SPT-SZ likelihoods

from cobaya_utilities import plots, tools

set_style(use_seaborn=True)

This notebook makes use of GetDist python package to plot and to analyse MCMC samples.

from cobaya_utilities.plots import (
    despine,
    get_default_settings,
    get_mc_samples,
    show_inputs,
)
from cobaya_utilities.tools import (
    plot_chains,
    plot_progress,
    print_chains_size,
    print_results,
)
from getdist.plots import get_subplot_plotter

Definitions

Define CMB & nuisance parameter names

cosmo_params = ["cosmomc_theta", "logA", "ns", "ombh2", "omch2", "H0", "tau"]

calibration_params = [
    "mapCal95",
    "mapCal150",
    "mapCal220",
    "FTS_calibration_error",
]
fg_params = [
    "czero_tsz",
    "czero_ksz",
    "tsz_dg_cor",
    "czero_dg_po",
    "czero_dg_cl",
    "czero_dg_cl2",
    "czero_cirrus",
]
sptsz_params = (
    calibration_params
    + fg_params
    + [
        "T_dg_po",
        "beta_dg_po",
        "sigmasq_dg_po",
        "beta_dg_cl",
        "beta_dg_cl2",
        "alpha_rg",
    ]
)

# Official SPT-SZ results from https://arxiv.org/pdf/2002.06197.pdf
reichardt_results = dict(
    czero_tsz=(3.42, 0.54),
    czero_ksz=(3.0, 1.0),
    tsz_dg_cor=(0.076, 0.040),
    czero_dg_po=(7.24, 0.63),
    czero_dg_cl=(2.21, 0.88),
    czero_dg_cl2=(1.82, 0.31),
    beta_dg_po=(1.48, 0.13),
    beta_dg_cl=(2.23, 0.18),
    czero_rg_po=(1.01, 0.17),
    alpha_rg=(-0.76, 0.15),
)

MCMC chains

Set a dictionnary holding the path to the MCMC chains and its name

mcmc_samples = {
    "SPT-SZ (fixed cosmology)": "../output/spt_hiell",
    "SPT-SZ (fixed cosmology) à la Douspis": "../output/spt_douspis",
}

Let's plot the chains size

print_chains_size(mcmc_samples, with_bar=True)

	mcmc 1				mcmc 2				mcmc 3				mcmc 4				all mcmc
	accept.	total	rate	R-1	accept.	total	rate	R-1	accept.	total	rate	R-1	accept.	total	rate	R-1	accept.	total	rate
SPT-SZ (fixed cosmology)	168809	833231	20.3%	0.01	149590	747882	20.0%	0.01	185792	927179	20.0%	0.01	197318	985299	20.0%	0.01	701509	3493591	20.1%
SPT-SZ (fixed cosmology) à la Douspis	135614	658751	20.6%	0.01	208083	1029793	20.2%	0.01	272709	1368134	19.9%	0.01	175135	882241	19.9%	0.01	791541	3938919	20.1%

and let's have a look at how chains evolve with time and the resulting convergence criteria

plot_chains(mcmc_samples, params=cosmo_params + sptsz_params, ncol=4, ignore_rows=0.4);

plot_progress(mcmc_samples);

MCMC distributions

SPT-SZ + Planck 2018 likelihood

ΛCDM parameters

sample, label, _ = get_mc_samples(
    {"SPT-SZ + Planck high-$\ell$ TT, TE, EE": "../output/spt_hiell+planck18"}, as_dict=False
)
g = get_subplot_plotter(settings=get_default_settings())
g.triangle_plot(
    sample,
    cosmo_params,
    filled=True,
    legend_labels=label,
)
despine(g)

# Official Planck 2018 results from https://arxiv.org/pdf/1807.06209.pdf
p18_results = dict(
    cosmomc_theta=(0.0104090, 0.0000031),
    logA=(3.045, 0.016),
    ns=(0.9649, 0.0044),
    ombh2=(0.02236, 0.00015),
    omch2=(0.1202, 0.0014),
    H0=(67.26, 0.60),
    tau=(0.0544, 0.0073),
)

show_inputs(g, inputs=p18_results, color="gray")

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df = print_results(sample, cosmo_params, labels=label)
df.loc["Planck 2018 results"] = [
    r"${} \pm {:f}$".format(*p18_results.get(par)) for par in cosmo_params
]
df

