Particle Physics Seminar

"Stop(Zh) Neural Networks! A case study in 3rd generation SUSY searches using Machine Learning"

Presented by Gabriele D'Amen, BNL

Thursday, June 11, 2020, 3:00 pm — Webcast

The widespread use of Machine Learning techniques in the high energy physics community is driving the creation of tools to simplify the implementation of Neural Network methods in a wide variety of analyses. We have developed an artificial neural network framework (HTag) to identify bb pairs coming from the decay of Higgs bosons in events with large jet multiplicities. The use of HTag is expected to improve reconstruction efficiency in a wide kinematic region and to reduce invariant mass bias with respect to traditional reconstruction methods. Information reconstructed with HTag has been used in a search for direct top squark pair production in events with missing transverse momentum plus either a pair of jets consistent with the Standard Model Higgs boson decay to b-quarks or a same-flavour opposite-sign dilepton pair with invariant mass consistent with a Z boson. The analysis is performed using the proton-proton collision data at √s=13 TeV collected by the ATLAS experiment during the LHC Run-2, corresponding to an integrated luminosity of 139 fb−1. No excess is observed in the data with respect to the Standard Model predictions. Zoom information: Meeting ID 974 2063 6116 Meeting Password 040606

Hosted by: Viviana Cavaliere

More Information

15777  |  INT/EXT  |  Events Calendar


Not all computers/devices will add this event to your calendar automatically.

A calendar event file named "calendar.ics" will be placed in your downloads location. Depending on how your device/computer is configured, you may have to locate this file and double click on it to add the event to your calendar.

Event dates, times, and locations are subject to change. Event details will not be updated automatically once you add this event to your own calendar. Check the Lab's Events Calendar to ensure that you have the latest event information.