  Stochastic resetting has recently emerged as an efficient target-searching
strategy in various physical and biological systems. The efficiency of this
strategy depends on the type of environmental noise, whether it is thermal or
telegraphic (active). While the impact of each noise type on a search process
has been investigated separately, their combined effects have not been
explored. In this work, we explore the effects of stochastic resetting on an
active system, namely a self-propelled run-and-tumble particle immersed in a
thermal bath. In particular, we assume that the position of the particle is
reset at a fixed rate with or without reversing the direction of self-propelled
velocity. Using standard renewal techniques, we compute the mean search time of
this active particle to a fixed target and investigate the interplay between
active and thermal fluctuations. We find that the active search can outperform
the Brownian search when the magnitude and flipping rate of self-propelled
velocity are large and the strength of environmental noise is small. Notably,
we find that the presence of thermal noise in the environment helps reduce the
mean first passage time of the run-and-tumble particle compared to the absence
of thermal noise. Finally, we observe that reversing the direction of
self-propelled velocity while resetting can also reduce the overall search
time.