	$\theta_\mathrm{MC}$	$\log(10^{10} A_\mathrm{s})$	$n_\mathrm{s}$	$\Omega_\mathrm{b}h^2$	$\Omega_\mathrm{c}h^2$	$H_0$	$\tau_\mathrm{reio}$
SPT-SZ + Planck high-$\ell$ TT, TE, EE	$ 0.0104080\pm 0.0000031$	$ 3.043\pm 0.016$	$ 0.9591\pm 0.0041$	$ 0.02222\pm 0.00015$	$ 0.1212\pm 0.0013$	$ 66.77\pm 0.58$	$ 0.0559\pm 0.0076$
Planck 2018 results	$0.010409 \pm 0.000003$	$3.045 \pm 0.016000$	$0.9649 \pm 0.004400$	$0.02236 \pm 0.000150$	$0.1202 \pm 0.001400$	$67.26 \pm 0.600000$	$0.0544 \pm 0.007300$

Foreground posterior distributions

planck_nuisance_params = [
    "A_planck",
    "calib_100T",
    "calib_217T",
    "A_cib_217",
    "xi_sz_cib",
    "A_sz",
    "ksz_norm",
    "gal545_A_100",
    "gal545_A_143",
    "gal545_A_143_217",
    "gal545_A_217",
    "ps_A_100_100",
    "ps_A_143_143",
    "ps_A_143_217",
    "ps_A_217_217",
    "galf_TE_A_100",
    "galf_TE_A_100_143",
    "galf_TE_A_100_217",
    "galf_TE_A_143",
    "galf_TE_A_143_217",
    "galf_TE_A_217",
]

g = get_subplot_plotter(settings=get_default_settings(), width_inch=15)
g.plots_1d(sample, planck_nuisance_params, nx=7, legend_labels=[])
despine(g, all_axes=True)

Comparisons between SPT-SZ likelihoods implementations: cobaya vs. cosmomc

Load MCMC samples

samples, kwargs = get_mc_samples(mcmc_samples)

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from getdist.plots import get_subplot_plotter

colors = ["red", "blue"]

g = get_subplot_plotter(settings=get_default_settings())
g.triangle_plot(
    samples,
    fg_params,
    filled=True, 
    legend_labels=(labels := kwargs["legend_labels"]),
    colors=colors,
    diag1d_kwargs={"colors": colors},
)
despine(g)

show_inputs(g, inputs=reichardt_results, color="gray")

print_results(samples, sptsz_params, labels=labels)

	$cal^\mathrm{95}$	$cal^\mathrm{150}$	$cal^\mathrm{220}$	$\sigma(FTS)$	$D_{3000}^{tSZ}$	$D_{3000}^{kSZ}$	$\xi^{tSZxCIB}$	$D_{3000}^{DSFG-p}$	$D_{3000}^{DSFG-1h}$	$D_{3000}^{DSFG-2h}$	$D_{3000}^{cirrus}$	$T^{DSFG-p}$	$\beta^{DSFG-p}$	$\sigma^{DSFG-p}$	$\beta^{DSFG-1h}$	$\beta^{DSFG-2h}$	$\alpha^{RG}$
SPT-SZ (fixed cosmology)	$ 0.9939\pm 0.0027$	$ 0.9973\pm 0.0017$	$ 0.9988\pm 0.0041$	$ -0.01\pm 0.30$	$ 3.58^{+0.56}_{-0.51}$	$ 2.3\pm 1.0$	$ 0.066^{+0.035}_{-0.043}$	$ 7.24^{+0.75}_{-0.62}$	$ 2.52^{+0.90}_{-1.1}$	$ 1.94^{+0.28}_{-0.39}$	$ 1.90\pm 0.38$	$-$	$ 1.42^{+0.18}_{-0.20}$	$< 0.476$	$ 2.14\pm 0.19$	$-$	$ -0.84^{+0.21}_{-0.17}$
SPT-SZ (fixed cosmology) à la Douspis	$ 0.9938\pm 0.0027$	$ 0.9972\pm 0.0017$	$ 0.9987\pm 0.0042$	$ -0.01\pm 0.30$	$ 3.57\pm 0.53$	$ 2.3\pm 1.1$	$ 0.067^{+0.035}_{-0.044}$	$ 7.22^{+0.75}_{-0.63}$	$ 2.55^{+0.89}_{-1.1}$	$ 1.94^{+0.29}_{-0.39}$	$ 1.89\pm 0.38$	$-$	$ 1.42\pm 0.19$	$< 0.484$	$ 2.14^{+0.20}_{-0.17}$	$-$	$ -0.83^{+0.21}_{-0.17}$

