Connecting The Dots 2018

Session

Session3

21 Mar 2018, 09:00

Physics-Astronomy Auditorium A118 (University of Washington Seattle)

Conveners

Session3: Session3

• David Rousseau (LAL-Orsay, FR)

35. Fast automated analysis of strong gravitational lenses with convolutional neural networks

Dr Yashar Hezaveh (Stanford University)

21/03/2018, 09:00

6: Beyond the conventional tracking

Oral

Strong gravitational lensing is a phenomenon in which images of distant galaxies appear highly distorted due to the deflection of their light rays by the gravity of other intervening galaxies. We often see multiple distinct arc-shaped images of the background galaxy around the intervening (lens) galaxy, like images in a funhouse mirror. Strong lensing gives astrophysicist a unique opportunity...

14. Online Event Reconstruction in IceCube Using Deep Learning Techniques

Mr Mirco Hünnefeld (TU Dortmund, IceCube)

21/03/2018, 09:30

3: Machine learning approaches

Oral

The IceCube Neutrino Observatory is a Cherenkov detector deep in the Antarctic ice. Due to limited computational resources and the high data rate, only simplified reconstructions restricted to a small subset of data can be run on-site at the South Pole. However, in order to perform online analyses and to issue real-time alerts, fast and powerful reconstructions are desired.

Recent advances,...

4. Improving jet substructure performance in ATLAS with unified tracking and calorimeter inputs

Noemi Calace (Universite de Geneve (CH))

21/03/2018, 10:00

5: Advanced usage of tracks

Oral

Jet substructure techniques play a critical role in ATLAS in searches for new physics, and are being utilized in the trigger. They become increasingly important in detailed studies of the Standard Model, among them the inclusive search for the Higgs boson produced with high transverse momentum decaying to a bottom-antibottom quark pair. To date, ATLAS has mostly focused on the use of...

39. Online Multi-target Tracking using Recurrent Neural Networks

Hamid Rezatofighi

21/03/2018, 11:00

2: Real-time pattern recognition and fast tracking

Oral

We present a novel approach to online multi-target tracking
based on recurrent neural networks (RNNs). Tracking multiple
objects in real-world scenes involves many challenges,
including a) an a-priori unknown and time-varying number of
targets, b) a continuous state estimation of all present targets,
and c) a discrete combinatorial problem of data association.
Most previous methods involve...

15. Expected performance of tracking and vertexing with the HL-LHC ATLAS detector

Noemi Calace (Universite de Geneve (CH))

21/03/2018, 11:30

4: Performance evaluation

Oral

The High Luminosity LHC (HL-LHC) aims to increase the LHC data-set by an order of magnitude in order to increase its potential for discoveries. Starting from the middle of 2026, the HL-LHC is expected to reach the peak instantaneous luminosity of $7.5\cdot10^{34}cm^{-2}s^{-1}$ which corresponds to about 200 inelastic proton-proton collisions per beam crossing. To cope with the large radiation...

12. Implementation and Performance of FPGA based track fitting for the Atlas Fast TracKer

Rui Zou (University of Chicago (US))

21/03/2018, 12:00

2: Real-time pattern recognition and fast tracking

Oral

The Fast TracKer (FTK) within the ATLAS trigger system provides global track reconstruction for all events passing the ATLAS Level 1 trigger by dividing the detector into parallel processing pipelines that implement pattern matching in custom integrated circuits and data routing, reduction, and parameter extraction in FPGAs. In this presentation we will describe the implementation of a...