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Mergers - Dynamical Models of Interacting Galaxies? MNRAS 000,1{29 (2015) Preprint 5 April 2016 Compiled using MNRAS LATEX style file v3.0 Galaxy Zoo: Mergers - Dynamical Models of Interacting Galaxies? Anthony J. Holincheck,1y John F. Wallin,2zx Kirk Borne,1 Lucy Fortson,3 Chris Lintott,4 Arfon M. Smith,5 { Steven Bamford,6 William C. Keel,7 and Michael Parrish5 1School of Physics, Astronomy & Computational Sciences, George Mason University, 4400 University Drive, Fairfax, VA, 22030, USA 2Center for Computational Science & Department of Physics and Astronomy, Middle Tennessee State University, 1301 East Main Street, Murfreesboro, TN, 37132, USA 3School of Physics and Astronomy, University of Minnesota, 116 Church St. SE, Minneapolis, MN 55455, USA 4Oxford Astrophysics, Denys Wilkinson Building, Keble Road, Oxford OX1 3RH, UK 5The Adler Planetarium, 1300 S. Lake Shore Drive, Chicago, IL 60605, USA 6School of Physics & Astronomy, The University of Nottingham, University Park, Nottingham, NG7 2RD, UK 7Department of Physics and Astronomy, University of Alabama, Box 870324, Tuscaloosa, AL 35487, USA Accepted XXX. Received YYY; in original form ZZZ ABSTRACT The dynamical history of most merging galaxies is not well understood. Correlations between galaxy interaction and star formation have been found in previous studies, but require the context of the physical history of merging systems for full insight into the processes that lead to enhanced star formation. We present the results of simu- lations that reconstruct the orbit trajectories and disturbed morphologies of pairs of interacting galaxies. With the use of a restricted three-body simulation code and the help of Citizen Scientists, we sample 105 points in parameter space for each system. We demonstrate a successful recreation of the morphologies of 62 pairs of interact- ing galaxies through the review of more than 3 million simulations. We examine the level of convergence and uniqueness of the dynamical properties of each system. These simulations represent the largest collection of models of interacting galaxies to date, providing a valuable resource for the investigation of mergers. This paper presents the simulation parameters generated by the project. They are now publicly available in electronic format at http://data.galaxyzoo.org/mergers.html. Though our best- fit model parameters are not an exact match to previously published models, our method for determining uncertainty measurements will aid future comparisons be- tween models. The dynamical clocks from our models agree with previous results of the time since the onset of star formation from star burst models in interacting systems and suggests that tidally induced star formation is triggered very soon after closest approach. Key words: galaxies: interactions, galaxies: kinematics and dynamics; galaxies: pe- culiar, methods: numerical arXiv:1604.00435v1 [astro-ph.GA] 1 Apr 2016 1 INTRODUCTION One of the major processes affecting the formation and evo- ? We would like to thank the thousands of members of Galaxy lution of galaxies is mutual interaction. These encounters Zoo: Mergers for their help on the project. A full list of those can include gravitational tidal distortion, mass transfer, and who contributed is available at http://data.galaxyzoo.org/ even mergers. In any hierarchical model, mergers are the key galaxy-zoo-mergers/authors.html mechanism in galaxy formation and evolution. Galaxy inter- y E-mail: [email protected] actions take place on timescales of a billion years or more. z E-mail: [email protected] x Faculty Affiliate School of Physics, Astronomy & Computa- Even though we are able to look back through time to ear- tional Science, George Mason University lier epochs and see galaxies at many stages of interaction, { Currently a staff member at GitHub, Inc. we cannot hope to observe any particular system for more c 2015 The Authors 2 Holincheck et al. than just a single instant in time. Because of this static view evolved, more elaborate versions of libraries have been con- provided by observations, researchers have turned to simu- structed (Korovyakovskaya & Korovyakovskij 1982; Howard lations of interacting systems. The assumption in modelling et al. 1993; Chilingarian et al. 2010). If the volume of po- interacting systems is that distorted morphological features tential parameter space used to describe a single pair of in- (tidal bridges, tails, etc.) are tied to the dynamical history teracting galaxies is large, then the possible number of pa- of the system (Toomre & Toomre 1972). By matching tidal rameter sets to describe all interacting pairs must be even features in models, we are matching the overall dynamical larger. Libraries of previous simulations will offer only rough history of the systems. matches at best. A further limitation is that the viewing an- gle parameters will be added to the list of parameters to be selected, further increasing the number of dimensions to 1.1 Modelling Populations of Interacting Systems search. Another approach is to attempt automated