Periodic Reporting for period 1 - SUNDIAL (SUrvey Network for Deep Imaging Analysis and Learning)
Reporting period: 2017-04-01 to 2019-03-31
The objective of SUNDIAL is to train researchers to address the most prominent CI topics related to the analysis of Big Data and their application to galaxy evolution studies. These are:
(1) Automatic detection of faint low surface brightness features (dwarf galaxies, merger remnants, intracluster light) in deep astronomical surveys, and interpreting them astrophysically in terms of galaxy formation and evolution.
(2) Automated object recognition in Big Data sets: (a) the unsupervised identification of groups of objects with similar (clustering), properties and (b) the supervised assignment of objects into pre-defined target classes (classification). The addition of prior information from astrophysics will be crucial in both cases.
(3) Simulations of galaxy interaction, their characterisation and visualisation. 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.
We have been working on developing two types of analyses for morphological classification of galaxies in the GAMA catalog (problem 2): an unsupervised and a supervised analysis with prototype-based methods. We assessed whether class structure can be recovered by a clustering of the data generated by the unsupervised Self-Organizing Map (SOM), and investigated if the morphological classification can be reproduced using the GMLVQ method. We are able to produce state-of-the-art results, but are limited by the human bias in morphological classification schemes. For that reason we will aim to go for new physically based classification schemes using new information, especially from the outer parts of galaxies, optimally using the astronomical datasets mentioned before.
As a testbed for problem 3, we use the jellyfish galaxy NGC 1427A, a galaxy with large amounts of gas forming many stars at present, which is presumably falling into the Fornax Cluster, and losing its gas due to ram pressure stripping by the intracluster medium. At present, we are making realistic computer simulations of such late-type dwarf galaxies to model this object falling into the cluster. 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.
To prepare attractive samples for galaxy classification, including spectral information, we have created a K-band infrared imaging survey of the Fornax Cluster. With this survey, together with the optical data of the FDS, we are preparing samples of galaxies with known photometric decompositions, which will serve as training sets for automatic morphological decompositions. For this we are developing new classifiers with more discriminative power, which we measure on the galaxy images. These include faint imaging features and spectral information for part of the dataset. We will also explore how to include spectral data in the automatic methods, using UCDs as a first training set.
We have performed realistic simulations of late-type dwarf galaxies falling into the Fornax Cluster. We are characterising them with a fully developed novel machine learning method for robust detection of multiple low-dimensional manifolds in a potentially significant noisy background. The manifolds 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.