  In this work, we test the robustness of the constancy of the Supernova
absolute magnitude $M_B$ using Non-parametric Reconstruction Techniques (NRT).
We isolate the luminosity distance parameter $d_L(z)$ from the Baryon Acoustic
Oscillations (BAO) data set and cancel the expansion part from the observed
distance modulus $\mu(z)$. Consequently, the degeneracy between the absolute
magnitude and the Hubble constant $H_0$, is replaced by a degeneracy between
$M_B$ and the sound horizon at drag epoch $r_d$. When imposing the $r_d$ value,
this yields the $M_B(z) = M_B + \delta M_B(z)$ value from NRT. We perform the
respective reconstructions using the model independent Artificial Neural
Network (ANN) technique and Gaussian processes (GP) regression. For the ANN we
infer $M_B = -19.22\pm0.20$, and for the GP we get $M_B = -19.25\pm0.39$ as a
mean for the full distribution when using the sound horizon from late time
measurements. These estimations provide a $1\,\sigma$ possibility of a nuisance
parameter presence $\delta M_B(z)$ at higher redshifts. We also tested
different known nuisance models with the Markov Chain Monte Carlo (MCMC)
technique which showed a strong preference for the constant model, but it was
not possible not single out a best fit nuisance model.
