diff options
Diffstat (limited to 'fg21sim/extragalactic/clusters')
| -rw-r--r-- | fg21sim/extragalactic/clusters/halo.py | 23 |
1 files changed, 10 insertions, 13 deletions
diff --git a/fg21sim/extragalactic/clusters/halo.py b/fg21sim/extragalactic/clusters/halo.py index a21caca..cc90c7a 100644 --- a/fg21sim/extragalactic/clusters/halo.py +++ b/fg21sim/extragalactic/clusters/halo.py @@ -160,6 +160,7 @@ class RadioHalo1M: self.x_cr = configs.getn(sec+"/x_cr") self.eta_b = self.x_cr # Equipartition between magnetic field and CR self.mass_index = configs.getn(sec+"/mass_index") + self.radius_index = configs.getn(sec+"/radius_index") self.gamma_min = configs.getn(sec+"/gamma_min") self.gamma_max = configs.getn(sec+"/gamma_max") self.gamma_np = configs.getn(sec+"/gamma_np") @@ -212,19 +213,16 @@ class RadioHalo1M: It is known that the halo radius scales non-linearly as the hosting cluster, breaking the self-similiarity, which may be caused by the - magnetic field and the releativistic electron distributions. So - make the halo radius scales with the magnetic field. + magnetic field and the releativistic electron distributions. Reference: Ref.[cassano2007],Sec.4 Unit: [kpc] """ - r = self.radius_turb(self.t_merger) - B = helper.magnetic_field(mass=self.M_obs, z=self.z_obs, - eta_b=self.eta_b, kT_out=self.kT_out) - B0 = helper.magnetic_field(mass=1e15, z=0, - eta_b=self.eta_b, kT_out=self.kT_out) - return r * self.f_radius * (B/B0) + r_turb = self.radius_turb(self.t_merger) + r_cl = helper.radius_cluster(mass=self.M_obs, z=self.z_obs) + r0_cl = helper.radius_cluster(mass=1e15, z=0) + return r_turb * self.f_radius * (r_cl/r0_cl)**self.radius_index @lru_cache() def radius_strip(self, t_merger): @@ -788,11 +786,10 @@ class RadioHaloAM(RadioHalo1M): The halo radius estimated by using the maximum turbulence radius. Unit: [kpc] """ - B = helper.magnetic_field(mass=self.M_obs, z=self.z_obs, - eta_b=self.eta_b, kT_out=self.kT_out) - B0 = helper.magnetic_field(mass=1e15, z=0, - eta_b=self.eta_b, kT_out=self.kT_out) - return self.f_radius * self.radius_turb_max * (B/B0) + r_turb = self.radius_turb_max + r_cl = helper.radius_cluster(mass=self.M_obs, z=self.z_obs) + r0_cl = helper.radius_cluster(mass=1e15, z=0) + return r_turb * self.f_radius * (r_cl/r0_cl)**self.radius_index @property @lru_cache() |