  To simulate the heated exterior of a meteorite, we impact a granular bed with
melted tin. The morphology of tin remnant and crater is found to be sensitive
to the temperature and solidification of tin. By employing deep learning and
convolutional neural network, we can quantify and map the complex impact
patterns onto network systems based on feature maps and Grad-CAM results. This
gives us unprecedented details on how the projectile deforms and interacts with
the granules, which information can be used to trace the development of
different remnant shapes. Furthermore, full dynamics of granular system is
revealed by the use of Particle Image Velocimetry. Kinetic energy, temperature
and diameter of the projectile are used to build phase diagrams for the
morphology of both crater and tin remnant. In addition to successfully
reproducing key features of simple and complex craters, we are able to detect a
possible artifact when compiling crater data from field studies. The depth of
craters from high-energy impacts in our work is found to be independent of
their width. However, when mixing data from different energy, temperature and
diameter of projectile, a bogus power-law relationship appears between them.
Like other controlled laboratory researches, our conclusions have the potential
to benefit the study of paint in industry and asteroid sampling missions on the
surface of celestial bodies.
