# Loose Kinematic Reconstruction (LKR)

In the dilepton channel, two neutrinos escape undetected, so reconstructing top-quark-pair kinematics has long relied on analytic approximations. Using a 7 TeV simulated sample from CMS Open Data, researchers first refined the loose kinematic reconstruction into LKRv3, then trained a residual multilayer perceptron and a conditional normalizing flow to regress the top-pair four-momenta directly from visible particles; relative to LKRv3, the two networks improve the resolution of the invariant mass, transverse momentum and rapidity by about 30%, 20% and 15%, reaching 30 GeV invariant-mass resolution near the production threshold.

In the dilepton channel, two neutrinos escape undetected, so reconstructing top-quark-pair kinematics has long relied on analytic approximations. Using a 7 TeV simulated sample from CMS Open Data, researchers first refined the loose kinematic reconstruction into LKRv3, then trained a residual multilayer perceptron and a conditional normalizing flow to regress the top-pair four-momenta directly from visible particles; relative to LKRv3, the two networks improve the resolution of the invariant mass, transverse momentum and rapidity by about 30%, 20% and 15%, reaching 30 GeV invariant-mass resolution near the production threshold.