New insights on the nebular emission, ionizing radiation, and low metallicity of Green Peas from advanced modelling

Low-metallicity, compact starburst galaxies referred to as Green Peas (GPs) provide a unique window to study galactic evolution across cosmic epochs. In this work, we present new deep optical spectra for three GPs from OSIRIS at the 10-m Gran Telescopio Canarias, which are studied using a state-of-the-art methodology. A stellar population synthesis is conducted with 1098 spectral templates. The methodology succeeds at characterizing stellar populations from 0.5 Myr to 10 Gyr. The light distribution shows a large red excess from a single population with log (age) > 8.5 yr in the GP sample analysed. This points towards an incomplete characterization of the gas luminosity, whose continuum already accounts between 7.4 and 27.6 per cent in the galaxy sample. The emission spectra are fitted with the largest Bayesian chemical model consisting of an electron temperature, an electron density, the logarithmic extinction coefficient and eleven ionic species under the direct method paradigm. Additionally, building on previous work, we propose a neural networks sampler to constrain the effective temperature and ionization parameter of each source from photoionization model grids. Finally, we combine both methodologies into a 16-dimensional model, which for the first time, simultaneously explores the direct method and photoionization parameter spaces. Both techniques consistently indicate a low-metallicity gas, 7.76 < 12 + log (O/H) < 8.04, ionized by strong radiation fields, in agreement with previous works.

Automated summary

This work uses deep optical spectra and advanced methods to study the stellar populations and gas properties of three Green Pea galaxies. The study finds a red excess in the light distribution of the stellar populations, indicating an incomplete characterization of the gas luminosity. By fitting emission spectra, it is determined that the gas has a low-metallicity and is ionized by strong radiation fields. A neural networks sampler is also proposed to constrain the parameters of photoionization models.