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The MegaMorph (Measurement of Galaxy Morphology) project is developing several novel approaches to aid the decomposition of galaxy images into their constituent physical components.


The software developed by MegaMorph: GALFITM and GALAPAGOS-2, are now publicly available from the project’s new homepage.

We also have a new Google+ page, where updates and news will be posted.

Our QNRF funding for the project has now come to an end, but the MegaMorph team is continuing its development and science activity, and we have several papers in the pipeline. In addition, a number of external groups are now using GALFITM and GALAPAGOS-2, so we look forward to seeing their results.

The mission

From the latest models of galaxy formation and evolution, it is becoming clear that there is a more fundamental distinction in the galaxy population than the usual classification: elliptical vs. spiral galaxies. The striking difference between elliptical and spiral galaxies is a result of variation in the relative visual prominence of the spheroid and disk components. However, measuring the properties of the single components within a galaxy is considerably more difficult than measuring its overall properties. Galaxies are complex structures and, beyond the general distinction between spheroids and disks, they display a range of higher level features; in addition to spiral arms, these include bars, rings, etc. These complexities make it difficult for computational methods to extract meaningful information.

MegaMorph tackles this problem by utilizing the full set of multi-color information available for each galaxy and applying a variety of novel techniques. We are developing efficient algorithms capable of robustly measuring the properties of the internal components of tens of thousands of galaxies. It is central to our goals that we produce tools that we can put to practical scientific use, both by ourselves, but also by the wider astronomical community.

The people

MegaMorph is led by Steven Bamford at the University of Nottingham, together with Alex Rojas at Carnegie Mellon University in Qatar. The project is primarily funded by a grant from the Qatar National Research Foundation, which provides two postdocs dedicated to tackling this problem, one at Nottingham and another at CMU-Q. These posts were filled by Boris Haeussler and Marina Vika, respectively. Boris has now moved to a postdoc position at Oxford/Herts where, amongst other things, he is applying MegaMorph techniques to the VIDEO survey. In addition, Benedetta Vulcani, Andrea Borch and Jim Cresswell have been employed to work on the project for short periods. Steven Bamford is supported by an STFC Advanced Fellowship. We have also received some funding from Amazon Web Services.

The project also draws on the advice of an international collaboration of astronomers, computer scientists and statisticians, and has strong links with both the Galaxy Zoo and GAMA projects.

Current status

The software developed by MegaMorph: GALFITM and GALAPAGOS-2, are now publicly available from the project’s homepage.

The second paper from the project, Vika et al., is in press with MNRAS and is available at arxiv:1307.4996. Further papers will follow in 2014.

The first paper from the project is published: Haeussler et al., 2013, MNRAS, 430, 330, available via ADS and arXiv.

A more general overview of the MegaMorph project, as of November 2011, is contained in this proceedings article from the SED2011 conference. Also see this older proceedings article from ADASS XX for more background details. Briefly, we have successfully developed multi-band versions of GALFIT and GALAPAGOS and are in the process of thoroughly testing the technique and demonstrating its advantages over previous approaches. We have completed tests using one-component Sersic models and are now well underway with testing bulge-disk models, while starting to produce science results with our single-Sersic fits to galaxies in the GAMA survey. We have also made progress with the inclusion of non-parametric components to account for the rich variety of galaxy features which ‘distract’ conventional model-fitting methods. Finally, we have made it possible to well sample the parameters’ posterior probability space with the aims of (a) assuring the robustness of the approach, (b) quantifying parameter confidence intervals and degeneracies, and (c) performing reliable model selection.

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