NIPS 2015 Workshop (Williams) 15523 Applying (machine) Learning to Experimental Physics (ALEPH)…
Experimental physics actively develops frontiers of our knowledge of the Universe and ranges from macroscopic objects observed through telescopes to micro-world of particle interaction. In each field of study scientists go from raw measurements (celestial objects spectra or energies of detected particles inside collider detectors) to higher levels of the representation that are more suitable for further analysis and to human perception. Each measurement can be used for supporting or refuting certain theory that compete for predictive power and completeness. lt br gt lt br gt In many areas of physical experiments it assimilated computational paradigms a long time ago: both simulators and semi-automatic data analysis techniques have been applied widely for decades. In particular, nonparametric classification and regression are now routinely used as parts of the reconstruction (inference) chain. More recently, state-of-the-art budgeted learning techniques have also started to be used for real-time event selection on LHC. Nevertheless, most of these applications went largely unnoticed by the machine learning (ML) community. lt br gt lt br gt Our primary goal is to bring the Physics and ML communities together to initiate discussions on Physics-motivated problems and applications in ML. It is not unknown that the ML community is still largely untouched by the numerous learning challenges coming from Physics. We hope that as a result of this workshop (as well as a result of the Flavours of Physics challenge organized before the workshop and the new dataset that we shared in its scope that are to be discussed at the workshop), these problems will attract more attention from ML researchers.
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