Spherically Averaging Ellipsoidal Galaxy Clusters in X-Ray and Sunyaev-Zel'dovich Studies: II. Biases

Astronomy and Astrophysics – Astrophysics – Cosmology and Extragalactic Astrophysics

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24 pages, 15 figures, 1 table. Accepted for Publication in MNRAS. Minor changes to match published version

Scientific paper

This is the second of two papers investigating the spherical averaging of ellipsoidal galaxy clusters in the context of X-ray and Sunyaev-Zel'dovich (SZ) observations. In the present study we quantify the orientation-average bias and scatter in observables that result from spherically averaging clusters described by ellipsoidal generalizations of the NFW profile or a nearly scale-free logarithmic potential. Although the mean biases are small and mostly <1%, the flattest cluster models generally have a significant mean bias; i.e., averaging over all orientations does not always eliminate projection biases. Substantial biases can result from different viewing orientations, where the integrated Compton-y parameter (Y_SZ) and the concentration have the largest scatter (as large as sigma ~10% for Y_SZ), and the emission-weighted temperature (T_X) has the smallest (sigma < ~0.5%). The very small scatter for T_X leads to Y_X and M_gas having virtually the same orientation biases. Substantial scatter is expected for individual clusters (up to sigma ~8%) in the correlation between Y_SZ and Y_X in comparison to the small mean bias (sigma < ~1%) applicable to a random sample of clusters of sufficient size. For ellipsoidal NFW models we show that the orientation bias for the total cluster mass attains a minimum near the radius r_2500 so that the spherically averaged mass computed at this radius is always within ~0.5% of the true value for any orientation. Finally, to facilitate the accounting for orientation bias in X-ray and SZ cluster studies, we provide cubic polynomial approximations to the mean orientation bias and 1-sigma scatter for each cluster observable as a function of axial ratio for the ellipsoidal NFW models.

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