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| author | Aaron LI <aly@aaronly.me> | 2017-10-16 16:35:34 +0800 | 
|---|---|---|
| committer | Aaron LI <aly@aaronly.me> | 2017-10-16 16:35:34 +0800 | 
| commit | 56ceb950e99f6e66935dd057e4bb3c77d9d62281 (patch) | |
| tree | 1e847e3bf34bef4c4da220376416dcc97257e062 /fg21sim/extragalactic/clusters/main.py | |
| parent | 008914cd9c10633e1f4d04770184fb3412e171b7 (diff) | |
| download | fg21sim-56ceb950e99f6e66935dd057e4bb3c77d9d62281.tar.bz2 | |
clusters: More regular shape for radio halos
Diffstat (limited to 'fg21sim/extragalactic/clusters/main.py')
| -rw-r--r-- | fg21sim/extragalactic/clusters/main.py | 11 | 
1 files changed, 5 insertions, 6 deletions
| diff --git a/fg21sim/extragalactic/clusters/main.py b/fg21sim/extragalactic/clusters/main.py index 7149f69..6b7cefa 100644 --- a/fg21sim/extragalactic/clusters/main.py +++ b/fg21sim/extragalactic/clusters/main.py @@ -127,11 +127,10 @@ class GalaxyClusters:          ----          felong (elongated fraction) ::              Adopt a definition (felong = b/a) similar to the Hubble -            classification for the elliptical galaxies.  As for the -            elliptical galaxies classification, E7 is the limit (e.g., -            Wikipedia), therefore felong is also restricted within -            [0.3, 1.0], and sampled from a cut and absolute normal -            distribution centered at 1.0 with sigma ~0.7/3 (<= 3σ). +            classification for the elliptical galaxies.  Considering that +            radio halos are generally regular, ``felong`` is thus restricted +            within [0.6, 1.0], and sampled from a cut and absolute normal +            distribution centered at 1.0 with sigma ~0.4/3 (<= 3σ).          """          logger.info("Preliminary processes to the catalog ...")          num = len(self.catalog) @@ -142,7 +141,7 @@ class GalaxyClusters:              "lon, lat : longitudes and latitudes [deg]")          logger.info("Added catalog columns: lon, lat.") -        felong_min = 0.3 +        felong_min = 0.6          sigma = (1.0 - felong_min) / 3.0          felong = 1.0 - np.abs(np.random.normal(scale=sigma, size=num))          felong[felong < felong_min] = felong_min | 
