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CosmoStat2013: Statistical challenges from large data sets in cosmology and particle physics

The purpose of this workshop is to bring together cosmologists, statisticians, experts in data mining and scientists from other fields, especially from particle physics (both phenomenologists and experimentalists), in order to provide a framework for a fruitful cross-fertilization of ideas across disciplines. THIS WORKSHOP IS ON INVITATION ONLY.
When 17 March 2013 08:00 PM to
22 March 2013 12:00 PM
Where Banff, Canada
Contact Name Roberto Trotta
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In 2013 both the cosmic microwave background satellite Planck and the Large Hadron Collider at CERN are expected to report first science results, which makes the proposed workshop highly timely. This conference therefore aims to leverage and combine the existing knowledge and to catalyze the building up of a new, integrated framework to help us along the path of understanding our world, what it is made of and where it came from.
The purpose of the proposed conference is to bring together cosmologists, statisticians, experts in data mining and scientists from other fields, especially from particle physics (both phenomenologists and experimentalists), in order to provide a framework for a fruitful cross-fertilization of ideas across disciplines. The main objective is to foster the exchange of ideas and concepts both within the respective research directions as well as in between them. This will involve critical assessment of algorithms and software packages developed in the 'other camp' which may not be known to workers outside the respective specialities. Ample time will be devoted to informal discussions, in order to leave room for the development of collaborations and the exchange of ideas.

In 2013 both the cosmic microwave background satellite Planck and the Large Hadron Collider at CERN are expected to report first science results, which makes the proposed workshop highly timely. This conference therefore aims to leverage and combine the existing knowledge and to catalyze the building up of a new, integrated framework to help us along the path of understanding our world, what it is made of and where it came from.The purpose of the proposed conference is to bring together cosmologists, statisticians, experts in data mining and scientists from other fields, especially from particle physics (both phenomenologists and experimentalists), in order to provide a framework for a fruitful cross-fertilization of ideas across disciplines. The main objective is to foster the exchange of ideas and concepts both within the respective research directions as well as in between them. This will involve critical assessment of algorithms and software packages developed in the 'other camp' which may not be known to workers outside the respective specialities. Ample time will be devoted to informal discussions, in order to leave room for the development of collaborations and the exchange of ideas.

One of the motivations for this workshop is the fact that cosmology and particle physics have developed two rather different approaches to the statistical analysis of their respective datasets, despite the commonalities in their respective goals. While particle physics is using mostly an approach based on the relative frequency of events (called 'frequentist'), which relies heavily on comparing observed data with simulations, the method of choice in cosmology is to translate data into probabilities representing 'degrees of beliefs' in the underlying models and their parameters ('Bayesian statistics').

The particular focus of this workshop will be to reflect upon the challenges in data reduction, statistical analysis and model inference posed by future cosmological observations and particle physics experiments, making best use of the experience accumulated over the years and drawing upon the expertise of leading statisticians. Examples of topics that will be addressed by the workshop include:

- sparse modeling and compressed sensing and their applications to astrophysical data analysis
- Bayesian computing and novel computational approaches for exploring large model spaces
- measuring performance of signal detection methods
- new data mining techniques for anomaly detection in extremely large data sets

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