A feedback-loop approach for galaxy physical properties estimation

Ongoing and forthcoming surveys promise great advances in our understanding of the Universe content and history, thanks to unprecedented improvements in the size and precision of observation datasets.On a cosmological scale, galaxies characteristics may be summarised by three main features, namely their redshift, stellar content mass and star formation rate, evolving throughout their lifetime.They are usually estimated from a set of photometric measurements, mapping their spectral emission.In this context, we propose a machine learning approach where we first evaluate redshift from the photometric data, and then merge it with them through a feedback loop, for subsequent estimation of the three desired parameters.In spite of its simplicity, our approach matches the performance of, and in some cases outperforms, significantly more complex previous tools exploiting also images.It achieves correct estimates on the near totality of instances for redshift and stellar mass, decreasing to about 70% on the more difficult case of SFR estimation.