Special Session SS32
29 June 2021
Machine Learning and Visualisation in Data Intensive Era
Aims and scope
Future large scale surveys such as LSST, SKA, JWST and their ongoing pathfinders ZTF, LOFAR, MeerKAT, etc. have ushered astronomy in an era of data-driven science. The datasets obtained here could span up to exa-scales, and may contain an unprecedented number of both known and unknown astronomical objects. Machine learning (ML) techniques have been extensively deployed in recent years to mine and classify these objects. Visualisation (VIS) techniques are being developed to provide deeper statistical insights from complex, multi-dimensional datasets.
Lessons from the usage of current datasets and ML algorithms are destined to be transferred to future surveys. However, current techniques are more effective in case of uniform noise realisations, low data gaps, and well characterised astronomical objects. Further, tremendous data volumes of future surveys demand i) development of effective real time analyses involving ML and VIS techniques, and ii) a better understanding of transferring models from current surveys. Challenges inherent to exa-scale data sizes are unique to the astronomy community. Anticipating and resolving these challenges in a timely manner is crucial to maximizing scientific yield from future facilities.
In this session, we aim to invite speakers and to consider submissions that present cutting-edge techniques at the heart of drawing insight from large-scale astronomical data. We aim to:
- Inform newcomers of the current status and ongoing developments in the field.
- Enable researchers to demonstrate relevant software packages.
- Create awareness and recognise future needs of the community in light of current limitations of ML and VIS techniques.
- Machine Learning
- Data Visualisation
- Data Mining
- Large-Scale Surveys
- Eliot Ayache (University of Bath, UK)
- Amruta Jaodand (Caltech, US)
- Tanmoy Laskar (University of Bath, UK)
- John Wenskovitch (Virginia Tech & PNNL, US)
- Martijn Wilhelm (Universiteit Leiden, NL)
John Wenskovitch: johnwenskovitch @ gmail.com
Updated on Mon Feb 01 00:53:54 CET 2021