The first analytical expression to estimate photometric redshifts suggested by a machine

We report the first analytical expression purely constructed by a machine to determine photometric redshifts ($z_{\rm phot}$) of galaxies. A simple and reliable functional form is derived using $41,214$ galaxies from the Sloan Digital Sky Survey Data Release 10 (SDSS-DR10) spectroscopic sample. The method automatically dropped the $u$ and $z$ bands, relying only on $g$, $r$ and $i$ for the final solution. Applying this expression to other $1,417,181$ SDSS-DR10 galaxies, with measured spectroscopic redshifts ($z_{\rm spec}$), we achieved a mean $\langle (z_{\rm phot} - z_{\rm spec})/(1+z_{\rm spec})\rangle\lesssim 0.0086$ and a scatter $\sigma_{(z_{\rm phot} - z_{\rm spec})/(1+z_{\rm spec})}\lesssim 0.045$ when averaged up to $z \lesssim 1.0$. The method was also applied to the PHAT0 dataset, confirming the competitiveness of our results when faced with other methods from the literature. This is the first use of symbolic regression in cosmology, representing a leap forward in astronomy-data-mining connection.Comment: 6 pages, 4 figures. Accepted for publication in MNRAS Letter

Torsion-Adding and Asymptotic Winding Number for Periodic Window Sequences

In parameter space of nonlinear dynamical systems, windows of periodic states are aligned following routes of period-adding configuring periodic window sequences. In state space of driven nonlinear oscillators, we determine the torsion associated with the periodic states and identify regions of uniform torsion in the window sequences. Moreover, we find that the measured of torsion differs by a constant between successive windows in periodic window sequences. We call this phenomenon as torsion-adding. Finally, combining the torsion and the period adding rules, we deduce a general rule to obtain the asymptotic winding number in the accumulation limit of such periodic window sequences

Using gamma regression for photometric redshifts of survey galaxies

Machine learning techniques offer a plethora of opportunities in tackling big data within the astronomical community. We present the set of Generalized Linear Models as a fast alternative for determining photometric redshifts of galaxies, a set of tools not commonly applied within astronomy, despite being widely used in other professions. With this technique, we achieve catastrophic outlier rates of the order of ~1%, that can be achieved in a matter of seconds on large datasets of size ~1,000,000. To make these techniques easily accessible to the astronomical community, we developed a set of libraries and tools that are publicly available.Comment: Refereed Proceeding of "The Universe of Digital Sky Surveys" conference held at the INAF - Observatory of Capodimonte, Naples, on 25th-28th November 2014, to be published in the Astrophysics and Space Science Proceedings, edited by Longo, Napolitano, Marconi, Paolillo, Iodice, 6 pages, and 1 figur