Abstract

Online selection is an essential step to collect the most relevant collisions from the very large number of collisions inside the ATLAS detector at the Large Hadron Collider (LHC). The Fast TracKer (FTK) is a hardware based track finder, built to greatly improve the ATLAS trigger system capabilities for identifying interesting physics processes through track-based signatures. The FTK is reconstructing after each Level-1 trigger all tracks with pT > 1 GeV, such that the high-level trigger system gains access to track information at an early stage. FTK track reconstruction starts with a pattern recognition step. Patterns are found with hits in seven out of eight possible detector layers. Disabled detector modules, as often encountered during LHC operation, lead to efficiency losses. To recover efficiency, WildCards (WC) algorithms are implemented in the FTK system. The WC algorithm recovers inefficiency but also causes high combinatorial background and thus increased data volumes in the FTK system, possibly exceeding hardware limitations. To overcome this, a refined algorithm to select patterns is developed and investigated in this article.

Highlights

  • The ATLAS trigger system [1] is a combination of a hardware-based Level 1 and softwarebased High Level Trigger (HLT), which reduces the event rate from 40 MHz to an average output rate of 1 kHz

  • The pattern recognition is the heart of the Fast TracKer (FTK) system, using a dedicated technology based on Content Addressable Memories (CAM), the Associative Memories boards (AM) chip [3]

  • The result is expected to be similar to the case P = 0, patterns can be packed more efficiently when knowing about disabled modules during bank production

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Summary

Introduction

The ATLAS trigger system [1] is a combination of a hardware-based Level 1 and softwarebased High Level Trigger (HLT), which reduces the event rate from 40 MHz to an average output rate of 1 kHz. During data-taking, tracking detectors may encounter problems, which can lead to disabled detector modules These do not provide useful hit information and cause inefficiencies in the FTK track reconstruction. Disabled modules on which WCs are set, are treated as if all their channels were on for each event This does recover efficiency losses, but leads to a sizable increase in the number of fake track segments, which has the potential to slow down or even saturate the FTK system. To control these effects, modifications to the pattern selection scheme are implemented that reduce the amount of data while keeping a reasonable track reconstruction efficiency.

FTK system design
Pattern recognition
Track fitting
TSP bank of pattern candidates
AM pattern bank for use in the pattern recognition
Packing pattern candidates to the AM pattern bank
WildCards optimisation
WildCards algorithm
The WildCards penalty algorithm
Comparisons
Results
Conclusion
Full Text
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