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Climbing halo merger trees with TreeFrog

Published online by Cambridge University Press:  05 August 2019

Pascal J. Elahi*
Affiliation:
International Centre for Radio Astronomy Research, University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia ARC Centre of Excellence for All Sky Astrophysics in 3 Dimensions (ASTRO 3D), UWA Node, University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia
Rhys J. J. Poulton
Affiliation:
International Centre for Radio Astronomy Research, University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia ARC Centre of Excellence for All Sky Astrophysics in 3 Dimensions (ASTRO 3D), UWA Node, University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia
Rodrigo J. Tobar
Affiliation:
International Centre for Radio Astronomy Research, University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia
Rodrigo Cañas
Affiliation:
International Centre for Radio Astronomy Research, University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia ARC Centre of Excellence for All Sky Astrophysics in 3 Dimensions (ASTRO 3D), UWA Node, University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia
Claudia del P. Lagos
Affiliation:
International Centre for Radio Astronomy Research, University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia ARC Centre of Excellence for All Sky Astrophysics in 3 Dimensions (ASTRO 3D), UWA Node, University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia
Chris Power
Affiliation:
International Centre for Radio Astronomy Research, University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia ARC Centre of Excellence for All Sky Astrophysics in 3 Dimensions (ASTRO 3D), UWA Node, University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia
Aaron S. G. Robotham
Affiliation:
International Centre for Radio Astronomy Research, University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia
*
Author for correspondence: Pascal J. Elahi, E-mail: pascal.elahi@icrar.org
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Abstract

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We present TreeFrog, a massively parallel halo merger tree builder that is capable comparing different halo catalogues and producing halo merger trees. The code is written in c++11, use the MPI and OpenMP API’s for parallelisation, and includes python tools to read/manipulate the data products produced. The code correlates binding energy sorted particle ID lists between halo catalogues, determining optimal descendant/progenitor matches using multiple snapshots, a merit function that maximises the number of shared particles using pseudo-radial moments, and a scheme for correcting halo merger tree pathologies. Focusing on VELOCIraptor catalogues for this work, we demonstrate how searching multiple snapshots spanning a dynamical time significantly reduces the number of stranded halos, those lacking a descendant or a progenitor, critically correcting poorly resolved halos. We present a new merit function that improves the distinction between primary and secondary progenitors, reducing tree pathologies. We find FOF accretion rates and merger rates show similar mass ratio dependence. The model merger rates from Poole, et al. [2017, 472, 3659] agree with the measured net growth of halos through mergers.

Type
Research Article
Copyright
Copyright © Astronomical Society of Australia 2019 

