Modern experiments search for extremely rare processes hidden in much larger background levels. As the experiment complexity, the accelerator backgrounds and luminosity increase we need increasingly exclusive selections to efficiently select the rare events inside the huge background. We present a fast, high-quality, track-based event selection for the self-triggered SLIM5 silicon telescope. This is an R&D experiment whose innovative trigger will show that high rejection factors and manageable trigger rates can be achieved using fine-granularity, low-material tracking detectors. This strategy requires massive computing power to minimize the online execution time of complex tracking algorithms. Affordable latency and rates are provided by a dedicated device, the Associative Memory (AM). The time consuming pattern recognition problem, generally referred to as the "combinatorial challenge", is beaten by the AM exploiting parallelism to the maximum level: it compares the event to precalculated "expectations" (pattern matching) at once. This approach reduces to linear the typical exponential complexity of the CPU-based algorithms. The problem is solved by the time data are loaded into the AM devices. We describe the AM-based trigger and its successful use in the SLIM5 test beam where a silicon detector telescope has been recently tested on the CERN SPS beam.

The associative memory for the self-triggered SLIM5 silicon telescope

Ratti, Lodovico;Giacomini, Gabriele;Rachevskaia, Irina;
2008

Abstract

Modern experiments search for extremely rare processes hidden in much larger background levels. As the experiment complexity, the accelerator backgrounds and luminosity increase we need increasingly exclusive selections to efficiently select the rare events inside the huge background. We present a fast, high-quality, track-based event selection for the self-triggered SLIM5 silicon telescope. This is an R&D experiment whose innovative trigger will show that high rejection factors and manageable trigger rates can be achieved using fine-granularity, low-material tracking detectors. This strategy requires massive computing power to minimize the online execution time of complex tracking algorithms. Affordable latency and rates are provided by a dedicated device, the Associative Memory (AM). The time consuming pattern recognition problem, generally referred to as the "combinatorial challenge", is beaten by the AM exploiting parallelism to the maximum level: it compares the event to precalculated "expectations" (pattern matching) at once. This approach reduces to linear the typical exponential complexity of the CPU-based algorithms. The problem is solved by the time data are loaded into the AM devices. We describe the AM-based trigger and its successful use in the SLIM5 test beam where a silicon detector telescope has been recently tested on the CERN SPS beam.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11582/13488
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