The High Luminosity LHC (HL-LHC) represents an unprecedented computing challenge. For the program to succeed the current estimates from the LHC experiments for the amount of processing and storage required are roughly 50 times more than are currently deployed. Although some of the increased capacity will be provided by technology improvements over time, the computing budget is expected to be...
Abstract: Data is being created at an alarming rate and we need faster and more efficient machines and algorithms to make sense of this data. Though we will still need the performance of traditional high performance computing, there are characteristics and relationships in the data that are needing more non-traditional computing approaches for greater efficiency. This need is also coupled with...
Moderator: Dr Peter Braam
Panelists: Dr Maria Girone, Dr Yujia Li, Dr Bojan Nikolic, Stephen Pawlowski
Dr. Vladimir “Vava” Gligorov, Research Scientist at CNRS/LPNHE
Abstract: LHCb is one of the four major experiments at the Large Hadron Collider (LHC) at CERN. It searches for particles and forces beyond our current physics theories, in particular
by making highly precise measurements of the properties of the particles produced in the LHC collisions. Doing so requires analyzing an enormous...
Moderator: Dr Alan Barr
Panelists: Dr. David Rousseau, Dr. Vladimir "Vava" Gligorov, Dr. Maurizio Pierini, Dr. Jennifer Thompson
Abstract: Over recent decades cosmology has transitioned from a data-poor to a data-rich field, which has lead to dramatic improvements in our understanding of the cosmic evolution of our Universe. Nevertheless, we remain ignorant of many aspects of the scenario that has been revealed. Little is known about the fundamental physics of structure formation in the early Universe or the formation...
Moderator: Prof. Richard McMahon
Panelists: Dr. Shirley Ho
Professor Terry Lyons FLSW FRSE FRS, Wallis Professor of Mathematics, Mathematical Institute, University of Oxford
Abstract: It often happens that measurements have redundant information over and above the invariants of interest; we might measure a rigid object in cartesian co-ordinates although we are only interested in the shape. Representing in an invariant way that does not carry...
Abstract: Machine learning techniques have proven to be increasingly useful in astronomical applications over the last few years, for example in object classification, estimating redshifts and data mining. A topic of current interest is to classify radio galaxy morphology, as it gives us insight into the nature of the AGN, surrounding environment and evolution of the host galaxy. The task of...
Abstract: In this talk, we consider the supervised learning problem where the explanatory variable is a data stream. We provide an approach based on identifying carefully chosen features of the stream which allows linear regression to be used to characterise the functional relationship between explanatory variables and the conditional distribution of the response; the methods used to develop...
Abstract: Breakthrough Listen is the largest effort to date to search for techno-signatures from extraterrestrial civilizations. We use extensive computing power to search at high frequency and time resolution for transients events in petabytes of observational data from the Green Bank Telescope, Parkes Telescope, LOFAR, and soon MeerKAT. Given the diverse manifestations of transient signals...
