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T: Fachverband Teilchenphysik

T 76: Search for Dark Matter 3

T 76.8: Vortrag

Mittwoch, 23. März 2022, 18:00–18:15, T-H35

Performance of different MET reconstruction methods in a monotop DM analysis — •Jost von den Driesch¹, Sebastian Wieland¹, Michael Waßmer¹, Nikita Shadskiy¹, Ulrich Husemann¹, Matteo Cremonesi², Lindsey Gray³, and Yihui Lai⁴ — ¹Institute of Experimental Particle Physics (ETP), Karlsruhe Institute of Technology (KIT) — ²University of Notre Dame (ND) — ³Fermilab — ⁴University of Maryland (UMD)

Missing transverse momentum (MET) is an important quantity in many analyses at hadron colliders. Especially Dark Matter (DM) analyses often make use of this quantity as DM particles leave the detector without interactions and therefore create large amounts of MET. However, due to its origin from non-detectable particles, MET cannot be measured directly, but must be estimated from the transverse momentum of all reconstructable particles.

Over the years, various MET reconstruction methods have been developed and applied at CMS. The latest approaches use machine learning methods, e.g. Convolutional Neural Networks (DeepMET) or Graph Neural Networks (GraphMET). Monte Carlo studies show an improvement of MET reconstruction performance by these novel reconstruction methods compared to the older ones. Yet, it remains unclear how large this effect will be in a full analysis.

This talk will introduce the aforementioned MET reconstruction methods and compare their expected impact on a monotop analysis, aimed at the search for Dark Matter in events with a single top quark and large MET.