Speaker
Description
The Mu3e experiment searches for the lepton flavour violating decay $\mu^+ \rightarrow e^+e^-e^+$,
aiming at a branching ratio sensitivity better than $10^{-16}$. To reach this
sensitivity, muon rates above $10^9 \mu/s$ are required. A high precision silicon tracking detector combined with excellent timing resolution from
scintillating fibers and tiles will measure the momenta, vertices and timing
of the decay products of muons stopped in the target to suppress background.
During the first phase of the experiment, a muon rate of $10^8 \mu/s$ will be
available, resulting in a rate of $\sim$10 GB/s of zero-suppressed
data. The trigger-less readout system consists of optical links and switching
FPGAs sending the complete
detector data for a time slice to one node of the filter farm.
Since we can only store $\sim$ 100 MB/s of data, a full online reconstruction is necessary for an event selection. This is the ideal situation to
make use of the highly parallel structure of graphics
processing units (GPUs).
An FPGA inside the filter farm PC therefore
transfers the event data to the main memory of the PC and then to GPU memory via PCIe direct memory access. The GPU
finds and fits tracks using a non-iterative 3D tracking algorithm for multiple scattering
dominated resolution. For three hits from subsequent detector planes, a helix
is fitted by assuming that multiple scattering at the middle hit is the only
source of uncertainty.
In a second step, a three track vertex selection is performed by calculating the
vertex position from the intersections of the tracks in the plane
perpendicular to the beam axis and weighting them by the uncertainties from
multiple scattering and pixel pitch.
Together with kinematic cuts this allows for a reduction
of the output data rate to below 100 MB/s by removing combinatorial background.
The talk will focus on the implementation of the track fit and vertex selection on the GPU and performance studies will be presented.
