| Popis: |
The ATLAS experiment at the Large Hadron Collider (LHC) op- erated very successfully in the years 2008 to 2018, in two periods identified as Run 1 and Run 2. ATLAS achieved an overall data-taking efficiency of 94%, largely constrained by the irreducible dead-time introduced to accommodate the limitations of the detector read-out electronics. Out of the 6% dead-time only about 15% could be attributed to the central trigger and DAQ system, and out of these, a negligible fraction was due to the Control and Configuration sub- system. Despite these achievements, and in order to improve even more the already excellent efficiency of the whole DAQ system in the coming Run 3, a new campaign of software updates was launched for the second long LHC shut- down (LS2). This paper presents, using a few selected examples, how the work was approached and which new technologies were introduced into the ATLAS DAQ system. Despite these being specific to this system, many solutions can be considered and adapted to different distributed DAQ systems. The ATLAS experiment at the Large Hadron Collider (LHC) operated very successfully in the years 2008 to 2018, in two periods identified as Run 1 and Run 2. ATLAS achieved an overall data-taking efficiency of 94%, largely constrained by the irreducible dead-time introduced to accommodate the limitations of the detector read-out electronics. Out of the 6% dead-time only about 15% could be attributed to the central trigger and DAQ system, and out of these, a negligible fraction was due to the Control and Configuration subsystem. Despite these achievements, and in order to improve even more the already excellent efficiency of the whole DAQ system in the coming Run 3, a new campaign of software updates was launched for the second long LHC shutdown (LS2). This paper presents, using a few selected examples, how the work was approached and which new technologies were introduced into the ATLAS Control and Configuration software. Despite these being specific to this system, many solutions can be considered and adapted to different distributed DAQ systems. |
