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In order to suppress pile-up effects and improve the resolution, ATLAS employs a suite of working points for missing transverse momentum (pTmiss) reconstruction, and each is optimal for different event topologies and different beam conditions. A neural network (NN) can exploit various event properties to combine complementary information from each of the working points on an event-by-event basis. The resulting regressed pTmiss (`METNet') offers improved resolution and pile-up resilience across a number of different topologies compared to the current pTmiss working points. Additionally, by using the NN's confidence in its predictions, a machine learning-based pTmiss significance (`METNetSig') can be defined. This note presents simulation-based studies of the behaviour and performance of METNet and METNetSig for several topologies compared to current ATLAS pTmiss reconstruction methods.

We predict the range of proper motions of 19 satellite galaxies of M31 that would rotationally stabilize the M31 plane of satellites consisting of 15–20 members as identified by Ibata et al. Our prediction is based purely on the current positions and line-of-sight velocities of these satellites and the assumption that the plane is not a transient feature. These predictions are therefore independent of the current debate about the formation history of this plane. We further comment on the feasibility of measuring these proper motions with future observations by the THEIA satellite mission as well as the currently planned observations by HST and JWST.