Data Unfolding Methods in High Energy Physics

@inproceedings{Schmitt2017DataUM,
  title={Data Unfolding Methods in High Energy Physics},
  author={Stefan Schmitt},
  year={2017}
}
A selection of unfolding methods commonly used in High Energy Physics is compared. The methods discussed here are: bin-by-bin correction factors, matrix inversion, template fit, Tikhonov regularisation and two examples of iterative methods. Two procedures to choose the strength of the regularisation are tested, namely the L-curve scan and a scan of global correlation coefficients. The advantages and disadvantages of the unfolding methods and choices of the regularisation strength are discussed… 

Figures and Tables from this paper

Deconvolution of the High Energy Particle Physics Data with Machine Learning
TLDR
In this particular study, deconvolution is interpreted as a classification problem, and neural networks are trained to deconvolute the Z boson invariant mass spectrum generated with MadGraph and pythia8 Monte Carlo event generators in order to prove the principle.
The electron–ion collider: assessing the energy dependence of key measurements
TLDR
It is demonstrated, through detailed simulations of the measurements, that the likelihood of transformational scientific insights is greatly enhanced by making the energy range and reach of the EIC as large as practically feasible.
Reporting Results in High Energy Physics Papers: a Manifesto
The complexity of collider data analyses has dramatically increased from early colliders to the LHC. Reconstruction of physics objects has reached a point that requires dedicated papers documenting
Analysis at Particle Level
The chapter is organised as follows. First, the disagreement in the b-tagged fraction observed in the previous chapter is investigated; a correction to the simulation is applied to fix the
Unfolding by Folding: a resampling approach to the problem of matrix inversion without actually inverting any matrix
TLDR
Rather than inverting the response matrix and transforming the observed distribution into the most likely parent distribution in generator space, the algorithm performs as well as traditional unfolding algorithms in cases where the inverse problem is well-defined in terms of the discretization of the true and smeared space, and outperforms them in cases when the inverse Problem is ill-defined.
Reporting results in High Energy Physics publications: A manifesto
Double-bunch unfolding methods for the Back-n white neutron source at CSNS
The Back-n white neutron source at China Spallation Neutron Source (CSNS) is designed specifically for nuclear data measurements and multidisciplinary neutron applications. The time of flight (TOF)
Frequentist-Bayes Hybrid Covariance Estimationfor Unfolding Problems
TLDR
A frequentist-Bayesian hybrid method for estimating covariances of unfolded distributions using pseudo-experiments that has the added advantage of not requiring a clear likelihood definition and can be used in combination with any unfolding algorithm that uses a response matrix to model the detector response.
Neutral-current electroweak physics and SMEFT studies at the EIC
We study the potential for precision electroweak (EW) measurements and beyond-the-Standard Model (BSM) searches using cross-section asymmetries in neutral-current (NC) deep inelastic scattering at
Measurement of double-differential cross sections for top quark pair production in pp collisions at and impact on parton distribution functions
Normalized double-differential cross sections for top quark pair (tt) production are measured in pp collisions at a centre-of-mass energy of 8 TeV with the CMS experiment at the LHC. The analyzed
...
...

References

SHOWING 1-10 OF 29 REFERENCES
TUnfold, an algorithm for correcting migration effects in high energy physics
TUnfold is a tool for correcting migration and background effects in high energy physics for multi-dimensional distributions. It is based on a least square fit with Tikhonov regularisation and an
An Iterative, Dynamically Stabilized (IDS) Method of Data Unfolding
TLDR
An iterative unfolding method for experimental data, making use of a regularization function, which allows one to build an improved normalization procedure for Monte Carlo spectra, unbiased by the presence of possible new structures in data.
The L-curve and its use in the numerical treatment of inverse problems
The L-curve and use the numerical The L-curve is a log-log plot of the norm of a regularized solution versus the norm of the corresponding residual norm. It is a convenient graphical tool for
Discrete Inverse Problems: Insight and Algorithms
Inverse problems arise when we reconstruct a sharper image from a blurred one or reconstruct the underground mass density from measurements of the gravity above the ground. When we solve an inverse
Private communication
vii
Soviet Math
  • Dokl. 4
  • 1963
...
...