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Title: Dissecting jets and missing energy searches using $$n$$-body extended simplified models

Journal Article · · Journal of High Energy Physics (Online)
 [1];  [2];  [3];  [4];  [4];  [4]
  1. Univ. of Oregon, Eugene, OR (United States)
  2. Univ. of Melbourne (Australia)
  3. Johannes Gutenberg-Univ., Mainz (Germany)
  4. Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)

Simplified Models are a useful way to characterize new physics scenarios for the LHC. Particle decays are often represented using non-renormalizable operators that involve the minimal number of fields required by symmetries. Generalizing to a wider class of decay operators allows one to model a variety of final states. This approach, which we dub the $$n$$-body extension of Simplified Models, provides a unifying treatment of the signal phase space resulting from a variety of signals. In this paper, we present the first application of this framework in the context of multijet plus missing energy searches. The main result of this work is a global performance study with the goal of 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. Utilizing boosted decision trees, we compare and classify the performance of missing energy, energy scale and energy structure observables. We demonstrate that including an observable from each of these three classes is required to achieve optimal performance. In conclusion, this work additionally serves to establish 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 new physics signatures beyond jets and missing energy.

Research Organization:
Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)
Sponsoring Organization:
USDOE Office of Science (SC), High Energy Physics (HEP)
Grant/Contract Number:
AC02-07CH11359
OSTI ID:
1287383
Report Number(s):
FERMILAB-PUB-16-122-E; arXiv:1605.01416; 1455878
Journal Information:
Journal of High Energy Physics (Online), Vol. 2016, Issue 8; ISSN 1029-8479
Publisher:
Springer BerlinCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 4 works
Citation information provided by
Web of Science

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Cited By (4)

Likelihood analysis of supersymmetric SU(5) GUTs text January 2017
Likelihood analysis of supersymmetric SU(5) GUTs journal February 2017
Likelihood analysis of supersymmetric SU(5) GUTs text January 2017
(Machine) Learning to Do More with Less text January 2017