Abstract
This work proposes a novel longitudinal phase-space reconstruction method for hadron machines. The proposed method is based on a Kalman filter and can therefore provide real-time estimates of the phase-space reconstruction. The main input for the method is real-time measurements of the longitudinal bunch profile. Beam conditions in the LHC are used throughout this work as examples of the applicability and practical implementation of such a method. Longitudinal phase-space reconstructions obtained with the proposed method are compared with a tomographic-based technique using experimentally logged data from the LHC wall current monitors.
Highlights
The knowledge of the longitudinal phase space can provide valuable information on the quality of the beam including injection matching, capture performance, stability, time/phase spread, momentum/energy spread, longitudinal emittance, injection oscillations and filamentation
Other tomographybased implementations and system design proposals have been reported in different facilities [2,3,4,5]
We propose here a different approach based on a Kalman filter
Summary
The knowledge of the longitudinal phase space can provide valuable information on the quality of the beam including injection matching, capture performance, stability, time/phase spread, momentum/energy spread, longitudinal emittance, injection oscillations and filamentation. A tomographic-based reconstruction method coupled with a particle tracking code using longitudinal beam profile measurements has been proposed in [1]. We propose here a different approach based on a Kalman filter. It can be used in order to reconstruct the phase space, in real time, for arbitrarily long (i.e., unbounded) time intervals. We build a model for the evolution of the particle density in a polar discretization of the longitudinal phase space as well as a model that relates this density with the longitudinal bunch profile. We show how these models can be incorporated in the context of a Kalman filter estimating the phase-space density using profile. We present reconstruction results based on both real and simulated data, as well as a comparison with the tomography-based method.
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