Astronomy and Astrophysics – Astrophysics – Cosmology and Extragalactic Astrophysics
Scientific paper
2009-03-31
Astrophys.J.730:127,2011
Astronomy and Astrophysics
Astrophysics
Cosmology and Extragalactic Astrophysics
10 pages, 7 figures, 2 appendices, accepted by ApJ
Scientific paper
10.1088/0004-637X/730/2/127
The thermal Sunyaev-Zel'dovich (tSZ) effect directly measures the thermal pressure of free electrons integrated along the line of sight and thus contains valuable information on the thermal history of the universe. However, the redshift information is entangled in the projection along the line of sight. This projection effect severely degrades the power of the tSZ effect to reconstruct the thermal history. We investigate the tSZ tomography technique to recover this otherwise lost redshift information by cross correlating the tSZ effect with galaxies of known redshifts, or alternatively with matter distribution reconstructed from weak lensing tomography. We investigate in detail the 3D distribution of the gas thermal pressure and its relation with the matter distribution, through our adiabatic hydrodynamic simulation and the one with additional gastrophysics including radiative cooling, star formation and supernova feedback. (1) We find a strong correlation between the gas pressure and matter distribution, with a typical cross correlation coefficient r ~ 0.7 at k . 3h/Mpc and z < 2. This tight correlation will enable robust cross correlation measurement between SZ surveys such as Planck, ACT and SPT and lensing surveys such as DES and LSST, at ~20-100{\sigma} level. (2) We propose a tomography technique to convert the measured cross correlation into the contribution from gas in each redshift bin to the tSZ power spectrum. Uncertainties in gastrophysics may affect the reconstruction at ~ 2% level, due to the ~ 1% impact of gastrophysics on r, found in our simulations. However, we find that the same gastrophysics affects the tSZ power spectrum at ~ 40% level, so it is robust to infer the gastrophysics from the reconstructed redshift resolved contribution.
Jing Yipeng
Lin Weipeng
Shao Jiawei
Zhang Pengjie
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