Abstract
The classification of events involving jets as signal-like or background-like can depend strongly on the jet algorithm used and its parameters. This is partly due to the fact that standard jet algorithms yield a single partition of the particles in an event into jets, even if no particular choice stands out from the others. As an alternative, we propose that one should consider multiple interpretations of each event, generalizing the Qjets procedure to event-level analysis. With multiple interpretations, an event is no longer restricted to either satisfy cuts or not satisfy them - it can be assigned a weight between 0 and 1 based on how well it satisfies the cuts. These cut-weights can then be used to improve the discrimination power of an analysis or reduce the uncertainty on mass or cross-section measurements. For example, using this approach on a Higgs plus Z boson sample, with h->bb we find an 28% improvement in significance can be realized at the 8 TeV LHC. Through a number of other examples, we show various ways in which having multiple interpretations can be useful on the event level.
Highlights
In the majority of cases, such as when there are a few, well-separated jets, the best guess interpretation from any algorithm provides an excellent representation of the event
We propose a way to consider multiple interpretations of an event at once
Since N different jet masses result one can look at the width of the mass distribution for a single jet
Summary
To quantity the improvement from our procedure we adopt as a measure the excess number of events measured S divided by the expected fluctuations in the background δB. If the background was only expected to fluctuate by δB = 20 events, the significance is S/δB = 5, which conventionally characterizes a discovery. A key feature of Qjets is that events are not characterized as signal or background (e.g. by passing some cuts or not passing them). Rather they are assigned a weight z between 0 and 1 based on how many interpretations of the event are signal-like (according to some measure). We first review how the expected value and variance of B are computed in a classical analysis and describe how cut-weights can improve significance
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
