How to evade the sample variance limit on measurements of redshift-space distortions

By P. Mcdonald, Uros Seljak. 2008

202 citations recorded by the source corpus.

We show how to use multiple tracers of large-scale density with different biases to measure the redshift-space distortion parameter β ≡ b−1f ≡ b−1d ln D/d ln a (where D is the growth factor and a the expansion factor), to, as the signal-to-noise (S/N) of a survey increases, much better precision than one could achieve with a single tracer (to arbitrary precision in the low noise limit). In combination with the power spectrum of the tracers this would allow a more precise measurement of the bias-free velocity divergence power spectrum, f2Pm, with the ultimate, zero noise limit, being that f2Pm can be measured as well as would be possible if velocity divergence was observed directly, with maximum rms improvement factor ∼ [5.2(β2+2β+2)/β2]1/2 (e.g., ≃ 10 times better than a single tracer with β = 0.4). This would allow a determination of fD as a function of redshift with an error as low as ∼ 0.1% (again, in the idealized case of the zero noise limit). The ratio b2/b1 can be determined with an even greater precision than β, potentially producing, when measured as a function of scale, an exquisitely sensitive probe of the onset of non-linear bias. We also extend in more detail previous work on the use of the same technique to measure non-Gaussianity. Currently planned redshift surveys are typically designed with S/N ∼ 1 on scales of interest, which severely limits the usefulness of our method. Our results suggest that there are potentially large gains to be achieved from technological or theoretical developments that allow higher S/N, or, in the long term, surveys that simply observe a higher number density of galaxies.

Subfields: Astronomy and Astrophysics, Instrumentation, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/564a73c508acb24028e605c169c53c563f40cb0b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Astronomy and Astrophysics

  1. D. Schneider
  2. Daniel J. Eisenstein
  3. Simon D. M. White
  4. Will J. Percival
  5. J. Brinkmann
  6. L. Hernquist
  7. P. Hopkins
  8. D. Weinberg

In Instrumentation

  1. D. Schneider
  2. Simon D. M. White
  3. F. V. D. Bosch
  4. Risa Wechsler
  5. M. Blanton
  6. L. Hernquist
  7. P. Hopkins
  8. C. Conroy

In Statistical and Nonlinear Physics

  1. Uros Seljak
  2. J. Tinker
  3. M. Blanton
  4. M. White
  5. J. Brinkmann
  6. Risa Wechsler
  7. P. Mcdonald
  8. C. Hirata

Citations in the corpus