References

Arthur, J., et al., 2017, MNRAS, 464, 2027CrossRefGoogle Scholar
Avila, S., et al., 2014, MNRAS, 441, 3488CrossRefGoogle Scholar
Bagla, J. S., Prasad, J., 2006, MNRAS, 370, 993CrossRefGoogle Scholar
Baugh, C. M., et al., 2018, preprint, arXiv:1808.08276Google Scholar
Behroozi, P. S., Wechsler, R. H., Wu, H.-Y., Busha, M. T., Klypin, A. A., Primack, J. R., 2013, ApJ, 763, 18CrossRefGoogle Scholar
Benson, A. J., 2017, MNRAS, 467, 3454CrossRefGoogle Scholar
Bhattacharyya, A., 1943, Bull. Calcutta Math. Soc., 35, 99Google Scholar
Boylan-Kolchin, M., Springel, V., White, S. D. M., Jenkins, A., Lemson, G., 2009, MNRAS, 398, 1150CrossRefGoogle Scholar
Cañas, R., Elahi, P. J., Welker, C., Lagos, C. d. P., Power, C., Dubois, Y., Pichon, C., 2018, preprint, p. arXiv:1806.11417Google Scholar
Cole, S., Lacey, C. G., Baugh, C. M., Frenk, C. S., 2000, MNRAS, 319, 168CrossRefGoogle Scholar
Comparat, J., Prada, F., Yepes, G., Klypin, A., 2017, MNRAS, 469, 4157CrossRefGoogle Scholar
Dubois, Y., et al., 2014, MNRAS, 444, 1453CrossRefGoogle Scholar
Elahi, P. J., et al., 2016, MNRAS, 458, 1096CrossRefGoogle Scholar
Elahi, P. J., Welker, C., Power, C., Lagos, C. d. P., Robotham, A. S. G., Cañas, R., Poulton, R., 2018, MNRAS, 475, 5338CrossRefGoogle Scholar
Elahi, P. J., Cañas, R., Tobar, R. J., Willis, J. S., Lagos, C. d. P., Power, C., Robotham, A. S. G., 2019, arXiv e-prints, p. arXiv:1902.01010Google Scholar
Fakhouri, O., Ma, C.-P., 2008, MNRAS, 386, 577CrossRefGoogle Scholar
Fakhouri, O., Ma, C.-P., Boylan-Kolchin, M., 2010, MNRAS, 406, 2267CrossRefGoogle Scholar
Foreman-Mackey, D., Hogg, D. W., Lang, D., Goodman, J., 2013, PASP, 125, 306CrossRefGoogle Scholar
Genel, S., Bouché, N., Naab, T., Sternberg, A., Genzel, R., 2010, ApJ, 719, 229CrossRefGoogle Scholar
Han, J., Cole, S., Frenk, C. S., Benitez-Llambay, A., Helly, J., 2018, MNRAS, 474, 604CrossRefGoogle Scholar
Hunter, J. D., 2007, Computing In Science & Engineering, 9, 90CrossRefGoogle Scholar
Jiang, L., Helly, J. C., Cole, S., Frenk, C. S., 2014, MNRAS, 440, 2115CrossRefGoogle Scholar
Jones, E., Oliphant, T., Peterson, P., et al., 2001–, SciPy: Open source scientific tools for Python, http://www.scipy.org/Google Scholar
Klypin, A., Prada, F., 2018, preprint, p. arXiv:1809.03637Google Scholar
Klypin, A., Yepes, G., Gottlöber, S., Prada, F., Heß, S., 2016, MNRAS, 457, 4340CrossRefGoogle Scholar
Knebe, A., et al., 2011a, MNRAS, 415, 2293CrossRefGoogle Scholar
Knebe, A., Libeskind, N. I., Doumler, T., Yepes, G., Gottlöber, S., Hoffman, Y., 2011b, MNRAS, 417, L56CrossRefGoogle Scholar
Knebe, A., et al., 2013, MNRAS, 435, 1618CrossRefGoogle Scholar
Knebe, A., et al., 2017, preprint, arXiv:1712.06420Google Scholar
Knollmann, S. R., Knebe, A., 2009, ApJS, 182, 608CrossRefGoogle Scholar
Lagos, C. d. P., Tobar, R. J., Robotham, A. S. G., Obreschkow, D., Mitchell, P. D., Power, C., Elahi, P. J., 2018, MNRAS, p. 2321Google Scholar
Lee, J., et al., 2014, MNRAS, 445, 4197CrossRefGoogle Scholar
Onions, J., et al., 2012, MNRAS, 423, 1200CrossRefGoogle Scholar
Parkinson, H., Cole, S., Helly, J., 2008, MNRAS, 383, 557CrossRefGoogle Scholar
Pedregosa, F., et al., 2011, Journal of Machine Learning Research, 12, 2825Google Scholar
Poole, G. B., Angel, P. W., Mutch, S. J., Power, C., Duffy, A. R., Geil, P. M., Mesinger, A., Wyithe, S. B., 2016, MNRAS, 459, 3025CrossRefGoogle Scholar
Poole, G. B., Mutch, S. J., Croton, D. J., Wyithe, S., 2017, MNRAS, 472, 3659CrossRefGoogle Scholar
Poulton, R. J. J., Robotham, A. S. G., Power, C., Elahi, P. J., 2018, preprint, arXiv:1809.06043Google Scholar
Rubner, Y., Tomasi, C., Guibas, L. J., 1998, in Proceedings of the Sixth International Conference on Computer Vision. ICCV ’98. IEEE Computer Society, Washington, DC, USA, p. 59–, http://dl.acm.org/citation.cfm?id=938978.939133Google Scholar
Schaye, J., et al., 2015, MNRAS, 446, 521CrossRefGoogle Scholar
Schneider, A., et al., 2016, Journal of Cosmology and Astro-Particle Physics, 2016, 047Google Scholar
Springel, V., 2005, MNRAS, 364, 1105CrossRefGoogle Scholar
Springel, V., et al., 2005, Nature, 435, 629CrossRefGoogle Scholar
Srisawat, C., et al., 2013, MNRAS, 436, 150CrossRefGoogle Scholar
Sutter, P. M., Elahi, P., Falck, B., Onions, J., Hamaus, N., Knebe, A., Srisawat, C., Schneider, A., 2014, MNRAS, 445, 1235CrossRefGoogle Scholar
Tinker, J. L., Robertson, B. E., Kravtsov, A. V., Klypin, A., Warren, M. S., Yepes, G., Gottlöber, S., 2010, ApJ, 724, 878CrossRefGoogle Scholar
Vogelsberger, M., et al., 2014, Nature, 509, 177CrossRefGoogle Scholar
Wang, Y., et al., 2016, MNRAS, 459, 1554CrossRefGoogle Scholar
Warren, M. S., Abazajian, K., Holz, D. E., Teodoro, L., 2006, ApJ, 646, 881CrossRefGoogle Scholar