2nd IML Machine Learning Workshop

Session

Session 6

11 Apr 2018, 14:00

500/1-001 - Main Auditorium (CERN)

Conveners

Session 6

• Rudiger Haake (CERN)
• Steven Randolph Schramm (Universite de Geneve (CH))

36. Tutorial: Advanced concepts

Michela Paganini (Yale University (US))

11/04/2018, 14:00

11. Direct Learning of Systematics-Aware Summary Statistics

Pablo De Castro Manzano (Universita e INFN, Padova (IT))

11/04/2018, 15:05

Complex machine learning tools, such as deep neural networks and gradient boosting algorithms, are increasingly being used to construct powerful discriminative features for High Energy Physics analyses. These methods are typically trained with simulated or auxiliary data samples by optimising some classification or regression surrogate objective. The learned feature representations are then...

12. Multivariate Analysis Techniques for charm reconstruction with ALICE

Chiara Zampolli (CERN)

11/04/2018, 15:30

ALICE is the experiment at the LHC dedicated to heavy-ion collisions. One of the key tools to investigate the strongly-interacting medium (Quark-Gluon Plasma, QGP) formed in heavy-ion collisions is the measurement of open-charm particle production. In particular, charmed baryons, such as ΛC, provide essential information for the understanding of charm thermalisation and hadronisation in the...

15. Classification of decays involving variable decay chains with convolutional architectures

Justin Tan (The University of Melbourne, Belle II)

11/04/2018, 16:30

Vidyo contribution

We present a technique to perform classification of decays that exhibit decay chains involving a variable number of particles, which include a broad class of $B$ meson decays sensitive to new physics. The utility of such decays as a probe of the Standard Model is dependent upon accurate determination of the decay rate, which is challenged by the combinatorial background...

4. Drones: Making faster and smarter decisions with software triggers

Sean Benson (Nikhef National institute for subatomic physics (NL))

11/04/2018, 16:55

Data collection rates in high energy physics (HEP), particularly those at the Large Hadron Collider (LHC) are a continuing challenge and require large amounts of computing power to handle. For example, at LHCb an event rate of 1 MHz is processed in a software-based trigger. The purpose of this trigger is to reduce the output data rate to manageable levels, which amounts to a reduction from 60...

37. Close-out and challenge results

Lorenzo Moneta (CERN), Markus Stoye (CERN), Paul Seyfert (CERN), Rudiger Haake (CERN), Steven Randolph Schramm (Universite de Geneve (CH))

11/04/2018, 17:20

46. TBA