# MAP estimation

Here, we give an example of how to compute the joint maximum a posteriori (MAP) estimate of the CMB temperature and polarization fields, $f$, and the lensing potential, $\phi$.

`using CMBLensing, PyPlot`

## Compute spectra

First, we compute the fiducial CMB power spectra which generate our simulated data,

`Cℓ = camb(r=0.05);`

Next, we chose the noise power-spectra:

`Cℓn = noiseCℓs(μKarcminT=1, ℓknee=100);`

Plot these up for reference,

```
loglog(Cℓ.total.BB,c="C0")
loglog(Cℓ.unlensed_total.BB,"--",c="C0")
loglog(Cℓ.total.EE,c="C1")
loglog(Cℓ.unlensed_total.EE,"--",c="C1")
loglog(Cℓn.BB,"k:")
legend(["lensed B","unlensed B","lensed E","unlensed E", "noise (beam not deconvolved)"]);
```

## Configure the type of data

These describe the setup of the simulated data we are going to work with (and can be changed in this notebook),

```
θpix = 3 # pixel size in arcmin
Nside = 128 # number of pixels per side in the map
pol = :P # type of data to use (can be :T, :P, or :TP)
T = Float32 # data type (Float32 is ~2 as fast as Float64);
```

## Generate simulated data

With these defined, the following generates the simulated data and returns the true unlensed and lensed CMB fields, `f`

and `f̃`

,and the true lensing potential, `ϕ`

, as well as a number of other quantities stored in the "DataSet" object `ds`

.

```
@unpack f, f̃, ϕ, ds = load_sim(
seed = 3,
Cℓ = Cℓ,
Cℓn = Cℓn,
θpix = θpix,
T = T,
Nside = Nside,
pol = pol,
)
@unpack Cf, Cϕ = ds;
```

## Examine simulated data

The true $\phi$ map,

`plot(ϕ, title = raw"true $\phi$");`

The "true" unlensed field, $f$,

`plot(f, title = "true unlensed " .* ["E" "B"]);`

And the "true" lensed field,

`plot(LenseFlow(ϕ)*f, title = "true lensed " .* ["E" "B"]);`

The data (stored in the `ds`

object) is basically `f̃`

with a beam applied plus a sample of the noise,

`plot(ds.d, title = "data " .* ["E" "B"]);`

# Run the optimizer

Now we compute the maximum of the joint posterior, $\mathcal{P}\big(f, \phi \,\big|\,d\big)$

`fJ, ϕJ, history = MAP_joint(ds, nsteps=30, progress=true);`

```
[32mMAP_joint: 100%|████████████████████████████████████████| Time: 0:08:30[39m
[34m step: 30[39m
[34m χ²: 32947.0[39m
[34m α: 0.09358431[39m
[34m CG: 2 iterations[39m
[34m Linesearch: 30 bisections[39m
```

# Examine results

The expected value of the final best-fit $\chi^2 (=-2\log \mathcal{P}$) is given by the number degrees of freedom in the data, i.e. the total number of pixels in T and/or EB.

`χ² = -2history[end][:lnP]`

`32947.0f0`

`dof = length(Map(f)[:])`

`32768`

Here's how far away our final $\chi^2$ is from this expectation, in units of $\sigma$. We expect this should be somewhere in the range (-3,3) for about 99.7% of simulated datasets.

`(χ² - dof)/sqrt(2dof)`

`0.69921875`

Here's the best-fit $\phi$ relative to the truth,

`plot(10^6*[ϕ ϕJ], title=["true" "best-fit"] .* raw" $\phi$", vlim=17);`

Here is the difference in terms of the power spectra. Note the best-fit has high-$\ell$ power suppressed, like a Wiener filter solution (in fact what we're doing here is akin to a non-linear Wiener filter). In the high S/N region ($\ell\lesssim1000$), the difference is approixmately equal to the noise, which you can see is almost two orders of magnitude below the signal.

```
loglog(ℓ⁴ * Cℓ.total.ϕϕ, "k")
loglog(get_ℓ⁴Cℓ(ϕ))
loglog(get_ℓ⁴Cℓ(ϕJ))
loglog(get_ℓ⁴Cℓ(ϕJ-ϕ))
xlim(80,3000)
ylim(5e-9,2e-6)
legend(["theory",raw"true $\phi$", raw"best-fit $\phi$", "difference"])
xlabel(raw"$\ell$")
ylabel(raw"$\ell^4 C_\ell$");
```

The best-fit unlensed fields relative to truth,

`plot([f,fJ], title = ["true", "best-fit"] .* " unlensed " .* ["E" "B"]);`

The best-fit lensed field (bottom row) relative to truth (top row),

`plot([f̃, LenseFlow(ϕJ)*fJ], title = ["true", "best-fit"] .* " lensed " .* ["E" "B"]);`