TL;DR: In this article, a physically motivated function was developed to accurately determine the total absorption peak in an electromagnetic calorimeter and to overcome biases present in many commonly used methods, such as the Crystal Ball function.
Abstract: A physically motivated function was developed to accurately determine the total absorption peak in an electromagnetic calorimeter and to overcome biases present in many commonly used methods. The function is the convolution of a detector resolution function with the sum of a delta function, which represents the complete absorption of energy, and a tail function, which describes the partial absorption of energy and depends on the detector materials and structures. Its performance was tested with the simulation of three typical cases. The accuracy of the extracted peak value, resolution, and peak area was improved by an order of magnitude on average, relative to the Crystal Ball function.
TL;DR: In this article, the authors proposed a simple alternative to the Crystal Ball function that has an exponential tail stitched to a Gaussian core, which offers more stable fits to peaks that continue into exponential tails.
Abstract: We present a simple alternative to the Crystal Ball function that has an exponential tail stitched to a Gaussian core. It has one parameter less than the Crystal Ball function and, where appropriate, offers more stable fits to peaks that continue into exponential tails. The function may also be extended with two exponential tails on each side of the Gaussian, and this has two parameters less than the corresponding double-shouldered Crystal Ball function. This function has been used to model background and signal processes in a recent Higgs pair production search and may be of versatile use in experimental physics and other fields.
TL;DR: In this article, generalizations of the Crystal Ball function are presented to describe mass peaks in which the per event mass resolution is unknown and marginalized over, and tested using a series of toy Monte Carlo samples generated with Pythia and smeared with different amounts of multiple scattering and for different detector resolutions.
Abstract: We present generalizations of the Crystal Ball function to describe mass peaks in which the per event mass resolution is unknown and marginalized over. The presented probability density functions are tested using a series of toy Monte Carlo samples generated with Pythia and smeared with different amounts of multiple scattering and for different detector resolutions. (C) 2014 Elsevier B.V. All rights reserved.