A combined repository containing all of my S_{1/2} Anomaly and Stacking Anomaly code which is related to the ISW effect.

Readme updated February 12, 2018, by Z Knight

This repository containss mostly Python code, as well as some helper programs and data files.

Other software used: numpy scipy CAMB HEALPix PolSPICE ramdisk.sh optimzeSx.so

Python files:

ISW_template.py: ISW template objects will have the parameters taken from the GNS catalogs. These are meant to be used in a nonlinear template_fit minimization ISW template objects will have a method to produce a template based on the two parameters of the model Pixel values will be DeltaT/T * 2.726K

ISWprofile.py: This program creates plots of mass overdensities for superclusters and supervoids, following the forumulation of Papai and Szapudi, 2010. (herafter PS) It starts with a matter power spectrum from CAMB, contained in file test_matterpower_2015.dat. From there it computes <delta(r)delta_in(R)> and <delta_in(R)^2>, as specified in the paper. These will be numerically integrated and dependant on radius r. Then for a given value of delta_in(R), the conditional expectation <delta(r)|delta_in(R)> is calculated. This is then used to create the mass overdensity as a function of R and r.

KS_showdown.py: Evaluates KS statistic on an ensemble of CMB realizations Compares different methods of template fitting for ISW map

SN_mode_filter.py: Uses covariance matrices for signal (s) and noise (n) with T=s+n model and a mask for selecting a data vector from a healpix map to find a rotation such that covariance matrices <s_iS_j>, <n_in_j> are diagonal Creates an ensemble of simulations from power spectra for s and n, extracts data vector from these, rotates into diagonal frame, squares and averages these to find (S+N)/N eigenvalues.

chisquare_test.py: Program to check that TC^-1T follows chi square distribution as expected

cosmography.py: calculate various distance measures in an expanding universe

gacf.py: functions to implement Gaussian Auto-Correlation Function as presented in ned.ipac.caltech.edu/level5/March02/White/White4.html

get_crosspower.py: load total, primordial, and late Cl power spectra and create cross power: C_l^tot = C_l^(prim,prim) + 2*C_l^(prim,late) + C_l^(late,late)

get_temps.py: Loads map and mask from fits files and coordinates from text files, measures temperatures of cluster and void stacks, and determines S/N ratio using random coordinates

gittesting.py: This file is just for testing git.

inversion_testing.py: Program to explore covariance matrix inversion

ispice.py: This program is not mine, but is included here because I use it. ispice defines tools to run Spice from Python, either in the Planck HFI DMC (aka piolib, objects managed by database) or using FITS files

legendre_test.py: to test accuracy of legendre transforms

legprodint.py: create the I_m,n factor that is the integral of a product of legendre polynomials: I_m,n(x) = \int_-1^x P_m(x')P_n(x')dx' Follows Appendix A (which has typos) of Copi et. al., 2009 Then modified for use in S_{1/2} as in Copi et. al., 2013

likelihood.py: Evaluate the CMB likelihood functions

make_Cmatrix.py: Program to create a covariance matrix for a given set of HEALpix pixels.

make_ISW_map.py: create a HEALpix spherical map containing an ISW map from one or more superclusters or supervoids Pixel values will be DeltaT/T * 2.726K

make_overmass.py: This program creates sets of ISW profiles following the formulation of Papai and Szapudi, 2010. (PS)

make_sims.py: to load simulated ISW maps and simulated CMB maps and add them

mask_check.py: Program to calculate amplitude of template on CMB using various masks

optSxJackknife.py: do Jackknife testing to check for stability of optimized x, S_x, P(x)

optimizeSx1.py: explore the presumed arbitrary cut off point for S_{1/2} by optimizing PTE(S_x) for random CMB realizations

optimizeSx2.py: explore the presumed arbitrary cut off point for S_{1/2} by optimizing PTE(S_x) for random CMB realizations

plot2Ddist.py: The plot2Ddist function plots the joint distribution of 2 variables, with estimated density contours and marginal histograms. Designed to plot parameter distributions for MCMC samples.

quadoctcorr.py: extract l=2,3 a_lm.s from SMICA to compare C(theta) against simulations

redshift_check.py: opens ISW profile files created for various redshifts and compares them. Looks for ratios and... ??

scaledlegs.py: show legendre polynomials * (2l+1) together

sim_stats.py: create simulated early and late CMB maps and analyze them for CMB anomalies

simple_Sonehalf.py: create simplistic CMB simulations and analyze S_{1/2} properties shows trend when increasing l_min Also plots C_2 and S_{1/2} together on 2d plot

surface3d_profiles.py: plotting ISW profiles as surfaces; functions of R and r

template.py: Just a template I used for quickly starting python files

template_fit.py: Program to calculate amplitude of template on CMB

template_fit_SN.py: Program to calculate amplitude of template on CMB using non-direct methods

template_fit_eig.py: Program to calculate amplitude of template on CMB using non-direct methods

Non-python program files:

optimizeSx.c:

• create S_x functions from input file
• create Pval(x) for each S_x using ensemble
• find global minimum for each Pval(x)
• -> can be used to create distribution S(xValMinima)

ramdisk.sh:

spice: the PolSPICE program