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Abstract

A new era of measurements and tests of the Standard Model of particle physics at the Large Hadron Collider has been shaped by novel methodologies applied to simulations and data analysis. Machine learning is leading this revolution with constant and progressive development of new tools for understanding our data. We have access to techniques that exploit correlations in high-dimensional phase spaces in a statistically principled way. In the context of colliders, these techniques not only boost searches for physics beyond the Standard Model but also improve the already advanced simulation chain. We consider two obstacles that will be critical for the LHC. We propose fast, accurate, and precise surrogate models for simulating the detector response, answering the increasing demand for simulations in the future runs of the LHC. Second, we develop tools for searches of new physics that do not rely on assumptions of the specific new physics signature These tools aim to complement the current paradigm of direct tests of extensions of the SM, which can carry limiting assumptions. We unify these two applications under the lens of precise density estimation using modern machine learning tools, and we demonstrate the importance of using powerful representations that leverage our physics knowledge, e.g. symmetries.