data vector (a .fits file with redshift distributions and 2-point measurements)
Download FITS ›
fiducial Polychord chain(.txt)
modeled with no intrinsic alignment, flexible baryon feedback, and analyzed at all scales
Download Chain ›
source selection dictionary(.pkl)
see instructions below to implement
Download Source Selection Dictionary ›
These galaxies have different redshift distributions and shear calibration than the fiducial Y3 analysis. When running chains, take care to incorporate the relevant systematic calibrations updated here.
Redshift Bin | $$\bar{z}$$ | $$\Delta \bar{z}$$ | $$m$$ | $$\Delta m$$ |
---|---|---|---|---|
1 | 0.3556 | 0.018 | -0.0129 | 0.0091 |
2 | 0.5175 | 0.015 | -0.0180 | 0.0078 |
3 | 0.6994 | 0.011 | -0.0203 | 0.0076 |
4 | 0.8994 | 0.017 | -0.0356 | 0.0076 |
The provided dictionary has keys 0, 1, 2, 3
for each tomographic bin with a list of photometric cell identifiers that meet the pure, blue, star-forming criteria we set in the paper, see Myles & Alarcon et al. 2021 for more information on the cell definitions. For the Y3 data products, you can find the most up-to-date cell assignment for the public source catalogs here, under /catalog/sompz/unsheared
with column name CELL_WIDE
. The dictionary can be read in with the following python snippet:
import pickle import numpy with open('blue_tomo_bins_wide_cell_dictionary.pkl','rb') as f: tomodict = pickle.load(f) #with different python versions, you may need to specify encoding=bytes or latin1 # to produce a selection for e.g. blue tomographic bin 1 given CELL_IDs in 'cat' mask = np.in1d(cat['CELL_WIDE'], tomodict[0]) cat_bin_1_blue = cat[mask]
For an example of ingesting the chain and running a plot, using custom class chain.py
, downloadable here.
#some imports, including the custom chain class from getdist import plots, MCSamples import getdist from chain import chain #place in same directory import numpy as np import matplotlib.pyplot as plt
#format the chain into something getdist can read bluenoia = chain('../data_release/chain_blue_noia_hm20tagn76_83.txt') bluenoia.add_s8() sample = np.array([bluenoia.samples['cosmological_parameters--omega_m'], bluenoia.samples['cosmological_parameters--s8']]) bluenoia_chain = MCSamples(samples=sample.T,names=['om', 'S8'], labels=['\Omega_m', 'S_8'], ranges = {'om':[0.1,0.9],'S8':[0.3,1.0]}, label= r'DES Y3 Blue Cosmic Shear', weights=bluenoia.weight, settings={'boundary_correction_order':0, 'mult_bias_correction_order':1})
#generate an S8 - Om contour plot! plt.rcParams['font.family'] = 'serif' g = plots.get_subplot_plotter(width_inch=4) g.plot_2d([bluenoia_chain], ['om', 'S8'], filled=[True], contour_args=[{'alpha':0.5,'lw':1.2, 'ls':'-','color':'blue'}], diag1d_kwargs={'normalized':True}) g.add_legend(['DES Y3\nBlue Cosmic Shear'],fontsize=10,legend_ncol=1, legend_loc='upper right',framealpha=0) plt.ylabel(r'S$_8 \equiv \sigma_8(\Omega_{\rm m}/0.3)^{1/2}$',fontsize=15) plt.xlabel(r'$\Omega_{\rm m}$',fontsize=15) plt.show()
This will generate a contour plot from the chain that can be combined with other results: