  The complex physics involved in atmospheric turbulence makes it very
difficult for ground-based astronomy to build accurate scintillation models and
develop efficient methodologies to remove this highly structured noise from
valuable astronomical observations. We argue that a Deep Learning approach can
bring a significant advance to treat this problem because of deep neural
networks' inherent ability to abstract non-linear patterns over a broad scale
range. We propose an architecture composed of long-short term memory cells and
an incremental training strategy inspired by transfer and curriculum learning.
We develop a scintillation model and employ an empirical method to generate a
vast catalog of atmospheric noise realizations and train the network with
representative data. We face two complexity axes: the signal-to-noise ratio
(SNR) and the degree of structure in the noise. Hence, we train our recurrent
network to recognize simulated astrophysical point-like sources embedded in
three structured noise levels, with a raw-data SNR ranging from 3 to 0.1. We
find that a slow and repetitive increase in complexity is crucial during
training to obtain a robust and stable learning rate that can transfer
information through different data contexts. We probe our recurrent model with
synthetic observational data, designing alongside a calibration methodology for
flux measurements. Furthermore, we implement a traditional matched filtering
(MF) to compare its performance with our neural network, finding that our final
trained network can successfully clean structured noise and significantly
enhance the SNR compared to raw data and in a more robust way than traditional
MF.
