The Splashback Radius of Planck SZ Clusters

We present evidence for the existence of the splashback radius in galaxy clusters selected using the Sunyaev–Zeldovich effect, a sample unaffected by systematics related to cluster finding in the optical wavelength range. We show that the deprojected cross-correlation of galaxy clusters found in the...

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Veröffentlicht in:The Astrophysical journal 2019-04, Vol.874 (2), p.184
Hauptverfasser: Zürcher, Dominik, More, Surhud
Format: Artikel
Sprache:eng
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Zusammenfassung:We present evidence for the existence of the splashback radius in galaxy clusters selected using the Sunyaev–Zeldovich effect, a sample unaffected by systematics related to cluster finding in the optical wavelength range. We show that the deprojected cross-correlation of galaxy clusters found in the Planck survey with galaxies detected photometrically in the Pan-STARRS survey shows a sharp steepening feature (a logarithmic slope steeper than −3), which we associate with the splashback radius. We infer the 3D splashback radius for the SZ cluster sample to be , where the cluster sample has an average halo mass of M 500 c  = 3.0 × 10 14 at an average redshift of z  = 0.18. The inferred value of the splashback radius appears marginally consistent with the expected location for dark matter halos in the standard cold dark matter paradigm. However, given the limited precision of our measurements, we cannot conclusively confirm or rule out the smaller splashback radius measured so far in the literature for optically selected galaxy clusters. We show that the splashback radius does not depend on the galaxy magnitude for galaxies fainter than and is present at a consistent location in galaxy populations divided by color. The presence of the splashback radius in the star-forming galaxy population could potentially be used to put lower limits on the quenching timescales for galaxies. We can marginally rule out the contamination of the star-forming galaxy sample by quenched galaxies, but the results would need further verification with deeper data sets.
ISSN:0004-637X
1538-4357
DOI:10.3847/1538-4357/ab08e8