Big data tools for physics and astronomy

Contribution List

3. Watch this space: emerging trends and techniques that could transform physics and astronomy

Kyle Stuart Cranmer (New York University (US))

19/06/2017, 09:00

13. Inter-experimental Machine Learning at the LHC

Dr Sergei Gleyzer (University of Florida (US))

19/06/2017, 09:45

15. Large Scale Machine Learning in Astronomy

Gieseke Fabian

19/06/2017, 10:15

17. GRID Processing of LOFAR spectroscopic and imaging surveys

Natalie Danezi (SurfSara)

19/06/2017, 11:05

18. HPC Processing and Deep Learning Techniques for Classifying Radio Bursts

Joeri van Leeuwen

19/06/2017, 11:35

14. Collaborative machine learning for science with OpenML

Prof. Joaquin Vanschoren (TU Eindhoven)

19/06/2017, 12:05

4. Amsterdam Data Science

Maarten de Rijke

19/06/2017, 14:00

5. eScience

Wilco Hazeleger

19/06/2017, 14:30

6. SurfSara

Maithili Kalamkar

19/06/2017, 15:00

7. Determining the origin of the Galactic Center excess using convolutional neural networks

Mr Hendriks Luc

19/06/2017, 16:00

8. Scaling up in complexity

Zahari Dimitrov Kassabov Zaharieva (University of Turin)

19/06/2017, 16:15

12. Good fits of gamma-ray data with high-dimensional modelling

Dr Emma Storm (UvA)

19/06/2017, 16:30

9. Generalizing LHC limits on Supersymmetry with Machine Learning

Mr Bob Stienen (IMAPP, Radboud University Nijmegen)

19/06/2017, 16:45

16. Wrap-up and next steps

Juan Rojo (VU Amsterdam and Nikhef)

19/06/2017, 17:00

10. Accelerating the BSM interpretation of LHC data with machine learning

Mr Sebastian Liem (GRAPPA, University of Amsterdam)

1. The proton structure with artificial neural networks.

Dr Juan Rojo (VU Amsterdam and Nikhef)

Contributed talks

We present how a model-independent and bias-free determination of the internal structure of the proton can be achieved by means of Artificial Neural Networks.