Search for New Physics Using High Energy Collider Data

New Physics searches using a high energy collider experiment should be performed systematically, especially when interpreting a new signal with a specific new Physics model among different possibilities. A data-driven method should be used in a model-independent way to capture all scenarios. In this article, we demonstrate how one can achieve a systematic and model-independent data analysis by separating an analysis on a new Physics signature into a phase space structure and matrix amplitude of a process. For instance, we consider a process with two visible particles and dark matter candidate signal at the high energy hadron collider. Finally, we recommend a proposed method to combine with a machine learning network and an algorithm to probe the broad scope of Physics beyond the standard model efficiently.