Fast-Forwarding the Search for New Particles

In the eyes of an old and famous statistics paper [1], searching for evidence of a new particle in the complex data of the Large Hadron Collider should be easy. You just need to calculate two numbers: the likelihood that the data came from the hypothetical particle and the likelihood that it did not. If the ratio between these numbers is high enough, you’ve made a discovery. Can it really be that easy? In principle, yes, but in practice, the two likelihood calculations are intractable. So instead, particle physicists approximate the likelihood ratio by making simplifying assumptions. Even then, the calculation requires a huge amount of computer time. In a pair of papers [2], Johann Brehmer of New York University and colleagues propose a new approach that avoids the typical simplifications and doesn’t demand long computation times. Their method, which relies on machine-learning tools, could significantly boost physicists’ power to discover new particles in their data.