EPJ Web Conf.
Volume 214, 201923rd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2018)
|Number of page(s)||7|
|Section||T2 - Offline computing|
|Published online||17 September 2019|
New techniques for pile-up simulation in ATLAS
Jožef Stefan Institute,
* e-mail: firstname.lastname@example.org
Published online: 17 September 2019
The high-luminosity data produced by the LHC leads to many proton-proton interactions per beam crossing in ATLAS, known as pile-up. In order to understand the ATLAS data and extract physics results it is important to model these effects accurately in the simulation. As the pile-up rate continues to grow towards an eventual rate of 200 for the HL-LHC, this puts increasing demands on the computing resources required for the simulation and the current approach of simulating the pile-up interactions along with the hard-scatter for each Monte Carlo production is no longer feasible. The new ATLAS “overlay” approach to pile-up simulation is presented. Here a pre-combined set of minimum bias interactions, either from simulation or from real data, is created once and a single event drawn from this set is overlaid with the hard-scatter event being simulated. This leads to significant improvements in CPU time. This contribution will discuss the technical aspects of the implementation in the ATLAS simulation and production infrastructure and compare the performance, both in terms of computing and physics, to the previous approach.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.