DOI: 10.5281/zenodo.18380587
title: DeepDISC photo-z catalog for JADES DR2 GOODS-S

description: README 

This file contains a photometric redshift catalog for all objects in the JADES DR2 GOODS-S sample.  Photo-zs were derived using deep learning framework DeepDISC.  

 

Associated paper here.

 

Abstract: Photo-z algorithms that utilize SED template fitting have matured, and are widely adopted for use on high-redshift near-infrared data that provides a unique window into the early universe. Alternative photo-z methods have been developed, largely within the context of low-redshift optical surveys. Machine learning based approaches have gained footing in this regime, including those that utilize raw pixel information instead of aperture photometry. However, the efficacy of image-based algorithms on high-redshift, near-infrared data remains underexplored. Here, we test the performance of Detection, Instance Segmentation and Classification with Deep Learning (DeepDISC) on photometric redshift estimation with NIRCam images from the JWST Advanced Deep Extragalactic Survey (JADES) program. DeepDISC is designed to produce probabilistic photometric redshift estimates directly from images, after detecting and deblending sources in a scene. Using NIRCam-only images and a compiled catalog of spectroscopic redshifts, we show that DeepDISC produces reliable photo-zs and uncertainties comparable to those estimated from template fitting using HST+JWST filters; DeepDISC even outperforms template fitting (lower scatter/fewer outliers) when the input photometric filters are matched. Compared with template fitting, DeepDISC does not require measured photometry from images, and can produce a catalog of 94000 photo-zs in ~4 minutes on a single NVIDIA A40 GPU. While current spectroscopic training samples are small and incomplete in color-magnitude space, this work demonstrates the potential of DeepDISC for increasingly larger image volumes and spectroscopic samples from ongoing and future programs. We discuss the impact of the training data on applications to broader samples and produce a catalog of photo-zs for all JADES DR2 photometric sources in the GOOD-S field, with quality flags indicating caveats. 

 

Catalog description

Columns



ID: JADES DR2 ID matching that from Eisenstein et al 2023

z_phot_mode: mode point estimate from the photo-z PDF

l68: lower bound of the 68th percentile confidence interval from the photo-z PDF

u68: upper bound of the 68th percentile confidence interval from the photo-z PDF

l95: lower bound of the 95th percentile confidence interval from the photo-z PDF

u95: upper bound of the 95th percentile confidence interval from the photo-z PDF

l99: lower bound of the 99th percentile confidence interval from the photo-z PDF

u99: upper bound of the 99th percentile confidence interval from the photo-z PDF

forced: whether or not forced photometry mode was used, i.e, DeepDISC did not initially detect the object

spec-rep: The number of training sample spectroscopic objects in the corresponding SOM cell based on the object's magnitude/colors.  See paper for details.  Higher = better and more trustworthy photo-z


SOM

The SOM is used to identify regions of color-space that are underrepresented by the training sample.  It can be loaded with 

with open('som_trained.p' ,'rb') as f:
    som = pickle.load(f)