g = get_subplot_plotter(settings=get_default_settings(), width_inch=6)
g.plot_2d(samples, "czero_tsz", "czero_ksz", filled=True, colors=colors)
g.add_x_marker(reichardt_results["czero_tsz"][0])
g.add_y_marker(reichardt_results["czero_ksz"][0])
g.add_legend(labels)
g.subplots[0, 0].set(xlim=(0, 6), ylim=(0, 7));

samples, _ = get_mc_samples(
    {
        "mathieu's run": "../output/spt_reichardt_HMcode",
        "adelie's run": dict(path="../output/cosmomc_ias/chains", prefix="test_spt_hiell_baseline"),
    }
)

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from getdist.plots import get_subplot_plotter

g = get_subplot_plotter(settings=get_default_settings(), width_inch=15)
sptsz_params = [
    par for par in sptsz_params if par not in ["T_dg_po", "sigmasq_dg_po", "beta_dg_cl2"]
]
g.plots_1d(samples, sptsz_params, nx=5, colors=colors, legend_labels=[])

show_inputs(g, inputs=reichardt_results, color="0.5")
despine(g, all_axes=True)

ax = g.subplots[0, -1]
ax.plot([], [], "--", color="0.5")
ax.legend(
    legend_labels := [
        "cobaya - SPT-SZ (M. Tristram)",
        "cosmomc - SPT-SZ (A. Gorce)",
        "published values",
    ],
    **(legend_kwargs := dict(loc="upper left", bbox_to_anchor=(1, 1), labelcolor="mfc")),
);

print_results(samples, sptsz_params, labels=legend_labels)

	$cal^\mathrm{95}$	$cal^\mathrm{150}$	$cal^\mathrm{220}$	$\sigma(FTS)$	$D_{3000}^{tSZ}$	$D_{3000}^{kSZ}$	$\xi^{tSZxCIB}$	$D_{3000}^{DSFG-p}$	$D_{3000}^{DSFG-1h}$	$D_{3000}^{DSFG-2h}$	$D_{3000}^{cirrus}$	$\beta^{DSFG-p}$	$\beta^{DSFG-1h}$	$\alpha^{RG}$
cobaya - SPT-SZ (M. Tristram)	$ 0.9937\pm 0.0027$	$ 0.9971\pm 0.0017$	$ 0.9987\pm 0.0042$	$ 0.00\pm 0.30$	$ 3.48\pm 0.53$	$ 2.6\pm 1.0$	$ 0.072^{+0.034}_{-0.043}$	$ 7.27^{+0.74}_{-0.60}$	$ 2.43^{+0.85}_{-1.0}$	$ 1.89^{+0.27}_{-0.36}$	$ 1.89\pm 0.39$	$ 1.43^{+0.15}_{-0.12}$	$ 2.18^{+0.18}_{-0.16}$	$ -0.79^{+0.18}_{-0.16}$
cosmomc - SPT-SZ (A. Gorce)	$ 0.9933\pm 0.0027$	$ 0.9969\pm 0.0017$	$ 0.9986\pm 0.0042$	$ -0.01\pm 0.30$	$ 3.40\pm 0.55$	$ 3.1\pm 1.1$	$ 0.073^{+0.034}_{-0.043}$	$ 7.23^{+0.72}_{-0.58}$	$ 2.35^{+0.83}_{-0.98}$	$ 1.83^{+0.25}_{-0.34}$	$ 1.90\pm 0.38$	$ 1.47^{+0.15}_{-0.12}$	$ 2.24\pm 0.18$	$ -0.76^{+0.18}_{-0.16}$

