Dark Matter @ LHC 2020 (DM@LHC)

Session

Thursday

07

4 Jun 2020, 14:00

Conveners

Thursday: Session 5: Cross-experimental topics

• Filippo Sala
• Giorgio Busoni (MPI-HD)

Thursday: Session 6: Analysis tools

• Steven Schramm (Universite de Geneve (CH))
• Sarah Malik (Imperial College (GB))

26. Dark Matter Capture in Neutron Stars (15'+5')

Nicole Bell (University of Melbourne)

04/06/2020, 14:00

Talk

29. Gravitational waves and dark matter (15'+5')

Toby Opferkuch (CERN)

04/06/2020, 14:20

43. Robust cosmological constraints on axion-like particles (8'+2')

Paul Frederik Depta (Deutsches Elektronen-Synchrotron (DESY))

04/06/2020, 14:40

Talk

Axion-like particles with masses in the keV-GeV range have a profound impact on the cosmological evolution of our Universe, in particular on the abundance of light elements produced during Big Bang Nucleosynthesis. The resulting limits are complementary to searches in the laboratory and provide valuable additional information regarding the validity of a given point in parameter space. A...

27. SUSY dark matter searches (15'+5')

Sicheng Wang (Univ. of California Santa Barbara (US))

04/06/2020, 14:50

Talk

28. Global fits of supersymmetry - can we still have light neutralino DM? (15'+5')

Are Raklev (University of Oslo (NO))

04/06/2020, 15:10

Talk

46. Conference picture!

04/06/2020, 15:30

30. Performance improvements at ATLAS (15'+5')

Jeanette Miriam Lorenz (Ludwig Maximilians Universitat (DE))

04/06/2020, 16:00

31. Object performance improvements at CMS (15'+5')

Laurent Thomas (Universite Libre de Bruxelles (BE)), Laurent Thomas (Universite Libre de Bruxelles (BE))

04/06/2020, 16:20

32. ML approaches in phenomenology (15'+5')

Sascha Caron (Nikhef National institute for subatomic physics (NL))

04/06/2020, 16:40

33. ML for Jet Tagging and Event Classification on ATLAS and CMS (15'+5')

Michael Aaron Kagan (SLAC National Accelerator Laboratory (US))

04/06/2020, 17:00

5. A novel density estimator and its use for LHC signal detection (8'+2')

Alessandro Morandini (SISSA)

04/06/2020, 17:20

Talk

We introduce a new density estimator based on Markov Chains. This estimator presents several benefits with respect to the usual ones and can be used straightforwardly in all density-based approaches to data science. After showing its consistency, we will present some promising results when applied to general scope datasets. Finally, we perform a preliminary analysis of a subset of the latest...