitude, but deeper in terms of redshift, with a significant population of red objects at $z>1$. We select objects using the $K_s$ band images, which reach an approximate AB magnitude limit $K_s \approx 22$. We generate masks and derive completeness functions to characterize the sample. We have tested the quality of the photometry and photometric redshifts using both internal and external checks. Our final catalogue includes $\approx 95,000$ sources down to $K_s \approx 22$, with a significant tail towards high redshift. We have checked that there is a large sample of objects with spectral energy distributions that correspond to that of massive, passively evolving galaxies at $z > 1$, reaching as far as $z \approx 2.5$. We have tested the possibility of combining our data with deep infrared observations at longer wavelengths, particularly Spitzer IRAC data

The Physics of the Accelerating Universe Survey: narrow-band image photometry

PAUCam is an innovative optical narrow-band imager mounted at the William Herschel Telescope built for the Physics of the Accelerating Universe Survey (PAUS). Its set of 40 filters results in images that are complex to calibrate, with specific instrumental signatures that cannot be processed with traditional data reduction techniques. In this paper, we present two pipelines developed by the PAUS data management team with the objective of producing science-ready catalogues from the uncalibrated raw images. The NIGHTLY pipeline takes care of entire image processing, with bespoke algorithms for photometric calibration and scatter-light correction. The Multi-Epoch and Multi-Band Analysis pipeline performs forced photometry over a reference catalogue to optimize the photometric redshift (photo-z) performance. We verify against spectroscopic observations that the current approach delivers an inter-band photometric calibration of 0.8 per cent across the 40 narrow-band set. The large volume of data produced every night and the rapid survey strategy feedback constraints require operating both pipelines in the Port d’Informació Cientifica data centre with intense parallelization. While alternative algorithms for further improvements in photo-z performance are under investigation, the image calibration and photometry presented in this work already enable state-of-the-art photo-z down to iAB = 23.0

The ROSAT International X-ray/Optical Survey (RIXOS): source catalogue

We describe the ROSAT International X-ray/Optical Survey (RIXOS), a medium-sensitivity survey and optical identification of X-ray sources discovered in ROSAT high Galactic latitude fields (|b|>28°) and observed with the Position Sensitive Proportional Counter (PSPC) detector. The survey made use of the central 17 arcmin of each ROSAT field. A flux limit of 3×10−14 erg cm−2 s−1 (0.5–2 keV) was adopted for the survey, and a minimum exposure time of 8000 s was required for qualifying ROSAT observations. X-ray sources in the survey are therefore substantially above the detection threshold of each field used, and many contain enough counts to allow the X-ray spectral slope to be estimated. Spectroscopic observations of potential counterparts were obtained of all sources down to the survey limit in 64 fields, totalling a sky area of 15.77 deg2. Positive optical identifications are made for 94 per cent of the 296 sources thus examined. A further 18 fields (4.44 deg2), containing 105 sources above the 3×10−14 erg cm−2 s−1 survey limit, are completely optically identified to a higher flux of 8×10−14 erg cm−2 s−1 (0.5–2 keV). Optical spectroscopic data are supplemented by deep CCD imaging of many sources to reveal the morphology of the optical counterparts, and objects too faint to register on Sky Survey plates. The faintest optical counterparts have R∼22. This paper describes the survey method, and presents a catalogue of the RIXOS sources and their optical identifications. Finding charts based on Sky Survey data are given for each source, supplemented by CCD imaging where necessary

Cosmic CARNage I: on the calibration of galaxy formation models

We present a comparison of nine galaxy formation models, eight semi-analytical, and one halo occupation distribution model, run on the same underlying cold dark matter simulation (cosmological box of comoving width 125h−1 Mpc, with a dark-matter particle mass of 1.24 × 109h−1M⊙) and the same merger trees. While their free parameters have been calibrated to the same observational data sets using two approaches, they nevertheless retain some ‘memory’ of any previous calibration that served as the starting point (especially for the manually tuned models). For the first calibration, models reproduce the observed z = 0 galaxy stellar mass function (SMF) within 3σ. The second calibration extended the observational data to include the z = 2 SMF alongside the z ∼ 0 star formation rate function, cold gas mass, and the black hole–bulge mass relation. Encapsulating the observed evolution of the SMF from z = 2 to 0 is found to be very hard within the context of the physics currently included in the models. We finally use our calibrated models to study the evolution of the stellar-to-halo mass (SHM) ratio. For all models, we find that the peak value of the SHM relation decreases with redshift. However, the trends seen for the evolution of the peak position as well as the mean scatter in the SHM relation are rather weak and strongly model dependent. Both the calibration data sets and model results are publicly available

nIFTy Cosmology: Comparison of Galaxy Formation Models

We present a comparison of 14 galaxy formation models: 12 different semi-analytical models and 2 halo-occupation distribution models for galaxy formation based upon the same cosmological simulation and merger tree information derived from it. The participating codes have proven to be very successful in their own right but they have all been calibrated independently using various observational data sets, stellar models, and merger trees. In this paper we apply them without recalibration and this leads to a wide variety of predictions for the stellar mass function, specific star formation rates, stellar-to- halo mass ratios, and the abundance of orphan galaxies. The scatter is much larger than seen in previous comparison studies primarily because the codes have been used outside of their native environment within which they are well tested and calibrated. The purpose of the `nIFTy comparison of galaxy formation models' is to bring together as many different galaxy formation modellers as possible and to investigate a common approach to model calibration. This paper provides a unified description for all participating models and presents the initial, uncalibrated comparison as a baseline for our future studies where we will develop a common calibration framework and address the extent to which that reduces the scatter in the model predictions seen here