SPT-SZ results for different lens_potential_accuracy levels

mcmc_samples = {f"SPT-SZ (accuracy = {acc})": f"../output/spt_accuracy_{acc}" for acc in range(9)}

print_chains_size(mcmc_samples, with_bar=True)

	mcmc 1				mcmc 2				mcmc 3				mcmc 4				all mcmc
	accept.	total	rate	R-1	accept.	total	rate	R-1	accept.	total	rate	R-1	accept.	total	rate	R-1	accept.	total	rate
SPT-SZ (accuracy = 0)	69462	270117	25.7%	0.01	88086	343563	25.6%	0.01	138001	550227	25.1%	0.01	129414	509703	25.4%	0.01	424963	1673610	25.4%
SPT-SZ (accuracy = 1)	47022	185098	25.4%	0.01	69766	272202	25.6%	0.01	141093	557485	25.3%	0.01	136679	532842	25.7%	0.01	394560	1547627	25.5%
SPT-SZ (accuracy = 2)	96049	383348	25.1%	0.01	110887	441050	25.1%	0.01	52756	209268	25.2%	0.01	69911	273378	25.6%	0.01	329603	1307044	25.2%
SPT-SZ (accuracy = 3)	94191	372905	25.3%	0.01	89384	350654	25.5%	0.01	83745	323516	25.9%	0.01	73068	286846	25.5%	0.01	340388	1333921	25.5%
SPT-SZ (accuracy = 4)	56848	221078	25.7%	0.01	103146	400156	25.8%	0.01	70551	277029	25.5%	0.01	101011	390218	25.9%	0.01	331556	1288481	25.7%
SPT-SZ (accuracy = 5)	70630	274278	25.8%	0.01	74246	290044	25.6%	0.01	58712	229615	25.6%	0.01	84892	335279	25.3%	0.01	288480	1129216	25.5%
SPT-SZ (accuracy = 6)	78024	302631	25.8%	0.01	88251	341409	25.8%	0.01	100818	391968	25.7%	0.01	88158	341587	25.8%	0.01	355251	1377595	25.8%
SPT-SZ (accuracy = 7)	101155	393175	25.7%	0.01	89265	349459	25.5%	0.01	123309	484087	25.5%	0.01	79706	310170	25.7%	0.01	393435	1536891	25.6%
SPT-SZ (accuracy = 8)	112810	455056	24.8%	0.01	80063	308399	26.0%	0.01	84766	330903	25.6%	0.01	117011	454472	25.7%	0.01	394650	1548830	25.5%

plot_progress(mcmc_samples);

samples, kwargs = get_mc_samples(mcmc_samples)

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g = get_subplot_plotter(settings=get_default_settings(), width_inch=15)
g.plots_1d(samples, sptsz_params, nx=5, legend_labels=[])

show_inputs(g, inputs=reichardt_results, color="0.5")
despine(g, all_axes=True)

ax = g.subplots[0, -1]
ax.plot([], [], "--", color="0.5")
ax.legend(legend_labels := kwargs["legend_labels"] + ["published values"], **legend_kwargs);

print_results(samples, sptsz_params, labels=legend_labels)

