Speaker
Description
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.
| First author | Benjamin Haslum Hodkinson |
|---|---|
| bh490@cam.ac.uk | |
| Collaboration / Activity | ATLAS |
