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The University of Southampton
The Alan Turing Institute

Data Science Seminar: The Data Science Revolution in Physics and Astronomy Event

Time:
11:00 - 12:30
Date:
12 October 2016
Venue:
Room 3077, Building 32, Highfield Campus, University of Southampton SO17 1BJ

For more information regarding this event, please email Professor Elena Simperl at E.Simperl@soton.ac.uk .

Event details

Heading into the 2020s, Physics and Astronomy are undergoing experimental revolutions that will reshape our picture of the fabric of the Universe. The Large Hadron Collider (LHC), the largest particle physics project in the world, produces 30 petabytes of data annually that need to be sifted through, analysed, and modelled. In astrophysics, the Large Synoptic Survey Telescope (LSST) will be taking a high-resolution image of the full sky every 3 days, leading to data rates of 30 terabytes per night over ten years. These experiments endeavour to answer the question why 96% of the content of the universe currently elude our physical understanding. Both the LHC and LSST share the 5-dimensional nature of their data, with position, energy and time being the fundamental axes. This talk will present an overview of the experiments and data that is gathered, and outlines the challenges in extracting information. Common strategies employed are very similar to industrial data science problems (e.g., data filtering, machine learning, statistical interpretation) and provide a seed for exchange of knowledge between academia and industry.

Speaker information

Professor Mark Sullivan,is a Professor of Astrophysics in the Department of Physics and Astronomy. Mark completed his PhD at Cambridge, and following postdoctoral study in Durham, Toronto and Oxford, now leads a research group at Southampton studying dark energy using exploding stars called "type Ia supernovae". Mark has many years’ experience of research that involves repeatedly imaging the night sky to track the arrival of transient objects, involving significant challenges in data handling, processing, classification and analysis.

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