Simultaneous Estimation of Photometric Redshifts and SED Parameters: Improved Techniques and a Realistic Error Budget

Abstract

We seek to improve the accuracy of joint galaxy photometric redshift estimation and spectral energy distribution (SED) fitting. By simulating different sources of uncorrected systematic errors, we demonstrate that if the uncertainties on the photometric redshifts are estimated correctly, so are those on the other SED fitting parameters, such as stellar mass, stellar age, and dust reddening. Furthermore, we find that if the redshift uncertainties are over(under)-estimated, the uncertainties in SED parameters tend to be over(under)-estimated by similar amounts. These results hold even in the presence of severe systematics and provide, for the first time, a mechanism to validate the uncertainties on these parameters via comparison with spectroscopic redshifts. We propose a new technique (annealing) to re-calibrate the joint uncertainties in the photo-z and SED fitting parameters without compromising the performance of the SED fitting + photo-z estimation. This procedure provides a consistent estimation of the multidimensional probability distribution function in SED fitting + z parameter space, including all correlations. While the performance of joint SED fitting and photo-z estimation might be hindered by template incompleteness, we demonstrate that the latter is "flagged" by a large fraction of outliers in redshift, and that significant improvements can be achieved by using flexible stellar populations synthesis models and more realistic star formation histories. In all cases, we find that the median stellar age is better recovered than the time elapsed from the onset of star formation [abridged].

11 pages, 5 figures, 3 tables. Accepted for publication in the Astrophysical Journal

References in corpus (4)

• EAZY: A Fast, Public Photometric Redshift Code
• COSMOS Photometric Redshifts with 30-bands for 2-deg2
• An ultra-deep near-infrared spectrum of a compact quiescent galaxy at z=2.2
• Biases and Uncertainties in Physical Parameter Estimates of Lyman Break Galaxies from Broad-band Photometry

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• Value Added Catalog of physical properties of more than 1.3 million galaxies from the DESI Survey
• Hierarchical Bayesian inference of photometric redshifts with stellar population synthesis models
• A re-assessment of strong line metallicity conversions in the machine learning era
• Leveraging Machine Learning for Accurate and Fast Stellar Mass Estimation of Galaxies
• Toward a stellar population catalog in the Kilo Degree Survey: the impact of stellar recipes on stellar masses and star formation rates
• Detecting episodes of star formation using Bayesian model selection
• Machine Learning vs. Spectral Energy Distribution Fitting: A Comparative Analysis of Accuracy in Stellar Mass Estimation
• Beyond mirkwood: Enhancing SED Modeling with Conformal Predictions
• The Photometric Analysis of the Environment Around Two Dusty Star-Forming Galaxies at $z\sim 2$