The extremely low flux of ultra-high energy cosmic rays
(UHECR) makes their direct observation by orbital experiments
practically impossible. For this reason all current and planning UHECR
experiments detect cosmic rays indirectly observing extensive air
showers (EAS) initiated by cosmic ray particles in the atmosphere.
Various types of shower observables are analysed in modern UHECR
experiments including secondary radio signal and fluorescent light from
excited nitrogen molecules. The most of data is collected by the network
of surface area detectors which allows to measure horizontal EAS profile
directly. The raw observables in this case are the time-resolved signals
for the set of adjacent triggered detectors. To recover primary particle
properties Monte Carlo shower simulation is performed. In traditional
techniques the MC simulation is used to fit some synthetic observable such
as shower rise time, shower front curvature and particle density normalized
to a given distance from the core. In this talk we consider an alternative
approach based on the deep convolutional neural network using detector
signal time series as an input and trained on a large Monte-Carlo dataset.
The above approach has proven its efficiency with the Monte-Carlo simulations
of the Telescope Array Observatory surface detector. We will discuss in detail how we optimize network architecture for the particular task.