  In classical cosmological analysis of large scale structure surveys with 2-pt
functions, the parameter measurement precision is limited by several key
degeneracies within the cosmology and astrophysics sectors. For cosmic shear,
clustering amplitude $\sigma_8$ and matter density $\Omega_m$ roughly follow
the $S_8=\sigma_8(\Omega_m/0.3)^{0.5}$ relation. In turn, $S_8$ is highly
correlated with the intrinsic galaxy alignment amplitude $A_{\rm{IA}}$. For
galaxy clustering, the bias $b_g$ is degenerate with both $\sigma_8$ and
$\Omega_m$, as well as the stochasticity $r_g$. Moreover, the redshift
evolution of IA and bias can cause further parameter confusion. A tomographic
2-pt probe combination can partially lift these degeneracies. In this work we
demonstrate that a deep learning analysis of combined probes of weak
gravitational lensing and galaxy clustering, which we call DeepLSS, can
effectively break these degeneracies and yield significantly more precise
constraints on $\sigma_8$, $\Omega_m$, $A_{\rm{IA}}$, $b_g$, $r_g$, and IA
redshift evolution parameter $\eta_{\rm{IA}}$. The most significant gains are
in the IA sector: the precision of $A_{\rm{IA}}$ is increased by approximately
8x and is almost perfectly decorrelated from $S_8$. Galaxy bias $b_g$ is
improved by 1.5x, stochasticity $r_g$ by 3x, and the redshift evolution
$\eta_{\rm{IA}}$ and $\eta_b$ by 1.6x. Breaking these degeneracies leads to a
significant gain in constraining power for $\sigma_8$ and $\Omega_m$, with the
figure of merit improved by 15x. We give an intuitive explanation for the
origin of this information gain using sensitivity maps. These results indicate
that the fully numerical, map-based forward modeling approach to cosmological
inference with machine learning may play an important role in upcoming LSS
surveys. We discuss perspectives and challenges in its practical deployment for
a full survey analysis.
