https://doi.org/10.1140/epjc/s10052-017-4687-y
Regular Article - Theoretical Physics
On the challenge of estimating diphoton backgrounds at large invariant mass
1
Jožef Stefan Institute, Jamova 39, 1000, Ljubljana, Slovenia
2
Faculty of Mathematics and Physics, University of Ljubljana, Jadranska 19, 1000, Ljubljana, Slovenia
3
Department of Particle Physics and Astrophysics, Weizmann Institute of Science, 7610001, Rehovot, Israel
4
Physik Department T75, Technische Universität München, James-Franck-Strasse 1, 85748, Garching, Germany
* e-mail: Matthias.Schlaffer@weizmann.ac.il
Received:
7
December
2016
Accepted:
8
February
2017
Published online:
24
February
2017
We examine, using the analyses of the 750 GeV diphoton resonance as a case study, the methodology for estimating the dominant backgrounds to diphoton resonance searches. We show that close to the high energy tails of the distributions, where background estimates rely on functional extrapolations or Monte Carlo predictions, large uncertainties are introduced, in particular by the challenging photon–jet background. Analyses with loose photon and low photon 
cuts and those susceptible to high photon rapidity regions are especially affected. Given that diphoton-based searches beyond 1 TeV are highly motivated as discovery modes, these considerations are relevant for future analyses. We first consider a physics-driven deformation of the photon–jet spectrum by next-to-leading order effects and a phase space dependent fake rate and show that this reduces the local significance of the excess. Using a simple but more general ansatz, we demonstrate that the originally reported local significances of the 750 GeV excess could have been overestimated by more than one standard deviation. We furthermore cross-check our analysis by comparing fit results based on the 2015 and 2016 LHC data sets. Finally we employ our methodology on the available 13 TeV LHC data set assessing the systematics involved in the current diphoton searches beyond the TeV region.
© The Author(s), 2017