optimiza- Previous progress in developing detailed models of specific, tion using fitness functions to match simulations to observed observed systems has been ad hoc. Since 2000, a number systems. Wahde(1998) was one of the first to demonstrate of researchers have developed semi-automated methods for the use of a genetic algorithm for optimizing models of in- trying to speed the process, (Theis & Kohle 2001; Wahde & teracting galaxies. A genetic algorithm (GA) uses the evo- Donner 2001; Smith et al. 2010). These methods have seen lutionary processes of crossover and mutation to randomly success in matching simulated systems used as truth data, assemble new offspring from an existing population of so- but their application to real sets of interacting galaxies usu- lutions. The parent solutions are chosen to generate off- ally requires detailed observational data beyond a simple spring in proportion to their fitness. The more fit, or better image as well as customized fitness functions. For example, matched, to the target system an individual model is, the one additional piece of data critical for verifying these mod- more often it will contribute its genetic information to subse- els is the velocity fields of the tidal features. To compare the quent generations. The genes in this GA approach are simply velocity fields of simulations and models, we need to have the dynamical model parameters like inclination, mass ratio, relatively high velocity resolution (typically about ∼ 1=10 disc orientations, etc. The fitness function to be evaluated of the disc rotation velocity or ∼ 30 km s−1) and spatial and optimized needs to provide a meaningful quantitative resolution (about ∼ 1=10 of the disc sizes for the galaxies, value for how well a given simulation result matches the tar- ∼ 5 arcmin for close galaxies) for the systems being mod- get system. elled. There are only a few systems where these kinematic data are available. Hence other methods that do not rely on With a fitness function defined, a GA is seeded with an kinematic data must be explored to obtain any systematic initial population and then set to evolve for some number of approach in learning models of interacting galaxies. generations. Researchers trying to optimize galaxy models Determining the dynamical parameters for a model of a will use a population size of typically between 50 and 1000, real system of interacting galaxies can be a time-consuming and then will evolve the system for 50 generations. There is process. Toomre & Toomre(1972) offered a series of coarse, an extensive body of research on the convergence behaviour yet revealing, parameter studies. For example they showed of GAs in terms of the nature of the fitness landscape being the different morphologies produced by varying the inclina- studied and the particular evolutionary mechanisms invoked tion angle while holding other values fixed. Using the physi- (De Jong 2006). cal intuition gained from such studies, researchers attempt- At least three groups have published results of GA op- ing to model a specific system can narrow the range of simu- timization of models of interacting galaxies: Wahde & Don- lation parameters to be used. However, there is a tremendous ner(2001), Theis & Harfst(2000), and Smith et al.(2010). amount of trial and error involved in finding a best-fit orbit. They all demonstrate convergence to one or a few best-fit This is especially true if one is trying to match the kine- models for real systems based on matching morphological 1 matics data from the simulation to observations. Hammer features. However, the convergence radius for these systems et al.(2009) claims that `[t]he accurate modelling of both is not well documented. A large radius of convergence (per- morphology and kinematics takes several months, from two haps even global in scale) is demonstrated by Wahde(1998) to six months for a well-experimented user.' The `Identikit' and others when they are modelling artificial systems. These software (Barnes & Hibbard 2009) has made this process systems use the simulation code itself to generate a high- easier, but it still requires a great deal of effort to match resolution simulated observation of a hypothetical system real systems. of interacting galaxies. The researchers are then able to use Several attempts at speeding and even automating this their GA to optimize and find a close fit to the known dy- process have been published. One approach is to build a li- namical parameters. Additionally, to demonstrate conver- brary of simulation results. Researchers would then browse gence as well as some amount of uniqueness, it is customary the set of pre-computed results to look for simulated systems to take the resulting best-fit models, apply a set of ran- that matched observed ones. The search is conducted by ex- dom alterations to the dynamical parameters, and then use amining the particle output at various time steps and rotat- these altered models as the initial population in a new GA ing in three dimensions to attempt to locate the proper view- run.
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