Volume 398 - The European Physical Society Conference on High Energy Physics (EPS-HEP2021) - T10: Searches for New Physics
METNet: A combined missing transverse momentum working point using a neural network with the ATLAS detector
B. Hodkinson
Full text: Not available
Abstract
In order to suppress pile-up effects and improve resolution, the ATLAS experiment at the LHC employs a suite of working points for missing transverse momentum ($p_\text{T}^\text{miss}$) reconstruction, and each is optimal for different event topologies and different beam conditions. A neural network (NN) can exploit various event properties to pick the optimal working point on an event-by-event basis, and also combine complementary information from each of the working points. The resulting regressed $p_\text{T}^\text{miss}$ (METNet) offers improved resolution and pile-up resistance across a number of different topologies compared to the current $p_\text{T}^\text{miss}$ working points. Additionally, by using the NN's confidence in its predictions, a machine learning-based $p_\text{T}^\text{miss}$ significance (`METNetSig') can be defined. This contribution presents simulation-based studies of the behaviour and performance of METNet and METNetSig for several topologies compared to current ATLAS $p_\text{T}^\text{miss}$ reconstruction methods.
How to cite

Metadata are provided both in "article" format (very similar to INSPIRE) as this helps creating very compact bibliographies which can be beneficial to authors and readers, and in "proceeding" format which is more detailed and complete.

Open Access