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The Starlab Environment for Dense Stellar Systems

Published online by Cambridge University Press:  26 May 2016

Piet Hut
Piet Hut*
Affiliation:
Institute for Advanced Study, Princeton, NJ 08540, U.S.A.

Abstract

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Traditionally, a simulation of a dense stellar system required choosing an initial model, running an integrator, and analyzing the output. Almost all of the effort went into writing a clever integrator that could handle binaries, triples and encounters between various multiple systems efficiently. Recently, the scope and complexity of these simulations has increased dramatically, for three reasons: 1) the sheer size of the data sets, measured in Terabytes, make traditional ‘awking and grepping’ of a single output file impractical; 2) the addition of stellar evolution data brings qualitatively new challenges to the data reduction; 3) increased realism of the simulations invites realistic forms of ‘SOS’: Simulations of Observations of Simulations, to be compared directly with observations. We are now witnessing a shift toward the construction of archives as well as tailored forms of visualization including the use of virtual reality simulators and planetarium domes, and a coupling of both with budding efforts in constructing virtual observatories. This review describes these new trends, presenting Starlab as the first example of a full software environment for realistic large-scale simulations of dense stellar systems.

Type
Hardware and Software Environments
Information
Symposium - International Astronomical Union , Volume 208: Astrophysical Supercomputing using Particle Simulations , 2003 , pp. 331 - 342
Copyright
Copyright © Astronomical Society of the Pacific 2003 

References

Aarseth, S. J. 1963, MNRAS 126 223 CrossRefGoogle Scholar
Aarseth, S. J. 1985, in Multiple Time Scales, ed. Brackbill, J.U. and Cohen, B.I. (New York: Academic), p. 377 Google Scholar
Aarseth, S. J. 2002, these proceedings. Google Scholar
Bettwieser, E. and Sugimoto, D. 1984, MNRAS 208, 439 Google Scholar
Brunner, R.J., Djorgovski, S.G., and Szalay, A.S., (eds.), 2001, Virtual Observatories of the Future, ASP Conference Series, Vol. 225 (San Francisco: ASP).Google Scholar
Cohn, H., Hut, P. & Wise, M. 1989, ApJ 342, 814 Google Scholar
Goodman, J. 1997, ApJ 313, 576 Google Scholar
Heggie, D.C. and Hut, P. 2002, The Gravitational Million-Body Problem [Cambridge University Press]Google Scholar
Hurley, J.R. 2002, these proceedings. Google Scholar
Hurley, J.R., Pols, O.R. and Tout, C.A. 2000, MNRAS 315, 543 CrossRefGoogle Scholar
Makino, J. 1996, ApJ 471, 796 Google Scholar
Makino, J. and Taiji, M. 1998, Scientific Simulations with Special-Purpose Computers — The GRAPE Systems, John Wiley and Sons, Chichester.Google Scholar
Makino, J. 2002, these proceedings. Google Scholar
McMillan, S. 2002, these proceedings. Google Scholar
Murphy, B.W., Cohn, H.N. & Hut, P. 1990, MNRAS, 245, 335 Google Scholar
Portegies Zwart, S.F., McMillan, S.L.W., Hut, P. and Makino, J. 2001, MNRAS 321, 199.Google Scholar
Sugimoto, D. and Bettwieser, E. 1983, MNRAS 204, 19p Google Scholar
Teuben, P.J. 2002, these proceedings. Google Scholar
Teuben, P.J., Hut, P., Levy, S., Makino, J., McMillan, S., Portegies Zwart, S., Shara, M., and Emmart, C. 2001, in Astronomical Data Analysis Software and Systems X , eds. Harnden, F.R. Jr., Primini, F.A. and Payne, H.E., ASP Conference Series, Vol. 238 (San Francisco: ASP), 499.Google Scholar
Teuben, P.J., De Young, D., Hut, P., Levy, S., Makino, J., McMillan, S., Portegies Zwart, S., and Slavin, S. 2002, in Astronomical Data Analysis Software and Systems XI , ASP Conference Series (San Francisco: ASP), to appear.Google Scholar
von Hoerner, S. 1960, Zeitschrift fuer Astrophysik, 50, 184 Google Scholar