Muons in the CMS High Level Trigger System

During its second run of operation (Run 2), started in 2015, the LHC will deliver a peak instantaneous luminosity that may reach 2 · 1034 cm-2s-1 with an average pileup of about 55, far larger than the design value. Under these conditions, the online event selection is a very challenging task. In CMS, it is realized by a two-level trigger system: the Level-1 (L1) Trigger, implemented in custom-designed electronics, and the High Level Trigger (HLT), a streamlined version of the offine reconstruction software running on a computer farm. In order to face this challenge, the L1 trigger has been through a major upgrade compared to Run 1, whereby all electronic boards of the system have been replaced, allowing more sophisticated algorithms to be run online. Its last stage, the global trigger, is now able to perform complex selections and to compute high-level quantities, like invariant masses. Likewise, the algorithms that run in the HLT have been greatly improved; in particular, new approaches for the online track reconstruction lead to a drastic reduction of the computing time, and to much improved performances. This document will describe the performance of the upgraded trigger system in Run 2.

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Demonstrator of the Belle II online tracking and pixel data reduction on the High Level Trigger system

2014 19th IEEE-NPSS Real Time Conference ◽

10.1109/rtc.2014.7097438 ◽

2014 ◽

Cited By ~ 1

Author(s):

T. Bilka ◽

G. Casarosa ◽

R. Fruhwirth ◽

P. Kodys ◽

P. Kvasnicka ◽

...

Keyword(s):

Data Reduction ◽

Trigger System ◽

Level Trigger ◽

Online Tracking ◽

Belle Ii ◽

High Level

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The CMS High Level Trigger System: Experience and Future Development

Journal of Physics Conference Series ◽

10.1088/1742-6596/396/1/012008 ◽

2012 ◽

Vol 396 (1) ◽

pp. 012008 ◽

Cited By ~ 3

Author(s):

G Bauer ◽

U Behrens ◽

M Bowen ◽

J Branson ◽

S Bukowiec ◽

...

Keyword(s):

Future Development ◽

Trigger System ◽

Level Trigger ◽

High Level

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The CMS high level trigger

International Journal of Modern Physics Conference Series ◽

10.1142/s201019451460297x ◽

2014 ◽

Vol 31 ◽

pp. 1460297 ◽

Cited By ~ 1

Author(s):

Valentina Gori

Keyword(s):

Trigger System ◽

Output Rate ◽

Computing Power ◽

Level Trigger ◽

Cms Experiment ◽

Pile Up ◽

Reconstruction Software ◽

Physics Performance ◽

Level 1 ◽

High Level

The CMS experiment has been designed with a 2-level trigger system: the Level 1 Trigger, implemented on custom-designed electronics, and the High Level Trigger (HLT), a streamlined version of the CMS offline reconstruction software running on a computer farm. A software trigger system requires a tradeoff between the complexity of the algorithms running on the available computing power, the sustainable output rate, and the selection efficiency. Here we will present the performance of the main triggers used during the 2012 data taking, ranging from simpler single-object selections to more complex algorithms combining different objects, and applying analysis-level reconstruction and selection. We will discuss the optimisation of the triggers and the specific techniques to cope with the increasing LHC pile-up, reducing its impact on the physics performance.

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The Impact of Applying Wildcards to Disabled Modules for Ftk Pattern Banks on Efficiency and Data Flow

EPJ Web of Conferences ◽

10.1051/epjconf/201921401039 ◽

2019 ◽

Vol 214 ◽

pp. 01039

Author(s):

Khalil Bouaouda ◽

Stefan Schmitt ◽

Driss Benchekroun

Keyword(s):

Early Stage ◽

Hadron Collider ◽

Track Reconstruction ◽

Trigger System ◽

Level Trigger ◽

Efficiency Losses ◽

Level 1 ◽

High Level ◽

The Impact ◽

Fast Tracker

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.

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Trigger Rate Monitoring Tools at CMS

EPJ Web of Conferences ◽

10.1051/epjconf/201921401047 ◽

2019 ◽

Vol 214 ◽

pp. 01047

Author(s):

Andrew Wightman ◽

Geoffrey Smith ◽

Kelci Mohrman ◽

Charles Mueller

Keyword(s):

Event Rate ◽

Trigger System ◽

Level Trigger ◽

Cms Experiment ◽

Trigger Rate ◽

Monitoring Tools ◽

Pile Up ◽

Overall Performance ◽

Level 1 ◽

High Level

One of the major challenges for the Compact Muon Solenoid (CMS)experiment, is the task of reducing event rate from roughly 40 MHz down to a more manageable 1 kHz while keeping as many interesting physics events as possible. This is accomplished through the use of a Level-1 (L1) hardware based trigger as well as a software based High-Level Trigger (HLT). Monitoring and understanding the output rates of the L1 and HLT triggers is of key importance for determining the overall performance of the trigger system and is intimately tied to what type of data is being recorded for physics analyses. We present here a collection of tools used by CMS to monitor the L1 and HLT trigger rates. One of these tools is a script (run in the CMS control room) that gives valuable real-time feedback of trigger rates to the shift crew. Another useful tool is a plotting library, that is used for observing how trigger rates vary over a range of beam and detector conditions, in particular how the rates of individual triggers scale with event pile-up.

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Operational experience with the new CMS DAQ-Expert

EPJ Web of Conferences ◽

10.1051/epjconf/201921401015 ◽

2019 ◽

Vol 214 ◽

pp. 01015 ◽

Cited By ~ 1

Author(s):

Jean-Marc Andre ◽

Ulf Behrens ◽

James Branson ◽

Philipp Brummer ◽

Sergio Cittolin ◽

...

Keyword(s):

Expert Knowledge ◽

Operational Experience ◽

Trigger System ◽

Web Based ◽

High Data ◽

Software Applications ◽

Level Trigger ◽

Level 1 ◽

Logic Modules ◽

High Level

The data acquisition (DAQ) system of the Compact Muon Solenoid (CMS) at CERN reads out the detector at the level-1 trigger accept rate of 100 kHz, assembles events with a bandwidth of 200 GB/s, provides these events to the high level-trigger running on a farm of about 30k cores and records the accepted events. Comprising custom-built and cutting edge commercial hardware and several 1000 instances of software applications, the DAQ system is complex in itself and failures cannot be completely excluded. Moreover, problems in the readout of the detectors,in the first level trigger system or in the high level trigger may provoke anomalous behaviour of the DAQ systemwhich sometimes cannot easily be differentiated from a problem in the DAQ system itself. In order to achieve high data taking efficiency with operators from the entire collaboration and without relying too heavily on the on-call experts, an expert system, the DAQ-Expert, has been developed that can pinpoint the source of most failures and give advice to the shift crew on how to recover in the quickest way. The DAQ-Expert constantly analyzes monitoring data from the DAQ system and the high level trigger by making use of logic modules written in Java that encapsulate the expert knowledge about potential operational problems. The results of the reasoning are presented to the operator in a web-based dashboard, may trigger sound alerts in the control room and are archived for post-mortem analysis - presented in a web-based timeline browser. We present the design of the DAQ-Expert and report on the operational experience since 2017, when it was first put into production.

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