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Clone the directory and from within it, run:

$ python marginalized_tsz_from_TS+P18CM_analysis.py


The 'Marginalized TSZ' points (black dots with error bars) are obtained by
subtracting the foreground residuals from the y-map power spectrum measurement.

In Figure 12, we chose to subtract the foreground as obtained in the TSZ-only analysis (default):
TSZ only ACIB: 7.54722e-02 +/- 5.99398e-02
TSZ only AIR: 1.80183e+00 +/- 4.03783e-01
TSZ only ARS: 2.13806e-01 +/- 1.96956e-01

One could chose to subtract the foreground as obtained in the TSZ+Planck analysis:
TSZ+P18CMB ACIB: 4.5620e-02 +/- 2.8713e-02
TSZ+P18CMB AIR: 1.5310e+00 +/- 1.0807e-01
TSZ+P18CMB ARS: 1.8398e-01 +/- 1.0711e-01
to chose this option, run:

$ python marginalized_tsz_from_TS+P18CM_analysis.py -fg_from_P18CMB yes

Here we also compare with the high-ell measurements with recent ACT and SPT points.
These are given by:

ACT result from Choi et al 2020 (https://arxiv.org/pdf/2007.07289.pdf)
They report a tSZ power result at 150 GHz, where g(nu) = -0.957
ACTCellnew = 5.29/(2.67)**2
ACTCellnewerr =	0.66/(2.67)**2


SPT result from Reichardt et al 2020 (https://arxiv.org/pdf/2002.06197.pdf)
They report a tSZ power result at 143 GHz, where g(nu) = -1.044
SPTCellnew = 3.42/(2.84)**2
SPTCellnewerr = 0.54/(2.84)**2


The denominator comes from the conversion to dimensionless y-units,
i.e., dividing by Tcmb*g(nu).


The best-fitting parameter values are:
A_CIB: 0.0047084
A_IR: 1.4864
A_RS: 0.18704
B: 1.1979
H0: 66.893
n_s: 0.96426
omega_b: 0.022218
omega_cdm: 0.12105

The correlated noise amplitude is fixed to 0.9033 (set by high-ell power, see Bolliet++18)

The other parameters and settings can be found in the file sz_input_evaluate_bf_p18_cmb.yaml

The files:
- szpowespectrum_measurement_urc_snr6_p18cmb_bf_fg_from_TSZ+P18_l_clyy_sigclyy_cib_ir_rs_cn.txt
- szpowespectrum_measurement_urc_snr6_p18cmb_bf_fg_from_TSZonly_l_clyy_sigclyy_cib_ir_rs_cn.txt
contain the 10^12*l*(l+1)/2pi*cl's in dimensionless DT/T units. The columns are as indicated in
the end of the file names. The foreground curves are the same, computed with the best-fitting values,
only the marginalised tSZ differs, as explained above.
The error 'sigcllyy' includes the diagonal element of the covariance matrix, including
trispectrum contribution.

The file:
- tSZ_trispectrum_urc_snr6_sz+p18cmb_bf.txt
is the trispectrum, so that the covmat is given by: covmat = trispectrum/f_sky/4/pi+ gaussian_part
This trispectrum is computed for the best-fitting parameters of the TSZ+P18CMB analysis.


The file:
- szpowespectrum_measurement_urc_snr6_p18cmb_best_fit_curve_l_cl1h_cl2h_cl1h+2h.txt
contains the best-fitting TSZ power spectrum, columns are as indicated at the end of the file name and
the spectra are 10^12*l*(l+1)/2pi*cl's in dimensionless DT/T units, for 1-halo, 2-halo and the sum,
from l=2 to 40,000.

We provide a python script that produces the figure from all these data files.
You can obtain a version of the figure by running:
$ python marginalized_tsz_from_TS+P18CM_analysis.py
in a Terminal.