Speaker
Description
New physics scenarios for the LHC are often characterized by Simplified Models,
where the decay of a given particle is represented by an operator involving a minimal
number of fields. Such decay operators can be generalized beyond the
standard cases to describe a wide variety of final state multiplicities. This approach,
which we dub the n-body extension of Simplified Models, provides a unifying
treatment of the signal phase space resulting from a large class of new physics
scenarios. In this talk, we present its first application, in the context of multijet
plus missing energy searches. We present a global performance study aiming at
identifying which set of observables yields the best discriminating power against the
largest Standard Model backgrounds for a wide range of signal jet multiplicities. Our
analysis compares combinations of one, two and three variables, placing emphasis
on the enhanced sensitivity gain resulting from non-trivial correlations. To this
end, machine-learning techniques known as boosted decision trees are employed.
We compare and classify performance of combinations of missing energy, energy
scale and energy structure observables, and we demonstrate that observables from
each of the three classes are required to achieve optimal performance. This work
additionally serves to demonstrate the utility of n-body extended Simplified Models
as a diagnostic for unpacking the relative merits of different search strategies, thereby
motivating their application to other signatures of new physics beyond jets and
missing energy.
