Gaussian Processes and Neural Networks applied to photometric redshift reconstruction
Looking at higher redshifts is equivalent to looking back in time: they improve the studies of cosmology, expanding our knowledge of the universe. It allows us to study various physical phenomena like the power spectrum of galaxies which describes the distribution of galaxies on a range of scales, galaxy clustering, and large scales, the detection of the Baryon Acoustic Oscillation feature. As a result, a significant amount of work has been done to increase the efficiency and accuracy of the process via new algorithms and optimization of existing ones. Astronomical datasets are undergoing a rapid growth in size and complexity as past, ongoing and future surveys produce massive multi-temporal and multi-wavelength datasets, with huge information to be extracted and analyzed. The alternative to a full spectroscopic survey is to obtain multi-color images of the sky and perform photometric redshift estimates for the galaxies we have available. When dealing with this problem, there are two main approaches: model-driven data analysis (template fitting methods) and data-driven analysis, which can use machine learning methods. To solve this problem, we use data-driven analysis, more specifically GPz (which uses Gaussian processes) and ANNz2 (which mainly uses neural networks), both python software.
Prerequisites: machine learning and basic math knowledge