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.