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LoCuSS: Scaling relations between galaxy cluster mass, gas, and stellar content

  • Mulroy, Sarah L.
  • Farahi, Arya
  • Evrard, August E.
  • Smith, Graham P.
  • Finoguenov, Alexis
  • O'Donnell, Christine
  • Marrone, Daniel P.
  • Abdulla, Zubair
  • Bourdin, Hervé
  • Carlstrom, John E.
  • Démoclès, Jessica
  • Haines, Chris P.
  • Martino, Rossella
  • Mazzotta, Pasquale
  • McGee, Sean L.
  • Okabe, Nobuhiro
Published Article
Publication Date
Jan 31, 2019
Submission Date
Jan 31, 2019
DOI: 10.1093/mnras/sty3484
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We present a simultaneous analysis of galaxy cluster scaling relations between weak-lensing mass and multiple cluster observables, across a wide range of wavelengths, that probe both gas and stellar content. Our new hierarchical Bayesian model simultaneously considers the selection variable alongside all other observables in order to explicitly model intrinsic property covariance and account for selection effects. We apply this method to a sample of 41 clusters at $0.15<z<0.30$, with a well-defined selection criteria based on RASS X-ray luminosity, and observations from Chandra / XMM, SZA, Planck, UKIRT, SDSS and Subaru. These clusters have well-constrained weak-lensing mass measurements based on Subaru / Suprime-Cam observations, which serve as the reference masses in our model. We present 30 scaling relation parameters for 10 properties. All relations probing the intracluster gas are slightly shallower than self-similar predictions, in moderate tension with prior measurements, and the stellar fraction decreases with mass. K-band luminosity has the lowest intrinsic scatter with a 95th percentile of 0.16, while the lowest scatter gas probe is gas mass with a fractional intrinsic scatter of $0.16 \pm 0.03$. We find no distinction between the core-excised X-ray or high-resolution Sunyaev-Zel'dovich relations of clusters of different central entropy, but find with modest significance that higher entropy clusters have higher stellar fractions than their lower entropy counterparts. We also report posterior mass estimates from our likelihood model.

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