Quantifying correlations between galaxy emission lines and stellar continua

We analyse the correlations between continuum properties and emission line equivalent widths of star-forming and active galaxies from the Sloan Digital Sky Survey. Since upcoming large sky surveys will make broad-band observations only, including strong emission lines into theoretical modelling of spectra will be essential to estimate physical properties of photometric galaxies. We show that emission line equivalent widths can be fairly well reconstructed from the stellar continuum using local multiple linear regression in the continuum principal component analysis (PCA) space. Line reconstruction is good for star-forming galaxies and reasonable for galaxies with active nuclei. We propose a practical method to combine stellar population synthesis models with empirical modelling of emission lines. The technique will help generate more accurate model spectra and mock catalogues of galaxies to fit observations of the new surveys. More accurate modelling of emission lines is also expected to improve template-based photometric redshift estimation methods. We also show that, by combining PCA coefficients from the pure continuum and the emission lines, automatic distinction between hosts of weak active galactic nuclei (AGNs) and quiescent star-forming galaxies can be made. The classification method is based on a training set consisting of high-confidence starburst galaxies and AGNs, and allows for the similar separation of active and star-forming galaxies as the empirical curve found by Kauffmann et al. We demonstrate the use of three important machine learning algorithms in the paper: k-nearest neighbour finding, k-means clustering and support vector machines.Comment: 14 pages, 14 figures. Accepted by MNRAS on 2015 December 22. The paper's website with data and code is at http://www.vo.elte.hu/papers/2015/emissionlines

The Evaluation Of Molecular Similarity And Molecular Diversity Methods Using Biological Activity Data

This paper reviews the techniques available for quantifying the effectiveness of methods for molecule similarity and molecular diversity, focusing in particular on similarity searching and on compound selection procedures. The evaluation criteria considered are based on biological activity data, both qualitative and quantitative, with rather different criteria needing to be used depending on the type of data available

Forecasting of electricity prices in the Spanish electricity market using machine learning tools

The objective of this research assignment was to forecast electricity prices in the Spanish electricity market using three different machine learning techniques: k-nearest neighbours, support vector regression and artificial neural networks. The achieved results were compared and the quality of developed models was evaluated. The project was implemented in Python3.Incomin