Speaker
Description
Unfolding can be considered a procedure for estimating an unknown probability density function. Both external and internal quality assessment methods can be used for this purpose.
In some cases, external criteria exist that allow for the gauging of the quality of deconvolution. A typical example is the deconvolution of a blurred image, where the sharpness of the unblurred image can be used to assess the quality of the result. In experimental physics, it is sometimes difficult to define such external criteria, especially when a measurement has never been done before. Therefore, internal criteria for assessing the goodness of the result, which do not require any reference to external information, are needed.
In this context, internal criteria for goodness are proposed and discussed. Their application is demonstrated in the comparison of Richardson-Lucy deconvolution and a new data unfolding method base on Mean Integrated Error Optimization.
Significance
It is new results will be presented first time
References
N.D. Gagunashvili, Data unfolding with mean integrated square error optimization.
arXiv:2402.12990
Will be published in Computer Physics Communication in March 2025
| Experiment context, if any | The author does not take part in any experiment. |
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