Optimized Galaxy Bayesian Surface Photometry on a Many-Core Platform

  • Igor Kolesnikov INPE
  • Celso Mendes INPE
  • Reinaldo de Carvalho Universidade Cruzeiro do Sul
  • Reinaldo Rosa INPE

Resumo


Parametric computational modeling of galaxies is a process with a high computational cost. The statistical component of modeling, which may involve model refinements in relation to the source brightness distribution, achieves more satisfactory results when the Bayesian approach is employed. In our research, we use GALaxy PHotometric ATtributes (GALPHAT) as our primary tool for data processing. In the current scenario of cosmology, to be scientifically relevant, this type of modeling must be performed on thousands of galaxies. In this article, we present the study and optimization of solutions based on modern HPC platforms, including a many-core processor, that enable effective processing of that amount of galaxies obtained from Sloan Digital Sky Survey.

Referências

Abazajian, K. N., Adelman-McCarthy, J. K., Agüeros, M. A., Allam, S. S., Prieto, C. A., An, D., Anderson, K. S., Anderson, S. F., Annis, J., and Bahcall, N. A. (2009). The seventh data release of the sloan digital sky survey. The Astrophysical Journal Supplement Series, 182(2):543.

Andredakis, Y. and Sanders, R. (1994). Exponential bulges in late-type spirals: an improved description of the light distribution. Monthly Notices of the Royal Astronomical Society, 267(2):283–296.

Barden, M., Häußler, B., Peng, C. Y., McIntosh, D. H., and Guo, Y. (2012). Galapagos: from pixels to parameters. Monthly Notices of the Royal Astronomical Society, 422(1):449–468.

Bernardi, M., Fischer, J.-L., Sheth, R., Meert, A., Huertas-Company, M., Shankar, F., and Vikram, V. (2017). Comparing pymorph and sdss photometry–ii. the differences are more than semantics and are not dominated by intracluster light. Monthly Notices of the Royal Astronomical Society, 468(3):2569–2581.

Bernardi, M., Sheth, R. K., Annis, J., Burles, S., Eisenstein, D. J., Finkbeiner, D. P., Hogg, D. W., Lupton, R. H., Schlegel, D. J., SubbaRao, M., et al. (2003). Early-type galaxies in the sloan digital sky survey. iii. the fundamental plane. The Astronomical Journal, 125(4):1866.

Bertin, E. and Arnouts, S. (1996). Sextractor: software for source extraction. Astronomy and Astrophysics Supplement Series, 117(2):393–404.

Chib, S. and Greenberg, E. (1995). Understanding the metropolis-hastings algorithm. The american statistician, 49(4):327–335.

Guo, Y., McIntosh, D. H., Mo, H., Katz, N., Van Den Bosch, F. C., Weinberg, M., Weinmann, S. M., Pasquali, A., and Yang, X. (2009). Structural properties of central galaxies in groups and clusters. Monthly Notices of the Royal Astronomical Society, 398(3):1129–1149.

Häussler, B., McIntosh, D. H., Barden, M., Bell, E. F., Rix, H.-W., Borch, A., Beckwith, S. V., Caldwell, J. A., Heymans, C., Jahnke, K., et al. (2007). Gems: galaxy tting catalogs and testing parametric galaxy tting codes: Galt and gim2d. The Astrophysical Journal Supplement Series, 172(2):615.

Hyde, J. B. and Bernardi, M. (2009). Curvature in the scaling relations of early-type galaxies. Monthly Notices of the Royal Astronomical Society, 394(4):1978–1990.

Kass, R. E. and Raftery, A. E. (1995). Bayes factors. Journal of the American Statistical Association, 90(430):773–795.

Kolesnikov, I. (2020). Study and optimization for high performance processing with galphat. Master's thesis, Instituto Nacional de Pesquisas Espaciais (INPE), São José dos Campos.

La Barbera, F., De Carvalho, R., de La Rosa, I., Lopes, P., Kohl-Moreira, J., and Capelato, H. (2010). Spider–i. sample and galaxy parameters in the grizyjhk wavebands. Monthly Notices of the Royal Astronomical Society, 408(3):1313–1334.

La Barbera, F., De Carvalho, R., Kohl-Moreira, J., Gal, R., Soares-Santos, M., Capaccioli, M., Santos, R., and Sant'Anna, N. (2008). 2dphot: a multi-purpose environment for the two-dimensional analysis of wide-eld images. Publications of the Astronomical Society of the Pacic, 120(868):681.

Mendel, J. T., Simard, L., Palmer, M., Ellison, S. L., and Patton, D. R. (2013). A catalog of bulge, disk, and total stellar mass estimates for the sloan digital sky survey. The Astrophysical Journal Supplement Series, 210(1):3.

Peng, C. Y., Ho, L. C., Impey, C. D., and Rix, H.-W. (2002). Detailed structural decomposition of galaxy images. The Astronomical Journal, 124(1):266.

Sérsic, J. (1963). Inuence of the atmospheric and instrumental dispersion on the brightness distribution in a galaxy. Boletin de la Asociacion Argentina de Astronomia La Plata Argentina, 6:41.

Simard, L. (1998). Gim2d: an iraf package for the quantitative morphology analysis of In Astronomical Data Analysis Software and Systems VII, volume distant galaxies. 145, page 108.

Simard, L., Mendel, J. T., Patton, D. R., Ellison, S. L., and McConnachie, A. W. (2011). A catalog of bulge+ disk decompositions and updated photometry for 1.12 million galaxies in the sloan digital sky survey. The Astrophysical Journal Supplement Series, 196(1):11.

Stalder, D., de Carvalho, R. R., Weinberg, M. D., Rembold, S. B., Moura, T. C., Rosa, R. R., and Katz, N. (2017). Bayesian surface photometry analysis for early-type galaxies. arXiv preprint arXiv:1711.02188.

Stalder Diaz, D. H. (2017). Applied computing to study structural and enviromental properties of SDSS's galaxies / Computação aplicada ao estudo das propriedades estruturais e ambientais de galáxias do SDSS. PhD thesis, Instituto Nacional de Pesquisas Espaciais (INPE), São José dos Campos.

Stokes, J. (2002). Introduction to multithreading, superthreading and hyperthreading. ArsTechnica. com.

Varoquaux, G. and Grisel, O. (2009). Joblib: running python function as pipeline jobs. 2009. Available from https://pypi.python.org/pypi/joblib#downloads.

Vikram, V., Wadadekar, Y., Kembhavi, A. K., and Vijayagovindan, G. (2010). Pymorph: automated galaxy structural parameter estimation using python. Monthly Notices of the Royal Astronomical Society, 409(4):1379–1392.

Weinberg, M. D., Yoon, I., and Katz, N. (2013). A remarkably simple and accurate method for computing the bayes factor from a Markov Chain Monte Carlo simulation of the posterior distribution in high dimension. arXiv preprint arXiv:1301.3156.

Yoon, I., Weinberg, M. D., and Katz, N. (2011). New insights into galaxy structure from galphat–i. motivation, methodology and benchmarks for sérsic models. Monthly Notices of the Royal Astronomical Society, 414(2):1625–1655.
Publicado
21/10/2020
KOLESNIKOV, Igor; MENDES, Celso; DE CARVALHO, Reinaldo; ROSA, Reinaldo. Optimized Galaxy Bayesian Surface Photometry on a Many-Core Platform. In: SIMPÓSIO EM SISTEMAS COMPUTACIONAIS DE ALTO DESEMPENHO (SSCAD), 21. , 2020, Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2020 . p. 335-346. DOI: https://doi.org/10.5753/wscad.2020.14081.