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Cosmology meets Machine Learning

NIPS 2011 Workshop, December 16th, Sierra Nevada, Spain

Description


Cosmology aims at the understanding of the universe and its evolution through scientific observation and experiment and hence addresses one of the most profound questions of human mankind. With the establishment of robotic telescopes and wide sky surveys cosmology already now faces the challenge of evaluating vast amount of data. Several projects will image large fractions of the sky in the next decade; for example the Dark Energy Survey will culminate in a catalogue of 300 million objects extracted from peta-bytes of observational data, while the Large Synoptic Survey Telescope is designed to image the entire observable Southern sky every few nights for 10 years. The importance of automatic data evaluation and analysis tools for the success of these surveys is undisputed.

Many problems in modern cosmological data analysis are tightly related to fundamental problems in machine learning, such as classifying stars and galaxies and cluster finding of dense galaxy populations. Other typical problems include data reduction, probability density estimation, how to deal with missing data and how to combine data from different surveys. An increasing part of modern cosmology aims at the development of new statistical data analysis tools and the study of their behaviour and systematics often not aware of recent developments in machine learning and computational statistics.

Therefore, the objectives of this workshop are two-fold:
  • To bring together experts from the Machine Learning and Computational Statistics community with experts in the field of cosmology to promote, discuss and explore the use of machine learning techniques in data analysis problems in cosmology and to advance the state of the art.
  • By presenting current approaches, their possible limitations, and open data analysis problems in cosmology to the NIPS community, this workshop aims to encourage scientific exchange and to foster collaborations among the workshop participants.

The workshop is held as a one-day workshop organised jointly by experts in the field of empirical inference and cosmology. The target group of participants are researchers working in the field of cosmological data analysis as well as researchers from the whole NIPS community sharing the interest in real-world applications in a fascinating, fast-progressing field of fundamental research. Due to the mixed participation of computer scientists and cosmologists the invited speakers will be asked to give talks with tutorial character and make the covered material accessible for both computer scientists and cosmologists.

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Sponsors

The workshop is funded as a PASCAL core workshop.


Header Image

The header image is a image collage by Michael Hirsch composed of a neural net imaged by Two-Photon Excitation Microscopy by Jochen Herms in collaboration with the Bioimaging Zentrum at the LMU M√ľnchen and part of a simulation of dark matter structure with new techniques for following the formation and evolution of the visible components (courtesy of Volker Springel).