  The ARIANNA experiment is an Askaryan detector designed to record radio
signals induced by neutrino interactions in the Antarctic ice. Because of the
low neutrino flux at high energies ($E > 10^{16} $), the physics output is
limited by statistics. Hence, an increase in sensitivity significantly improves
the interpretation of data and offers the ability to probe new parameter
spaces. The amplitudes of the trigger threshold are limited by the rate of
triggering on unavoidable thermal noise fluctuations. We present a real-time
thermal noise rejection algorithm that enables the trigger thresholds to be
lowered, which increases the sensitivity to neutrinos by up to a factor of two
(depending on energy) compared to the current ARIANNA capabilities. A deep
learning discriminator, based on a Convolutional Neural Network (CNN), is
implemented to identify and remove thermal events in real time. We describe a
CNN trained on MC data that runs on the current ARIANNA microcomputer and
retains 95 percent of the neutrino signal at a thermal noise rejection factor
of $10^5$, compared to a template matching procedure which reaches only $10^2$
for the same signal efficiency. Then the results are verified in a lab
measurement by feeding in generated neutrino-like signal pulses and thermal
noise directly into the ARIANNA data acquisition system. Lastly, the same CNN
is used to classify cosmic-rays events to make sure they are not rejected. The
network classified 102 out of 104 cosmic-ray events as signal.