The Dark Energy Spectroscopic Instrument (DESI)

We present the status of the Dark Energy Spectroscopic Instrument (DESI) and its plans and opportunities for the coming decade. DESI construction and its initial five years of operations are an approved experiment of the US Department of Energy and is summarized here as context for the Astro2020 panel. Beyond 2025, DESI will require new funding to continue operations. We expect that DESI will remain one of the world's best facilities for wide-field spectroscopy throughout the decade. More about the DESI instrument and survey can be found at https://www.desi.lbl.gov

HOST GALAXY IDENTIFICATION FOR SUPERNOVA SURVEYS

Host galaxy identification is a crucial step for modern supernova (SN) surveys such as the Dark Energy Survey and the Large Synoptic Survey Telescope, which will discover SNe by the thousands. Spectroscopic resources are limited, and so in the absence of real-time SN spectra these surveys must rely on host galaxy spectra to obtain accurate redshifts for the Hubble diagram and to improve photometric classification of SNe. In addition, SN luminosities are known to correlate with host-galaxy properties. Therefore, reliable identification of host galaxies is essential for cosmology and SN science. We simulate SN events and their locations within their host galaxies to develop and test methods for matching SNe to their hosts. We use both real and simulated galaxy catalog data from the Advanced Camera for Surveys General Catalog and MICECATv2.0, respectively. We also incorporate "hostless" SNe residing in undetected faint hosts into our analysis, with an assumed hostless rate of 5%. Our fully automated algorithm is run on catalog data and matches SNe to their hosts with 91% accuracy. We find that including a machine learning component, run after the initial matching algorithm, improves the accuracy (purity) of the matching to 97% with a 2% cost in efficiency (true positive rate). Although the exact results are dependent on the details of the survey and the galaxy catalogs used, the method of identifying host galaxies we outline here can be applied to any transient survey

The PSZ-MCMF catalogue of Planck clusters over the des region

We present the first systematic follow-up of Planck Sunyaev–Zeldovich effect (SZE) selected candidates down to signal-to-noise (S/N) of 3 over the 5000 deg2 covered by the Dark Energy Survey. Using the MCMF cluster confirmation algorithm, we identify optical counterparts, determine photometric redshifts, and richnesses and assign a parameter, fcont, that reflects the probability that each SZE-optical pairing represents a random superposition of physically unassociated systems rather than a real cluster. The new PSZ-MCMF cluster catalogue consists of 853 MCMF confirmed clusters and has a purity of 90 per cent. We present the properties of subsamples of the PSZ-MCMF catalogue that have purities ranging from 90 per cent to 97.5 per cent, depending on the adopted fcont threshold. Halo mass estimates M500, redshifts, richnesses, and optical centres are presented for all PSZ-MCMF clusters. The PSZ-MCMF catalogue adds 589 previously unknown Planck identified clusters over the DES footprint and provides redshifts for an additional 50 previously published Planck-selected clusters with S/N>4.5. Using the subsample with spectroscopic redshifts, we demonstrate excellent cluster photo-z performance with an RMS scatter in Δz/(1 + z) of 0.47 per cent. Our MCMF based analysis allows us to infer the contamination fraction of the initial S/N>3 Planck-selected candidate list, which is ∼50 per cent. We present a method of estimating the completeness of the PSZ-MCMF cluster sample. In comparison to the previously published Planck cluster catalogues, this new S/N>3 MCMF confirmed cluster catalogue populates the lower mass regime at all redshifts and includes clusters up to z∼1.3

DeepZipper. II. Searching for Lensed Supernovae in Dark Energy Survey Data with Deep Learning

Gravitationally lensed supernovae (LSNe) are important probes of cosmic expansion, but they remain rare and difficult to find. Current cosmic surveys likely contain 5-10 LSNe in total while next-generation experiments are expected to contain several hundred to a few thousand of these systems. We search for these systems in observed Dark Energy Survey (DES) five year SN fields—10 3 sq. deg. regions of sky imaged in the griz bands approximately every six nights over five years. To perform the search, we utilize the DeepZipper approach: a multi-branch deep learning architecture trained on image-level simulations of LSNe that simultaneously learns spatial and temporal relationships from time series of images. We find that our method obtains an LSN recall of 61.13% and a false-positive rate of 0.02% on the DES SN field data. DeepZipper selected 2245 candidates from a magnitude-limited (m i < 22.5) catalog of 3,459,186 systems. We employ human visual inspection to review systems selected by the network and find three candidate LSNe in the DES SN fields