Identification and Investigation of Interacting Galaxies Using
Kiyoaki OMORI,
Spatially
Resolved
Data
Tsutomu T. TAKEUCHI

Kiyoaki Christopher Omori, Tsutomu
T. Takeuchi
Nagoya University
k.omori116@gmail.com

Division of Particle and Astrophysical Science, Nagoya University
Motivation:
Galaxy interactions are a very fundamental process when discussing galaxy evolution, but many processes that occur during interactions are not fully understood, andquantitative studies are not trivial. Identifying interacting galaxies is also difficult, as analyzing images is not sufficient.
This work focuses on the star formation properties of interacting galaxies, so we can gain an understanding of how interaction affects galaxy evolution. From a galaxy
evolution standpoint, the star formation activity of a galaxy can alter its spectral information, which tells us about properties such as stellar population and chemicalcomposition. These alterations can be seen over a much longer timescale as opposed to optical images.
Spatially resolved star formation properties can tell us about where the star formation activity is occurring or has occurred, and show us if there is a relation to the location ofinteractions. Galaxy interactions can trigger star formation, but where does the star formation occur?

Identification:
Existing studies on identification of interacting galaxies are mostly done on optical images (e.g.
SDSS). Popular methods of identification from optical images are by visual inspection (e.g.
Galaxy Zoo Project (Darg et al. 2009)) and machine learning techniques (e.g. Ackermann et al.
2018). An issue with these methods is misclassifications, which can hinder the science ofinteracting galaxies. Visually based identification are susceptible to miss chance encounters or
post-mergers. We suggest a more physically motivated identification method.

Chance encounter Pearson et al. (2019)

MaNGA Post-Merger

We have spatially resolved data that may possibly help us develop a good indicator ofinteraction. For this study, we have conducted identification of galaxies in the MaNGA
Catalogue (Bundy et al. 2014) and classified them based on spatially resolved stellar dynamics,
looking at MaNGA stellar velocity and stellar velocity dispersion two-dimensional maps.

A)

8945-1902 Stellar Velocity

Investigation:
After identifying interacting galaxies using our method, we have used MaNGA andMaNGA FIREFLY Value Added Catalogue (Goddard et al. 2017 and Parikh et al. 2018)
datamodels to analyze the behaviour of interacting and post-merger systems.
The MaNGA FIREFLY VAC applies the SED Fitting code FIREFLY (Wilkinson et al. 2016)
onto MaNGA DAP Voronoi binned spectra, which returns spatially resolved stellar
population properties such as stellar population age. Each spectra was resolved intoa maximum of 8 SSPS which were then ordered from youngest to oldest, giving aquantitative SFH of the galaxy. The interacting galaxy (galaxy B) on the left) shows
young stellar populations (blue areas in top left plot) particularly in the areas where
the galaxies are interacting, indicating recent star formation possibly being inducedby the merger event. There is also recent star formation occurring in the center ofthe system.

Star Formation History of Interacting Galaxy7443-12703

18945-1902 Velocity Dispersion

75

We identified interacting galaxies by searching for asymmetries and distortions in the stellar
velocity maps. The stellar velocity dispersion maps showed increased values in a) the cores ofthe galaxies, b) locations of interaction and c) merger indicators such as tidal tails.

B)

7443-12704 Stellar Velocity7443-12704 Velocity Dispersion

Post-merger galaxies, like written above, are a common cause of misclassification, as their
optical images look like single galaxies. However, their stellar dynamic maps show disturbances.
A characteristic that has been observed in post-merging systems is “counterrotation”, as notedin studies such as Johnston et al. (2018). The stellar velocity dispersion maps of such galaxies
showed two cores, perhaps an indication of remnants of the two galaxies that merged tocreate the post-merger galaxy.

C)

8143-3702 Stellar Velocity

28143-3702 Velocity Dispersion

Issues:Not all interacting systems show similar
disturbances in their dynamical maps,
and not all post-mergers havecounterrotation. There is no ‘common’feature among all mergers/postmergers, so machine learning networks
cannot be trained with a single criteria
for classification.

384

786

As for the post-merger galaxy (galaxy C) on the left), the star formation history maps
show that the younger stellar populations (blue regions, age < .1 Gyr) lie in a ringlike structure around the core of the galaxy. The central region does not have as
young stellar populations, indicating that star formation activity was more recent in
the ring. This shows a consistency with previous simulation works such as Moreno etal. (2015).

Star Formation History of Post-Merger Galaxy
8143-3702

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45678Conclusion – Future Works
From our works, we can see that focusing on certain features in the dynamical structure maps ofgalaxies can be a supplementary method to classify galaxies as interacting. In particular,classification of post-mergers may be done with greater accuracy by looking for features such ascounterrotation.
Future works will look to:a) Look for further characteristics in other dynamical maps such as gas kinematicsb) Design a machine learning technique that is trained using multiple dynamical maps in addition
to optical imagesc) Do quantitative analysis on the effects of galaxy interaction of local physics of galaxies