Speaker
Description
Galaxy clusters are privileged astrophysical laboratories, tracing the most massive dark matter haloes in the Universe and allowing for accurate studies about their gravitational potential. The high masses (>10^14 Msun) of galaxy clusters make them ideal objects to probe new gravity models through gravitational redshift (GRS), which is recently established among the most important probes to detect deviations from general relativity (GR). Traditional GRS detection methods require to stack a high (~10^5) number of spectroscopically confirmed cluster members, analysing the mean rest-frame velocity shift from 0 km/s in a set of radial bins. In my talk, I will present a new method to reduce the necessary number of cluster members to detect GRS, which analyses the overall GRS-induced distortion of a galaxy cluster projected phase-space (PPS). This new method, coded in the Gravitational Redshift in GAlaxy clusters Software (GRAGAS), allows detecting GRS signal in massive galaxy clusters (virial mass M_200c ~10^15 Msun). It is designed to deliver a full dynamical analysis through a Bayesian fit of the PPS, and it has proven to be capable of recovering the original parameters of a mock galaxy clusters validation set. GRAGAS key feature is the flexible likelihood, that allows for an easy inclusion of different gravity models. Moreover, I will show that for massive clusters, although GRS effects are small (≲50 km/s) compared to their velocity dispersion (~1000 km/s), neglecting GRS in dynamical models can lead to an incorrect measurement of velocity anisotropy or to a ~5% overestimate of M_200c values, depending on the sampling algorithm. The GRAGAS method offers a crucial benchmark to test new gravity models even with few cluster members, and a key solution to avoid biases when measuring virial masses of clusters.
| Parallel session | Astrophysical Probes of Dark Matter and Dark Energy |
|---|