Improving ATLAS Hadronic Object Performance with ML/AI Algorithms

Hadronic object reconstruction is one of the most promising settings for cutting-edge machine learning and artificial intelligence algorithms at the LHC. In this contribution, selected highlights of ML/AI applications by ATLAS to particle and boosted-object identification, MET reconstruction and other tasks are presented.Hadronic object reconstruction is one of the most promising settings for cutting-edge machine learning and artificial intelligence algorithms at the LHC. In this contribution, selected highlights of ML/AI applications by ATLAS to particle and boosted-object identification, MET reconstruction and other tasks are presented

METNet: A combined missing transverse momentum working point using a neural network with the ATLAS detector

In order to suppress pile-up effects and improve resolution, ATLAS 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 allows to 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 poster 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

METNet: A combined missing transverse momentum working point using a neural network with the ATLAS detector

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

Improving ATLAS Hadronic Object Performance with ML/AI Algorithms

Hadronic object reconstruction is one of the most promising settings for cutting-edge machine learning and artificial intelligence algorithms at the LHC. In this contribution, selected highlights of ML/AI applications by ATLAS to particle and boosted-object identification, MET reconstruction and other tasks will be presented