	$cal^\mathrm{95}$	$cal^\mathrm{150}$	$cal^\mathrm{220}$	$\sigma(FTS)$	$D_{3000}^{tSZ}$	$D_{3000}^{kSZ}$	$\xi^{tSZxCIB}$	$D_{3000}^{DSFG-p}$	$D_{3000}^{DSFG-1h}$	$D_{3000}^{DSFG-2h}$	$D_{3000}^{cirrus}$	$\beta^{DSFG-p}$	$\beta^{DSFG-1h}$	$\alpha^{RG}$
SPT-SZ (accuracy = 0)	$ 0.9938\pm 0.0027$	$ 0.9971\pm 0.0017$	$ 0.9987\pm 0.0042$	$ -0.01\pm 0.30$	$ 3.48\pm 0.54$	$ 3.3\pm 1.1$	$ 0.070^{+0.034}_{-0.042}$	$ 7.31^{+0.74}_{-0.59}$	$ 2.26^{+0.84}_{-1.0}$	$ 1.95^{+0.28}_{-0.39}$	$ 1.89\pm 0.39$	$ 1.46^{+0.15}_{-0.12}$	$ 2.18^{+0.19}_{-0.17}$	$ -0.82^{+0.18}_{-0.16}$
SPT-SZ (accuracy = 1)	$ 0.9936\pm 0.0027$	$ 0.9971\pm 0.0017$	$ 0.9987\pm 0.0042$	$ -0.01\pm 0.30$	$ 3.49\pm 0.53$	$ 2.5\pm 1.0$	$ 0.072^{+0.034}_{-0.043}$	$ 7.26^{+0.75}_{-0.61}$	$ 2.44^{+0.86}_{-1.0}$	$ 1.89^{+0.27}_{-0.37}$	$ 1.89\pm 0.38$	$ 1.43^{+0.15}_{-0.13}$	$ 2.18^{+0.18}_{-0.16}$	$ -0.79^{+0.18}_{-0.16}$
SPT-SZ (accuracy = 2)	$ 0.9936\pm 0.0028$	$ 0.9971\pm 0.0017$	$ 0.9987\pm 0.0042$	$ -0.01\pm 0.30$	$ 3.50\pm 0.53$	$ 2.5\pm 1.0$	$ 0.071^{+0.034}_{-0.042}$	$ 7.26^{+0.74}_{-0.61}$	$ 2.44^{+0.85}_{-1.0}$	$ 1.89^{+0.28}_{-0.36}$	$ 1.89\pm 0.39$	$ 1.43^{+0.15}_{-0.12}$	$ 2.18^{+0.18}_{-0.16}$	$ -0.79^{+0.18}_{-0.16}$
SPT-SZ (accuracy = 3)	$ 0.9937\pm 0.0028$	$ 0.9971\pm 0.0017$	$ 0.9987\pm 0.0042$	$ 0.00\pm 0.30$	$ 3.49\pm 0.53$	$ 2.5\pm 1.0$	$ 0.071^{+0.035}_{-0.042}$	$ 7.26^{+0.74}_{-0.61}$	$ 2.42^{+0.87}_{-1.0}$	$ 1.89^{+0.28}_{-0.36}$	$ 1.90\pm 0.38$	$ 1.43^{+0.15}_{-0.12}$	$ 2.18^{+0.18}_{-0.16}$	$ -0.80^{+0.18}_{-0.16}$
SPT-SZ (accuracy = 4)	$ 0.9937\pm 0.0027$	$ 0.9971\pm 0.0017$	$ 0.9987\pm 0.0041$	$ 0.00\pm 0.30$	$ 3.50\pm 0.53$	$ 2.5\pm 1.0$	$ 0.070^{+0.034}_{-0.042}$	$ 7.26^{+0.73}_{-0.60}$	$ 2.41^{+0.86}_{-0.98}$	$ 1.90^{+0.27}_{-0.37}$	$ 1.89\pm 0.39$	$ 1.43^{+0.15}_{-0.12}$	$ 2.18^{+0.18}_{-0.16}$	$ -0.79^{+0.18}_{-0.16}$
SPT-SZ (accuracy = 5)	$ 0.9937\pm 0.0028$	$ 0.9971\pm 0.0017$	$ 0.9987\pm 0.0041$	$ -0.01\pm 0.30$	$ 3.49\pm 0.53$	$ 2.5\pm 1.0$	$ 0.072^{+0.034}_{-0.043}$	$ 7.26^{+0.74}_{-0.61}$	$ 2.43^{+0.87}_{-1.0}$	$ 1.90^{+0.27}_{-0.37}$	$ 1.89\pm 0.38$	$ 1.43^{+0.15}_{-0.12}$	$ 2.18^{+0.18}_{-0.16}$	$ -0.79\pm 0.17$
SPT-SZ (accuracy = 6)	$ 0.9937\pm 0.0027$	$ 0.9971\pm 0.0017$	$ 0.9988\pm 0.0042$	$ -0.01\pm 0.30$	$ 3.49\pm 0.53$	$ 2.5\pm 1.0$	$ 0.071^{+0.034}_{-0.042}$	$ 7.27^{+0.73}_{-0.62}$	$ 2.42^{+0.87}_{-1.0}$	$ 1.90^{+0.27}_{-0.37}$	$ 1.90\pm 0.39$	$ 1.43^{+0.15}_{-0.13}$	$ 2.18^{+0.18}_{-0.16}$	$ -0.79^{+0.18}_{-0.16}$
SPT-SZ (accuracy = 7)	$ 0.9937\pm 0.0027$	$ 0.9972\pm 0.0017$	$ 0.9987\pm 0.0041$	$ -0.01\pm 0.30$	$ 3.48\pm 0.53$	$ 2.5\pm 1.0$	$ 0.072^{+0.034}_{-0.042}$	$ 7.27^{+0.75}_{-0.60}$	$ 2.41^{+0.85}_{-1.0}$	$ 1.89^{+0.28}_{-0.36}$	$ 1.89\pm 0.38$	$ 1.43^{+0.15}_{-0.13}$	$ 2.18^{+0.18}_{-0.16}$	$ -0.79^{+0.18}_{-0.16}$
SPT-SZ (accuracy = 8)	$ 0.9937\pm 0.0027$	$ 0.9971\pm 0.0017$	$ 0.9987\pm 0.0042$	$ -0.01\pm 0.30$	$ 3.48\pm 0.54$	$ 2.5\pm 1.0$	$ 0.072^{+0.034}_{-0.043}$	$ 7.27^{+0.74}_{-0.61}$	$ 2.41^{+0.87}_{-1.0}$	$ 1.90^{+0.27}_{-0.37}$	$ 1.90\pm 0.38$	$ 1.43^{+0.15}_{-0.12}$	$ 2.18^{+0.18}_{-0.16}$	$ -0.79\pm 0.17$

