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Transformationally decoupling clustering and tracer bias

Published online by Cambridge University Press:  01 July 2015

Mark C. Neyrinck
Mark C. Neyrinck*
Affiliation:
Department of Physics and Astronomy, The Johns Hopkins University, Baltimore, MD 21211 email: [email protected]
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Abstract

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Gaussianizing transformations are used statistically in many non-cosmological fields, but in cosmology, we are only starting to apply them. Here I explain a strategy of analyzing the 1-point function (PDF) of a spatial field, together with the ‘essential’ clustering statistics of the Gaussianized field, which are invariant to a local transformation. In cosmology, if the tracer sampling is sufficient, this achieves two important goals. First, it can greatly multiply the Fisher information, which is negligible on nonlinear scales in the usual δ statistics. Second, it decouples clustering statistics from a local bias description for tracers such as galaxies.

Keywords

large-scale structure of universecosmology: theorymethods: statistical
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 10 , Symposium S306: Statistical Challenges in 21st Century Cosmology , May 2014 , pp. 251 - 254
Copyright
Copyright © International Astronomical Union 2015 

References

Aitchison, J. & Brown, J. 1957, The Lognormal Distribution, Department of Applied Economics Monographs, Cambridge University PressGoogle Scholar
Aragón-Calvo, M. A. 2012, MNRAS, submittedGoogle Scholar
Carron, J. 2011, ApJ 738, 86Google Scholar
Carron, J. & Szapudi, I. 2014, MNRAS 439, L11Google Scholar
Coles, P. & Jones, B. 1991, MNRAS 248, 1CrossRefGoogle Scholar
Gott, J. R., Choi, Y.-Y., Park, C., & Kim, J. 2009, ApJL 695, L45Google Scholar
Kitaura, F.-S., Jasche, J., & Metcalf, R. B. 2010, MNRAS 403, 589Google Scholar
Meiksin, A. & White, M. 1999, MNRAS 308, 1179Google Scholar
Neyrinck, M. C. 2011, ApJ 742, 91CrossRefGoogle Scholar
Neyrinck, M. C., Aragón-Calvo, M. A., Jeong, D., & Wang, X. 2014, MNRAS 441, 646Google Scholar
Neyrinck, M. C. & Szapudi, I. 2007, MNRAS 375, L51Google Scholar
Neyrinck, M. C., Szapudi, I., & Rimes, C. D. 2006, MNRAS 370, L66Google Scholar
Neyrinck, M. C., Szapudi, I., & Szalay, A. S. 2009, ApJL 698, L90CrossRefGoogle Scholar
Neyrinck, M. C. & Yang, L. F. 2013, MNRAS 433, 1628CrossRefGoogle Scholar
Rhoads, J. E., Gott, J. R. III, & Postman, M. 1994, ApJ 421, 1Google Scholar
Rimes, C. D. & Hamilton, A. J. S. 2005, MNRAS 360, L82Google Scholar
Springel, V., White, S. D. M., Jenkins, A., Frenk, C. S., Yoshida, N., Gao, L., Navarro, J., Thacker, R., Croton, D., Helly, J., Peacock, J. A., Cole, S., Thomas, P., Couchman, H., Evrard, A., Colberg, J., & Pearce, F. 2005, Nature 435, 629Google Scholar
Szalay, A. S. 1988, ApJ 333, 21Google Scholar
Takahashi, R., Yoshida, N., Takada, M., Matsubara, T., Sugiyama, N., Kayo, I., Nishizawa, A. J., Nishimichi, T., Saito, S., & Taruya, A. 2009, ApJ 700, 479Google Scholar
Weinberg, D. H. 1992, MNRAS 254, 315Google Scholar