Jamie McCullough

Star-forming spiral galaxies have less crowded extragalactic neighborhoods, making them excellent candidates for mapping cosmic structure.

McCullough, Amon, Legnani, Gruen et al. 2024

Abstract

Modeling the intrinsic alignment (IA) of galaxies poses a challenge to weak lensing analyses. The Dark Energy Survey, limited to pure blue, star-forming galaxies is expected to be less impacted by IA. The cosmological parameter constraints from this blue sample are stable to IA model choice, unlike the passive galaxies in the full DES Y3 sample, the goodness-of-fit is improved and the $\Omega_{\rm m}$ and $S_8$ better agree with Planck Cosmic Microwave Background observations. Mitigating intrinsic alignments via sample selection, instead of flexible model choices, can reduce uncertainty in $S_8$ by a factor of 1.5, with less uncertain IA on small scales.

Available data products

Read the paper: arxiv, journal (TBD)
For DES Y3 data access: See the Year 3 Cosmology Data Release
For those doing cosmology with the fiducial Y3 blue sample:

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 ›

Calibration for the blue sample

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.

For more information, see Table I in the paper.
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

Major Collaborators

Alexandra Amon

Elisa Legnani

Daniel Gruen

Using the cosmology results: an example

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: