  The objective of the Cyclotron Radiation Emission Spectroscopy (CRES)
technology is to build precise particle energy spectra. This is achieved by
identifying the start frequencies of charged particle trajectories which, when
exposed to an external magnetic field, leave semi-linear profiles (called
tracks) in the time-frequency plane. Due to the need for excellent instrumental
energy resolution in application, highly efficient and accurate track
reconstruction methods are desired. Deep learning convolutional neural networks
(CNNs) - particularly suited to deal with information-sparse data and which
offer precise foreground localization - may be utilized to extract track
properties from measured CRES signals (called events) with relative
computational ease. In this work, we develop a novel machine learning based
model which operates a CNN and a support vector machine in tandem to perform
this reconstruction. A primary application of our method is shown on simulated
CRES signals which mimic those of the Project 8 experiment - a novel effort to
extract the unknown absolute neutrino mass value from a precise measurement of
tritium $\beta^-$-decay energy spectrum. When compared to a point-clustering
based technique used as a baseline, we show a relative gain of 24.1% in event
reconstruction efficiency and comparable performance in accuracy of track
parameter reconstruction.
