Repository for my coursework in the "Deep learning and machine learning in science" course at ELTE

Subject code: deeplea17em
Credits: 2

The final grade was determined by our scores on two in-class Kaggle challenges.

1. Photometric redshift estimation (regression)

Description:

To create a 3D map of the Universe we need to measure 3 coordinates of galaxies. The celestial coordinates on the sky are easy to get, but measuring their distance is much harder. Since the Universe is expanding, the photons from a far away galaxy are also expanded during their long journey. The expansion of photons is the redshift: well known spectral features are shifted to redder colours. According to Hubble's law, the distance is approximately proportional to the redshift.
Since galaxies are very faint, it takes a lot of observing time to spread their light in spectrographs and get high resolution spectrum. So, we have spectroscopic redshift only for a limited set of them, for others, redshift may be estimated form broadband photometry, the brightness of galaxies took by few colour filters. Estimating the redshift from this limited set is called photometric redshift estimation.