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Register a new userMonte Carlo simulation for radiative kaon decays
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C. Gatti
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For high precision measurements of K decays, the presence of radiated photons cannot be neglected. The Monte Carlo simulations must include the radiative corrections in order to compute the correct event counting and efficiency calculations. In this paper we briefly describe a method for simulating such decays.
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The algorithm for Monte Carlo simulation of parton-level events based on an Artificial Neural Network (ANN) proposed in arXiv:1810.11509 is used to perform a simulation of $Hto 4ell$ decay. Improvements in the training algorithm have been implemented to avoid numerical instabilities. The integrated decay width evaluated by the ANN is within 0.7% of the true value and unweighting efficiency of 26% is reached. While the ANN is not automatically bijective between input and output spaces, which can lead to issues with simulation quality, we argue that the training procedure naturally prefers bijective maps, and demonstrate that the trained ANN is bijective to a very good approximation.
While measuring the ratio R_K = Gamma(K+e2(gamma))/Gamma(K+mu2(gamma)), the KLOE Collaboration has studied the radiative process Ke2gamma. The ratio of the width for the Ke2gamma decay with a positively polarized photon from structure-dependent radiation to the inclusive Kmu2(gamma) width is found to be 1.484(68) 10-5. The observed radiation spectrum agrees with predictions from chiral perturbation theory and is in contrast with predictions based on the light front quark model. This result reduces the contribution to systematic uncertainties on measurements of R_K. In a separate study, KLOE has measured the ratio of the radiative Ke3gamma decay width to the inclusive Ke3(gamma) width to be 924(28) 10-5. The distribution in energy and angle of the radiative photon has been analyzed in an attempt to isolate the signature from interference of the inner-bremsstrahlung and structure-dependent amplitudes.
Monte Carlo simulations are widely used in many areas including particle accelerators. In this lecture, after a short introduction and reviewing of some statistical backgrounds, we will discuss methods such as direct inversion, rejection method, and Markov chain Monte Carlo to sample a probability distribution function, and methods for variance reduction to evaluate numerical integrals using the Monte Carlo simulation. We will also briefly introduce the quasi-Monte Carlo sampling at the end of this lecture.
Because of properties of QED, the bremsstrahlung corrections to decays of particles or resonances can be calculated, with a good precision, separately from other effects. Thanks to the widespread use of event records such calculations can be embodied into a separate module of Monte Carlo simulation chains, as used in High Energy Experiments of today. The PHOTOS Monte Carlo program is used for this purpose since nearly 20 years now. In the following talk let us review the main ideas and constraints which shaped the program version of today and enabled it widespread use. We will concentrate specially on conflicting requirements originating from the properties of QED matrix elements on one side and degrading (evolving) with time standards of event record(s). These issues, quite common in other modular software applications, become more and more difficult to handle as precision requirements become higher.
The theory and numerical modelling of radiation processes and radiative transfer play a key role in astrophysics: they provide the link between the physical properties of an object and the radiation it emits. In the modern era of increasingly high-quality observational data and sophisticated physical theories, development and exploitation of a variety of approaches to the modelling of radiative transfer is needed. In this article, we focus on one remarkably versatile approach: Monte Carlo Radiative Transfer (MCRT). We describe the principles behind this approach, and highlight the relative ease with which they can (and have) been implemented for application to a range of astrophysical problems. All MCRT methods have in common a need to consider the adverse consequences of Monte Carlo noise in simulation results. We overview a range of methods used to suppress this noise and comment on their relative merits for a variety of applications. We conclude with a brief review of specific applications for which MCRT methods are currently popular and comment on the prospects for future developments.
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