SPT-SZ results with $C_\ell^\text{TT}$ from cosmomc

mcmc_samples = {
    "SPT-SZ cobaya": "../output/spt_accuracy_1",
    "SPT-SZ cobaya + $C_\ell^\mathrm{TT}$ from cosmomc": "../output/spt_cl_tt_from_cosmomc",
    "SPT-SZ cosmomc": dict(path="../output/cosmomc_ias/chains", prefix="test_spt_hiell_baseline"),
}

samples, labels, _ = get_mc_samples(mcmc_samples, as_dict=False)

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g = get_subplot_plotter(settings=get_default_settings(), width_inch=15)
g.plots_1d(samples, sptsz_params, nx=5, legend_labels=[])

show_inputs(g, inputs=reichardt_results, color="0.5")
despine(g, all_axes=True)

ax = g.subplots[0, -1]
ax.plot([], [], "--", color="0.5")
ax.legend(legend_labels := labels + ["published values"], **legend_kwargs);

print_results(samples, sptsz_params, labels=legend_labels)

	$cal^\mathrm{95}$	$cal^\mathrm{150}$	$cal^\mathrm{220}$	$\sigma(FTS)$	$D_{3000}^{tSZ}$	$D_{3000}^{kSZ}$	$\xi^{tSZxCIB}$	$D_{3000}^{DSFG-p}$	$D_{3000}^{DSFG-1h}$	$D_{3000}^{DSFG-2h}$	$D_{3000}^{cirrus}$	$\beta^{DSFG-p}$	$\beta^{DSFG-1h}$	$\alpha^{RG}$
SPT-SZ cobaya	$ 0.9936\pm 0.0027$	$ 0.9971\pm 0.0017$	$ 0.9987\pm 0.0042$	$ -0.01\pm 0.30$	$ 3.49\pm 0.53$	$ 2.5\pm 1.0$	$ 0.072^{+0.034}_{-0.043}$	$ 7.26^{+0.75}_{-0.61}$	$ 2.44^{+0.86}_{-1.0}$	$ 1.89^{+0.27}_{-0.37}$	$ 1.89\pm 0.38$	$ 1.43^{+0.15}_{-0.13}$	$ 2.18^{+0.18}_{-0.16}$	$ -0.79^{+0.18}_{-0.16}$
SPT-SZ cobaya + $C_\ell^\mathrm{TT}$ from cosmomc	$ 0.9934\pm 0.0027$	$ 0.9970\pm 0.0017$	$ 0.9987\pm 0.0041$	$ -0.01\pm 0.30$	$ 3.49\pm 0.54$	$ 2.9\pm 1.0$	$ 0.069^{+0.034}_{-0.042}$	$ 7.24^{+0.74}_{-0.61}$	$ 2.43^{+0.86}_{-1.0}$	$ 1.90^{+0.27}_{-0.36}$	$ 1.89\pm 0.38$	$ 1.44^{+0.15}_{-0.12}$	$ 2.18^{+0.18}_{-0.16}$	$ -0.81^{+0.18}_{-0.15}$
SPT-SZ cosmomc	$ 0.9933\pm 0.0027$	$ 0.9969\pm 0.0017$	$ 0.9986\pm 0.0042$	$ -0.01\pm 0.30$	$ 3.40\pm 0.55$	$ 3.1\pm 1.1$	$ 0.073^{+0.034}_{-0.043}$	$ 7.23^{+0.72}_{-0.58}$	$ 2.35^{+0.83}_{-0.98}$	$ 1.83^{+0.25}_{-0.34}$	$ 1.90\pm 0.38$	$ 1.47^{+0.15}_{-0.12}$	$ 2.24\pm 0.18$	$ -0.76^{+0.18}_{-0.16}$

version control - commit 223989c - 2023-10-26