ML4Jets2020

Session

Applications

17 Jan 2020, 15:20

KC 802 (Kimmel Center for University Life)

Conveners

Applications

• Dan Guest (University of California Irvine (US))
• Christine Angela McLean (SUNY Buffalo)

56. Jet substructure tagging and pileup mitigation

Alejandro Gomez Espinosa (ETH Zurich (CH))

17/01/2020, 15:20

Recent advances in neural networks and harsh pileup conditions in the second half on LHC Run 2 with on average 38 PU interactions, have sparked significant developments in techniques for jet tagging and missing transverse momentum reconstruction. Through the study of jet substructure properties, jets originating from quarks, gluons, W/ Z/Higgs bosons, top quarks and pileup interactions are...

72. Machine learning approaches to the identification of jets originating from heavy-flavor quarks.

Philipp Windischhofer (University of Oxford (GB))

17/01/2020, 15:40

The identification of jets originating from heavy-flavor quarks (b-quark, c-quark) is central to the LHC physics program. High-performance heavy-flavor tagging is necessary both in precise standard model measurements as well as in searches for new physics. Jets containing heavy-flavor have a distinct characteristics, but the production rate of such jets is several orders of magnitude smaller...

4. Searching for long lived particles with a neural-network-based displaced jet tagger

Robert John Bainbridge (Imperial College (GB))

17/01/2020, 16:00

We present a neural-network-based tagger that is trained to identify the presence of displaced jets arising from the decays of new long-lived particle (LLP) states in data recorded by the CMS detector at the CERN LHC. Information from individual particles and secondary vertices within jets are refined through the use of convolutional networks before being combined with high-level engineered...

36. Jet or Event? - Physics at Future $e^-e^+$ Colliders

Mr Sijun Xu (Department of Physics, The Hong Kong University of Science and Technology)

17/01/2020, 16:20

Information loss caused by dimension reduction in jet clustering is one of the major limitations for the measurement precision of hadronic events at future $e^-e^+$ colliders, where the precision frontier of particle physics for next decades is expected to be defined. Such measurements are key for probing, e.g., the nature of Higgs boson, since the hadronic events are dominant in Higgs data....

30. The Di-Higgs Photography with Deep Neural Networks

Dr Jeong Han Kim (University of Notre Dame)

17/01/2020, 16:40

We search for a hint of new physics concealed in the structure of the Standard Model (SM) via double Higgs production. Focusing on a relatively overlooked bbWW* final state, we portray an entire final state using charged/neutral hadron, lepton, and reconstructed neutrino images. We design various types of residual neural networks (ResNet), which efficiently exploit the correlations among the...

42. Cornering charming Higgs decays

Mr Joseph Walker (University of Durham )

17/01/2020, 17:00

This talk discusses on how to identify events with fatjets from charming Higgs decays, $H\to cc$, at the LHC. To reduce the overwhelmingly large backgrounds and to reduce false positives, we consider applying a combination of jet shape observables and imaging techniques, using a selection of neural network architectures.

48. Using machine learning to constrain the Higgs total width

Dylan Sheldon Rankin (Massachusetts Inst. of Technology (US)), Cristina Ana Mantilla Suarez (Johns Hopkins University (US))

17/01/2020, 17:20

Despite the discovery of the Higgs boson decay in five separate channels many parameters of the Higgs boson remain largely unconstrained. In this paper, we present a new approach to constraining the Higgs total width by requiring the Higgs to be resolved as a single high pT jet and measuring the visible and partially visible Higgs boson cross section. This approach complements existing...