In WP2 we have improved a detection algorithm previously developed by our group, called MTObjects. tool has been compared extensively with other faint object detection methods, and turns out to give a superior performance in most aspects. While the single-band MTObjects has been the most effective, we have also worked on a multi-band version, using graph-representation. This latter version Is very promising, although it is still very calculation-intensive, and therefore cannot handle large images at the moment.
Our comparison of various faint detection methods shows that MTObjects is the best method to use to detect faint galaxies in deep data. It is reliable, fast, and objective. A number of applications has been done with MTO.
In WP3 we have applied ML techniques to work on methods for the classification of galaxies. We are able to produce state-of-the-art results, but are limited by the human bias in morphological classification schemes. In the field of photometric redshifts, we used ML methods to prove that a large part of the erroneous prediction can be related to previously undetected errors in the spectroscopic information (usually assumed in the literature to be "error free"). As an application, we used FDS (the Fornax Deep Survey) data to perform multicomponent structural decompositions of 586 galaxies in the Fornax cluster We also have calculated “non-parametric” measures of morphology (concentration, asymmetry, smoothness, Gini, M20) in order to characterise galaxies systematically independent of the complexity of their structures, and studied the presence of a nucleus. A second application of classification uses ML tools to determine a new sample of UCD galaxies in the Fornax Cluster. Up to now such objects were only found in the very center of galaxy clusters.
In WP4 we have run simulations of dwarf galaxies falling into clusters, including gas, stars and dark matter. The simulations serve to identify the critical characterisation, necessary to optimally identify how observations can be described. Such comparisons will lead to a better parametrisation and understanding of galaxy cluster evolution. To describe these simulations, we have developed models which are able to automatically detect dense (possibly lower-dimensional) structures embedded in a substantial noisy background. As a testbed for comparison with real observations, we use the jellyfish galaxy NGC 1427A, a galaxy with large amounts of gas forming many stars at present. The models describing the simulations produce manifolds, that can be of different dimensionalities and the methodology does not assume their number is known in advance. The method gives us potentially unprecedented possibilities to quantitatively compare simulations with each other and with observations (with full flexibility in defining the observation space). More refined calibration of simulations enabled by this methodology can help us to better understand the physics of dwarf galaxies falling into clusters.
