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adjustable DLA clustering by randomly shifting DLA redshift #3

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14 changes: 13 additions & 1 deletion py/qsotools/mocklib.py
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Original file line number Diff line number Diff line change
Expand Up @@ -221,6 +221,10 @@ class DLASampler():
settled on resampling the input Gaussian field, which reduced the excess
clustering of DLAs.

This can be further tuned with ``sigma_kms`` option, which adds random
velocity separation to DLA redshift obtained from abundance matching. It
adds random ``dz = (1 + z_dla) * Normal(0, sigma_kms) / c`` to DLA redshift

Other methods tested:
- Optical depth mapping on the resampled (0.2 A) skewers. The PDF turns
out non-trivial when smoothed; and you can't control excess clustering.
Expand All @@ -245,13 +249,14 @@ def fn_z_evo(z, zpivot=2.4, gamma_l=1.5):
return ((1 + z) / (1 + zpivot))**gamma_l

def __init__(
self, wide_pix=2**10., nmin=19., nmax=23.,
self, wide_pix=2**10., sigma_kms=0, nmin=19., nmax=23.,
zmin=0, zmax=20., nzbins=5000
):
self.nmin = nmin
self.nmax = nmax

self.wide_pix = None
self.sigma_kms = sigma_kms
self.sigma_scale = None
self.set_var_gauss(wide_pix)

Expand Down Expand Up @@ -406,6 +411,13 @@ def _downsample(x):
num_dlas = w.sum()

z_dlas = refac_z[w]
if self.sigma_kms > 0:
# Store and restore random state for backwards reproducibility
state = RNST.bit_generator.state
z_dlas += (1 + z_dlas) * RNST.normal(
0, self.sigma_kms, size=z_dlas.size) / LIGHT_SPEED
RNST.bit_generator.state = state

Nhi_dlas = self.get_random_NHi(num_dlas, RNST)
id_dlas = np.array([
hash(f"{mockids[jj]:020d}{x:02d}") for x in np.arange(num_dlas)
Expand Down
5 changes: 4 additions & 1 deletion py/qsotools/scripts/generate_mocks.py
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Expand Up @@ -101,6 +101,9 @@ def get_parser():
parser.add_argument(
"--invcdf-nz", default=PKG_ICDF_Z_TABLE,
help="Table for inverse cdf of n(z).")
parser.add_argument(
"--dla-sigma-kms", default=0, type=float,
help="Random shift central DLA redshift.")

parser.add_argument(
"--chunk-dyn", action="store_true",
Expand Down Expand Up @@ -590,7 +593,7 @@ def main():
args.z_forest_min = 0
args.keep_nolya_pixels = True
args.save_full_flux = True
dla_sampler = lm.DLASampler()
dla_sampler = lm.DLASampler(sigma_kms=args.dla_sigma_kms)
else:
dla_sampler = None

Expand Down