Recognition: unknown
An iterative, dynamically stabilized method of data unfolding
read the original abstract
We propose a new iterative unfolding method for experimental data, making use of a regularization function. The use of this function allows one to build an improved normalization procedure for Monte Carlo spectra, unbiased by the presence of possible new structures in data. We are able to unfold, in a dynamically stable way, data spectra which can be strongly affected by fluctuations in the background subtraction and simultaneously reconstruct structures which were not initially simulated. This method also allows one to control the amount of correlations introduced between the bins of the unfolded spectrum, when the transfers of events correcting the systematic detector effects are performed.
This paper has not been read by Pith yet.
Forward citations
Cited by 3 Pith papers
-
Differential measurements of $\gamma\gamma\to\tau\tau$ and constraints on $\tau$-lepton electromagnetic moments in Pb+Pb collisions at $\sqrt{s_{_\text{NN}}} = 5.02$ TeV with ATLAS
First differential cross-sections for γγ→ττ in Pb+Pb collisions yield 95% CL intervals -0.057 < a_τ < 0.035 and |d_τ| < 2.7×10^{-16} e cm.
-
Multiplicity dependence of prompt and non-prompt J/$\psi$ production at midrapidity in pp collisions at $\sqrt{s} = 13$ TeV
Self-normalized yields of prompt and non-prompt J/ψ increase stronger than linearly with charged-particle multiplicity in pp collisions at 13 TeV, with stronger effect in the toward azimuthal region.
-
Muon $g$$-$2: correlation-induced uncertainties in precision data combinations
A general framework quantifies correlation-induced uncertainties in precision data combinations and applies it to e+e- to hadrons cross sections for muon g-2 HVP determinations.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.