J-NEP: 60-band photometry and photometric redshifts for the James Webb Space Telescope North Ecliptic Pole Time-Domain Field

Abstract

The Javalambre-Physics of the Accelerating Universe Astrophysical Survey (J-PAS) will observe approximately one-third of the northern sky with a set of 56 narrow-band filters using the dedicated 2.55 m Javalambre Survey Telescope (JST) at the Javalambre Astrophysical Observatory. Prior to the installation of the main camera, in order to demonstrate the scientific potential of J-PAS, two small surveys were performed with the single-CCD Pathfinder camera: miniJPAS (~1 deg2 along the Extended Groth Strip), and J-NEP (~0.3 deg2 around the JWST North Ecliptic Pole Time Domain Field), including all 56 J-PAS filters as well as u, g, r, and i. J-NEP is ~0.5–1.0 mag deeper than miniJPAS, providing photometry for 24,618 r-band-detected sources and photometric redshifts (photo-z) for the 6662 sources with r < 23. In this paper, we describe the photometry and photo-z of J-NEP and demonstrate a new method for the removal of systematic offsets in the photometry based on the median colours of galaxies, which we call ‘galaxy locus recalibration’. This method does not require spectroscopic observations except in a few reference pointings and, unlike previous methods, is directly applicable to the whole J-PAS survey. We use a spectroscopic sample of 787 galaxies to test the photo-z performance for J-NEP and in comparison to miniJPAS. We find that the deeper J-NEP observations result in a factor ~1.5–2 decrease in σNMAD (a robust estimate of the standard deviation of the photo-z error) and η (the outlier rate) relative to miniJPAS for r > 21.5 sources, but no improvement in brighter ones, which is probably because of systematic uncertainties. We find the same relation between σNMAD and odds in J-NEP and miniJPAS, which suggests that we will be able to predict the σNMAD of any set of J-PAS sources from their odds distribution alone, with no need for additional spectroscopy to calibrate the relation. We explore the causes of photo-z outliers and find that colour-space degeneracy at low S/N, photometry artefacts, source blending, and exotic spectra are the most important factors.

The miniJPAS survey: Maximising the photo-z accuracy from multi-survey datasets with probability conflation

“…An important step in determining accurate photo-z over a large area will be taken by the Javalambre-Physics of the Accelerating Universe Astrophysical Survey (J-PAS; Benítez et al 2009Benítez et al , 2014, which will cover one third of the northern hemisphere with a unique set of 56 optical filters (plus i band for detection) and provides, for each 0.48 ′′ × 0.48 ′′ pixel in the sky, an R ∼ 60 photo-spectrum (J-spectrum) covering the 3800-9100 Å range. Two small surveys were carried out to demonstrate the scientific potential of J-PAS: miniJPAS (Bonoli et al 2021) and J-NEP (Hernán-Caballero et al 2023), showing that σ(∆z)=0.3% is attainable with the 56 bands of J-PAS plus u, g, r, and i (Hernán- Caballero et al 2021Caballero et al , 2023Laur et al 2022).…”

PEARLS: Near-infrared Photometry in the JWST North Ecliptic Pole Time Domain Field*

“…This prompted a multiobservatory effort to collect visible and NIR data for the calibrations and to provide the first epoch of observations to identify bright transients and variable sources. These visible and NIR data also provide counterparts for observations obtained at other wavelengths, both from space, e.g., Chandra (PI: W. Maksym), NuSTAR (PI: F. Civano; Zhao et al 2021), XMM (PI: F. Civano), eROSITA (PI: A. Merloni & R. Sunyaev), AstroSat (PI: K. Saha), and Hubble Space Telescope (HST; PIs: R. Jansen & N. Grogin), and from the ground, e.g., Large Binocular Telescope/Large Binocular Camera (PI: R. Jansen), J-NEP (PI: S. Bonoli & R. Dupke;Hernán-Caballero et al 2023), HiPeRCAM/Gran Telescopio de Canarias (PI: V. Dhillon), Hyper-Suprime-Cam/Subaru (HEROES, PI: G. Hasinger & E. Hu; Songaila et al 2018;Taylor et al 2023), NOEMA (PI: S. Cohen), SCUBA2 (PIs: M. Im & I. Hyun et al 2023), Submillimeter Array (PI: G. Fazio), Very Large Array (VLA, PI: R. Hyun et al 2023;Willner et al 2023), and LOFAR (PI: R. van Weeren).…”