We report the application of the new Monte Carlo method, smoothed particle inference (SPI, described in a pair of companion papers), toward analysis and interpretation of X-ray observations of clusters of galaxies with the XMM-Newton satellite. Our sample consists of publicly available well exposed observations of clusters at redshifts z > 0.069, totaling 101 objects. We determine the luminosity and temperature structure of the X-ray emitting gas, with the goal to quantify the scatter and the evolution of the LX-T relation, as well as to investigate the dependence on cluster substructure with redshift. This work is important for the establishment of the potential robustness of mass estimates from X-ray data which in turn is essential toward the use of clusters for measurements of cosmological parameters. We use the luminosity and temperature maps derived via the SPI technique to determine the presence of cooling cores, via measurements of luminosity and temperature contrast. The LX-T relation is investigated, and we confirm that LX ∝ T3. We find a weak redshift dependence (∝ (1 + z) βLT, βlt = 0.50 ± 0.34), in contrast to some Chandra results. The level of dynamical activity is established using the "power ratio" method, and we compare our results to previous application of this method to Chandra data for clusters. We find signs of evolution in the P3/P0 power ratio. A new method, the "temperature two-point correlation function," is proposed. This method is used to determine the "power spectrum" of temperature fluctuations in the X-ray emitting gas as a function of spatial scale. We show how this method can be fruitfully used to identify cooling core clusters as well as those with disturbed structures, presumably due to ongoing or recent merger activity. © 2009. The American Astronomical Society.
CITATION STYLE
Andersson, K., Peterson, J. R., Madejski, G., & Goobar, A. (2009). Characterizing the properties of clusters of galaxies as a function of luminosity and redshift. Astrophysical Journal, 696(1), 1029–1050. https://doi.org/10.1088/0004-637X/696/1/1029
Mendeley helps you to discover research relevant for your work.