CMB background

This post is a historical background overview of CMB science.

The CMB prediection by Ralph Alpher (George Gamow's Ph.D.) and Robert Herman in about 1948. This is a list of historical paperss from 1965 to 2018 which may provide useful background in CMB (reference BICEP's internal group!).

1965: The accidental discovery by Penzias and Wilson using a horn antenna at Bell laboratory
A Measurement of Excess Antenna Temperature at 4080 Mc/s
A. A. Penzias and R. W. Wilson, ApJ 142, 419(1965).
1981: A. Guth's initial paper of an inflation universe
Inflationary universe: A possible solution to the horizon and flatness problems
A. Guth, Phys. Rev. D 23, 347 (1981).
1982: Linde's initial paper on inflation, addressing some of the self-admitted problems with Guth's model
A new inflationary universe scenario: A possible solution of the horizon, flatness, homogeneity, isotropy, and primordial monopole problems
A.D. Linde, Phys. Lett. B 108, 389 (1982).
1992: First detection of temperature anisotropy in the CMB by COBE satellite. COBE also measureed the spectrum of CMB as a black body with temperature of 2.725 K.
Structure in the COBE Differential Microwave Radiometer First-Year Maps
G. Smoot et al., ApJL 396, L1 (1992).
1997: Seljak and Zaldarriaga's paper initially describing the E and B decomposition of CMB polarization as a way of probing for primordial gravitational waves from the Big Bang, starting the time for CMB polarization measurement experiment.
Signature of Gravity Waves in the Polarization of the Microwave Background
U. Seljak and M. Zaldarriaga, Phys. Rev. Lett. 78, 2054 (1997).
1997: Kamionksowski, Kosowsky, and Stebbins' paper also deriving the E and B basis, in a slightly different way than Seljak and Zaldarriaga, published alongside said paper
A Probe of Primordial Gravity Waves and Vorticity
M. Kamionkowski, A. Kosowsky, A Stebbins, Phys. Rev. Lett. 78, 2058 (1997).
2002: First detection of polarization in the CMB E modes by DASI
Detection of polarization in the cosmic microwave background using DASI
J.M. Kovac et al., Nature 420, 772 (2002).
2014: BICEP claim CMB B mode (tensor-to-scalar ratio) r < 0.2 detection, but later join analysis with Planck confirm there has contamination from foreground
Detection of B-Mode Polarization at Degree Angular Scales by BICEP2
BICEP Rev. Lett. 112, 241101, 2014
2018: Planck Satellite's latest (2018) results on cosmological parameters
Planck 2018 results. VI. Cosmological Parameters
Planck Collaboration et al., A&A 641, A6 (2020).
2018: Planck Satellite's latest (2018) constraints on inflation (using BICEP/Keck's "BK15" data set)
Planck 2018 results. X. Constraints on inflation
Planck Collaboration et al., A&A 641, A10 (2020).
2022: The upper limit on tensor-to-scalar r < 0.032 (95% CL). using BICEP and Planck data
Improved limits on the tensor-to-scalar ratio using BICEP and Planck data
M. Tristram, et al., Phys. Rev. D 105, 083524 – Published 26 April 2022

There are several nice papers to look at

1997: A pedagogical introduction to CMB polarization. Includes a description of how polarization is generated during recombination, a breakdown of E and B and how they relate to Q and U, etc.
A CMB Polarization Primer
W. Hu and M. White, New Astron. 2, 323 (1997).
2016: A nice review on the CMB polarization in 2016,
The Quest for B Modes from Inflationary Gravitational Waves
Marc Kamionkowski and Ely D. Kovetz annual reviews

There are many good books in the field,

An Introduction to Modern Cosmology, Andrew Liddle 3rd Edition
This is a good book for the beginner, it covers all the important aspect in Cosmology
Introduction to Cosmology, Barbara Ryden 2nd Edition
This books describe detail calculation of Universe models, problem and solutions to the Big Bang Universe.
Modern Cosmology, Scott Dodelson, Fabian Schmidt 2nd Edition
This is a good book for graduate level and researcher.
Cosmology, Daniel Baumann
This is resource for advanced students in theoritical physics, cosmology.



How to make an RGB image using aplpy package

This is an example of making a RGB image using aplpy package.
The data is archived from the Spitzer Galactic Legacy Infrared Midplane Survey Extraordinaire
(GLIMPSE).
We will choose 3 over 4 channels data to generate an RGB optical-liked image of M17 region
(Omega Nebula/Swan Nebula/Horseshoe Nebula) ...Read More

1. Read the data 

            
            import aplpy
            import matplotlib.pyplot as plt
            from pylab import arange, show, cm
            cmap='rainbow'
        
            # Galactic Legacy Infrared Midplane Survey Extraordinaire (GLIMPSE)
            fname1 = '/GLM_01550-0075_mosaic_I1.fits'   # chanel 1: 3.6 \mu m
            fname2 = '/GLM_01550-0075_mosaic_I2.fits'   # chanel 2: 4.5 \mu m
            fname3 = '/GLM_01550-0075_mosaic_I3.fits'   # chanel 3: 6 \mu m
            fname4 = '/GLM_01550-0075_mosaic_I4.fits'   # chanel 4: 8 \mu m

2. Make a cube data from 3 input files 

            
            # Make RGB cube (3 dimensional data); This will have ouput file: M17_GLM_Spitzer_cube_data_2d.fits
            aplpy.make_rgb_cube([fname4, fname2, fname1],'M17_GLM_Spitzer_cube_data.fits')

3. Make RGB image 

            
              # Make RGB image - you can specify color parameters here!
              aplpy.make_rgb_image('M17_GLM_Spitzer_cube_data.fits','M17_GLM_Spitzer_cube_data.png',
                    make_nans_transparent=True) #,stretch_r='arcsinh',stretch_g='arcsinh',stretch_b='arcsinh')
              
              # Customize parameters
              aplpy.make_rgb_image('M17_GLM_Spitzer_cube_data.fits','M17_GLM_Spitzer_cube_data.png',
                                  stretch_r='arcsinh', vmid_r=0.01, stretch_g='arcsinh', vmid_g=0.06,
                                  stretch_b='arcsinh', vmid_b=0.06, make_nans_transparent=True)

4. Plot and save the RGB image 

            
              fig = plt.figure(figsize=(16, 12) )
        
              f = aplpy.FITSFigure('M17_GLM_Spitzer_cube_data_2d.fits',figure=fig)
              
              # Apply grayscale mapping of image
              #f.show_grayscale()
              #f.show_colorscale(cmap='rainbow')
              
              f.show_rgb('M17_GLM_Spitzer_cube_data.png')
              
              #f.tick_labels.set_font(size='xx-small')
              #f.axis_labels.set_font(size='x-small')
              f.ticks.hide()
              f.tick_labels.hide()
              f.axis_labels.hide()
              
              #f.axis_labels.show()
              #f.tick_labels.show()
              #f.tick_labels.show_x()
              #f.ticks.show()
              #f.ticks.show_x()
              f.frame.set_color('white')
              
              #f.axis_labels.set_xtext('Right Ascension [J2000]')
              #f.axis_labels.set_ytext('Declination [J2000]')
              
              plt.savefig('M17_spitzer_GLM_data.pdf', bbox_inches='tight')
              plt.savefig('M17_spitzer_GLM_data.png', dpi=800, bbox_inches='tight')

The output of the script


...Read Less