Searching for Lorentz invariance violation with artificial neural networks

Lorentz invariance violation (LIV) can have multiple consequences on very-high energy gamma rays' emission, propagation, and detection, such as energy-dependent photon group velocity, photon instability, vacuum birefringence, and modified electromagnetic interaction. Depending on the underlying theoretical model, several of these effects can coexist. Nevertheless, in experimental tests of LIV, each effect is tested separately and independently. Here, we are performing a search for traces of several coexisting effects in a single analysis. We present our analysis method based on artificial neural networks and put our very first results in the context of experimental searches for LIV.