The impact of applying WildCards to disabled modules for FTK pattern banks on efficiency and data flow

TL;DR

This paper investigates the implementation of WildCards algorithms in the FTK system at the LHC to recover efficiency lost due to disabled detector modules, balancing increased data flow with pattern selection refinement.

Contribution

It introduces a refined pattern selection algorithm to mitigate increased data volumes caused by WildCards in FTK pattern recognition.

Findings

WildCards recover efficiency in FTK despite disabled modules.

Refined pattern selection reduces combinatorial background.

Implementation balances efficiency gains with data flow constraints.

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 $ p_{\textrm T}>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.