EAZY

EAZY
Name	EAZY
Developer	Max Planck Institute for Astronomy; University of California, Berkeley collaborators
Released	2008
Latest release	2013
Programming language	IDL (programming language), Python (programming language)
Platform	Cross-platform
License	Open-source

EAZY

EAZY is a photometric redshift code used in observational cosmology and extragalactic astronomy. Designed to estimate redshifts for faint galaxies in deep surveys, it integrates template fitting, Bayesian priors, and template error functions to improve robustness across datasets from instruments such as the Hubble Space Telescope, Spitzer Space Telescope, Subaru Telescope, Very Large Telescope, and surveys like the COSMOS (astronomical survey), Sloan Digital Sky Survey, and CANDELS. The software has been cited in works by teams at the University of California, Berkeley, the Max Planck Institute for Astronomy, and groups participating in the Great Observatories Origins Deep Survey.

Overview

EAZY addresses the problem of estimating photometric redshifts when spectroscopic measurements from facilities such as the Keck Observatory, Gemini Observatory, or the European Southern Observatory are unavailable or incomplete. It builds on prior efforts exemplified by codes used in the Hubble Deep Field analyses and methodologies explored in the COSMOS collaboration. The approach emphasizes flexibility for datasets from instruments including the Subaru Telescope's Suprime-Cam, the Canada–France–Hawaii Telescope MegaCam, and the Very Large Array ancillary photometry, enabling work by research groups at the Space Telescope Science Institute, Max Planck Society, and various university departments.

Development and Versions

Initial development began in the late 2000s with contributions from researchers affiliated with the Max Planck Institute for Astronomy and collaborators at the University of California, Berkeley. Subsequent versions incorporated lessons from comparisons with alternative codes used by teams at the Leiden Observatory, University of Edinburgh, and the University of Oxford. Releases included updates to template libraries informed by spectral atlases from the Sloan Digital Sky Survey and population synthesis models originating from groups like those behind the Bruzual & Charlot models and the Maraston models. Community forks and implementations in languages such as Python (programming language) increased compatibility with analysis pipelines used by consortia including CANDELS, 3D-HST, and the Dark Energy Survey teams.

Methodology and Features

EAZY's core methodology combines template fitting with a template error function and a prior probability distribution to mitigate color–redshift degeneracies common in deep-field observations like the Hubble Ultra Deep Field. The template set often references empirical spectra compiled from the Sloan Digital Sky Survey and synthetic templates based on Bruzual & Charlot population synthesis. Key features adopted by research groups include a flexible linear combination of templates, a user-definable prior inspired by luminosity functions measured by the VIMOS VLT Deep Survey, and a rest-frame template error array calibrated against spectroscopic samples from the DEEP2 Redshift Survey and the zCOSMOS project. The code supports bandpasses from filter systems used by Hubble Space Telescope instruments (e.g., ACS, WFC3), ground-based observatories like the Subaru Telescope, and infrared arrays on the Spitzer Space Telescope.

Applications and Use Cases

EAZY has been applied in studies of galaxy evolution, large-scale structure, and galaxy cluster identification by teams referencing surveys such as COSMOS, UltraVISTA, CANDELS, and 3D-HST. Research groups have used it to select high-redshift candidates for follow-up with instruments at the Keck Observatory, Very Large Telescope, and the Atacama Large Millimeter/submillimeter Array; to map stellar mass functions leveraging ancillary data from Spitzer Space Telescope photometry; and to assemble photometric catalogs feeding pipelines at institutions like the Space Telescope Science Institute and the National Optical Astronomy Observatory. EAZY's probabilistic redshift distributions have supported weak-lensing analyses conducted by collaborations including the CFHTLenS team and aided in photometric cluster searches pursued by projects associated with the South Pole Telescope.

Performance and Validation

Validation of EAZY has relied on comparisons with spectroscopic redshifts obtained from campaigns at the Keck Observatory, VLT, and the Subaru Telescope as part of surveys like DEEP2, zCOSMOS, and VVDS. Performance metrics reported by groups show reduced outlier fractions and improved bias relative to simple single-template fits, particularly when using empirically calibrated priors drawn from catalogs such as COSMOS and SDSS. Studies by teams at the Max Planck Institute for Astronomy and collaborators have quantified scatter and catastrophic failure rates across magnitude ranges probed by Hubble Space Telescope deep surveys and ground-based wide fields like SDSS. Cross-comparisons with alternative photometric redshift codes used by the DES and KiDS collaborations highlight strengths in template combination flexibility and sensitivity to medium-band photometry employed by observatories such as Subaru and LBT.

Adoption and Community Support

EAZY is widely adopted by research groups at institutions including the Max Planck Society, University of California, Berkeley, Space Telescope Science Institute, and numerous university astronomy departments. Its open-source distribution encouraged integration into analysis workflows of large collaborations such as CANDELS, COSMOS, and 3D-HST. Community support has been fostered through workshops at conferences organized by entities like the American Astronomical Society, training sessions at facilities such as the Space Telescope Science Institute, and code comparisons coordinated by the Euclid Consortium and the LSST Science Collaboration. Continued use in forthcoming projects linked to the James Webb Space Telescope, Euclid (spacecraft), and the Vera C. Rubin Observatory underscores its role in modern photometric redshift estimation.

Category:Astrophysics software