  We apply deep learning techniques to the late-time turbulent regime in a
post-inflationary model where a real scalar inflaton field and the standard
model Higgs doublet interact with renormalizable couplings between them. After
inflation, the inflaton decays into the Higgs through a trilinear coupling and
the Higgs field subsequently thermalizes with gauge bosons via its $SU(2)\times
U(1)$ gauge interaction. Depending on the strength of the trilinear interaction
and the Higgs self-coupling, the effective mass squared of Higgs can become
negative, leading to the tachyonic production of Higgs particles. These
produced Higgs particles would then share their energy with gauge bosons,
potentially indicating thermalization. Since the model entails different
non-perturbative effects, it is necessary to resort to numerical and
semi-classical techniques. However, simulations require significant costs in
terms of time and computational resources depending on the model used.
Particularly, when $SU(2)$ gauge interactions are introduced, this becomes
evident as the gauge field redistributes particle energies through rescattering
processes, leading to an abundance of UV modes that disrupt simulation
stability. This necessitates very small lattice spacings, resulting in
exceedingly long simulation runtimes. Furthermore, the late-time behavior of
preheating dynamics exhibits a universal form by wave kinetic theory.
Therefore, we analyze patterns in the flow of particle numbers and predict
future behavior using CNN-LSTM (Convolutional Neural Network combined with Long
Short-Term Memory) time series analysis. In this way, we can reduce our
dependence on simulations by orders of magnitude in terms of time and
computational resources.
