Improving missing transverse momentum estimation with a deep neural network - Hayrapetyan,A; Makarenko,V; Tumasyan,A; Adam,W; Andrejkovic,JW; Benato,L; Bergauer,T; Dragicevic,M; Giordano,C; Hussain,PS; Jeitler,M; Krammer,N; et al. (Physical Review D, 2026)
At hadron colliders, the net transverse momentum of particles that do not interact with the detector (missing transverse momentum, <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" display="inline"> <a:msubsup> <a:mover accent="true"> <a:mi>p</a:mi> <a:mo stretchy="false">→</a:mo> </a:mover> <a:mi mathvariant="normal">T</a:mi> <a:mrow> <a:mi>miss</a:mi> </a:mrow> </a:msubsup> </a:math> ) is a crucial observable in many analyses. In the standard model, <f:math xmlns:f="http://www.w3.org/1998/Math/MathML" display="inline"> <f:msubsup> <f:mover accent="true"> <f:mi>p</f:mi> <f:mo stretchy="false">→</f:mo> </f:mover> <f:mi mathvariant="normal">T</f:mi> <f:mrow> <f:mi>miss</f:mi> </f:mrow> </f:msubsup> </f:math> originates from neutrinos. Many beyond-the-standard-model particles, such as dark matter candidates, are also expected to leave the experimental apparatus undetected. This paper presents a novel deep neural network based <k:math xmlns:k="http://www.w3.org/1998/Math/MathML" display="inline"> <k:ms