Speaker
Description
In collider experiments, particle identification (PID) in drift chambers has traditionally relied on the ionization energy loss ($dE/dx$), whose resolution is fundamentally limited by large Landau fluctuations. Instead, cluster counting ($dN/dx$) measures the number of primary ionization clusters, which is Poisson in nature with smaller statistical fluctuations and can offer higher separation power. However, measuring $dN/dx$ at drift chambers proposed for future colliders requires high-resolution waveforms which result in very large data rates. Running the reconstruction at the edge would drastically reduce the bandwidth required to transmit data off the detector. In this paper, we propose applying spiking neural networks (SNNs) to this task due to their sparse and low-power characteristics. We show that our model achieves better PID performance than traditional derivative-based methods, and that when synthesized to an FPGA, its inference latency is compatible with the constraints of future collider experiments, making the SNN an excellent algorithmic candidate for real-time data readout in future drift chambers.
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