REVIEW 3 cited by
Improved Extrapolation Methods of Data-driven Background Estimation in High-Energy Physics
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Data-driven methods of background estimations are often used to obtain more reliable descriptions of backgrounds. In hadron collider experiments, data-driven techniques are used to estimate backgrounds due to multi-jet events, which are difficult to model accurately. In this article, we propose an improvement on one of the most widely used data-driven methods in the hadron collision environment, the "ABCD" method of extrapolation. We describe the mathematical background behind the data-driven methods and extend the idea to propose improved general methods.
Forward citations
Cited by 3 Pith papers
-
Search for soft unclustered energy patterns in proton-proton collisions at $\sqrt{s}$ = 13 TeV using data scouting
No SUEP signal is observed in CMS scouting data; the search sets the most stringent limits to date on gluon-fusion production of heavy scalars decaying to SUEP-like final states.
-
Search for the nonresonant and resonant production of a Higgs boson in association with an additional scalar boson in the $\gamma\gamma\tau\tau$ final state in proton-proton collisions at $\sqrt{s}$ = 13 TeV
CMS finds no evidence for Higgs-pair or new-scalar production in the two-photon plus two-tau final state and sets new upper limits on these processes.
-
Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC
The new nonclosure loss term, made differentiable with a sigmoid approximation, lets the neural network optimize the ABCD background estimate directly, improving closure and training stability.
Discussion (0). Sign in to comment.