Improving the Estimation of Star formation Rates and Stellar Population Ages of High-redshift Galaxies from Broadband Photometry

TL;DR

This paper demonstrates that using more realistic, gradually rising star formation histories improves the accuracy of estimating star formation rates and stellar ages of high-redshift galaxies from broadband photometry, compared to traditional models.

Contribution

It introduces and validates a new parametric star formation history model with a gradual rise, leading to better estimates of galaxy properties from photometric data.

Findings

Gradual rise models outperform exponential decline models in estimating galaxy ages.

Linearly increasing star formation rate models reduce age overestimation to 9-16%.

Peak star formation models improve age and rate estimates for U-dropouts.

Abstract

We explore methods to improve the estimates of star formation rates and mean stellar population ages from broadband photometry of high redshift star-forming galaxies. We use synthetic spectral templates with a variety of simple parametric star formation histories to fit broadband spectral energy distributions. These parametric models are used to infer ages, star formation rates and stellar masses for a mock data set drawn from a hierarchical semi-analytic model of galaxy evolution. Traditional parametric models generally assume an exponentially declining rate of star-formation after an initial instantaneous rise. Our results show that star formation histories with a much more gradual rise in the star formation rate are likely to be better templates, and are likely to give better overall estimates of the age distribution and star formation rate distribution of Lyman break galaxies. For B- and V-dropouts, we find the best simple parametric model to be one where the star formation rate increases linearly with time. The exponentially-declining model overpredicts the age by 100 % and 120 % for B- and V-dropouts, on average, while for a linearly-increasing model, the age is overpredicted by 9 % and 16 %, respectively. Similarly, the exponential model underpredicts star-formation rates by 56 % and 60 %, while the linearly-increasing model underpredicts by 15 % 22 %, respectively. For U-dropouts, the models where the star-formation rate has a peak (near z ~ 3) provide the best match for age -- overprediction is reduced from 110 % to 26 % -- and star-formation rate -- underprediction is reduced from 58 % to 22 %. We classify different types of star-formation histories in the semi-analytic models and show how the biases behave for the different classes. We also provide two-band calibration formulae for stellar mass and star formation rate estimations.