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In this thesis, Planck size black holes are discussed. Specifically, new families of black holes are presented. Such black holes exhibit an improved short scale behaviour and can be used to implement gravity self-complete paradigm. Such geometries are also studied within the ADD large extra dimensional scenario. This allows black hole remnant masses to reach the TeV scale. It is shown that the evaporation endpoint for this class of black holes is a cold stable remnant. One family of black holes considered in this thesis features a regular de Sitter core that counters gravitational collapse with a quantum outward pressure. The other family of black holes turns out to nicely fit into the holographic information bound on black holes, and lead to black hole area quantization and applications in the gravitational entropic force. As a result, gravity can be derived as emergent phenomenon from thermodynamics.
The thesis contains an overview about recent quantum gravity black hole approaches and concludes with the derivation of nonlocal operators that modify the Einstein equations to ultraviolet complete field equations.
The state-of-the-art pattern recognition method in machine learning (deep convolution neural network) is used to identify the equation of state (EoS) employed in the relativistic hydrodynamic simulations of heavy ion collisions. High-level correlations of particle spectra in transverse momentum and azimuthal angle learned by the network act as an effective EoS-meter in deciphering the nature of the phase transition in QCD. The EoS-meter is model independent and insensitive to other simulation inputs including the initial conditions and shear viscosity for hydrodynamic simulations. Through this study we demonstrate that there is a traceable encoder of the dynamical information from the phase structure that survives the evolution and exists in the final snapshot of heavy ion collisions and one can exclusively and effectively decode these information from the highly complex final output with machine learning when traditional methods fail. Besides the deep neural network, the performance of traditional machine learning classifiers are also provided.