DISA Seminar December 6th: Machine Learning for Astroparticle Physics

13:39 by Diana Unander

Astroparticle physics is a sub-branch of Physics dealing with the detection of gamma-rays, neutrinos, gravitational waves and cosmic rays from the Universe. In this seminar, we will focus on the field of extragalactic gamma-ray astronomy.

The two main types of datasets in the field are those produced via simulations and those acquired via detector equipments (“real”), leading to a large amount of data to be calibrated, prepared, filtered, reconstructed and analysed. Simulations are especially needed in order to understand the sensitivity of the given equipment to a given searched physics “signal” and to prepare the data analysis procedure before looking at the real data. Decades of experience in data analysis in the field lead to the ability to publish solid results.

In this context, machine learning is giving a big boost in speeding up the data analysis procedures. The first successful applications of supervised Machine Learning in the field date back to the years 2010-2013 and concern classification and regression methods. Nowadays, there is a huge effort to exploit Deep Learning methods to achieve faster simulations and to improve the current data analysis methods, where physicists cannot “see” or “predict” any significant features describing the datasets.

I will present the current activities and future challenges of my research groups at Linnaeus University and at the University of Paris and, more generally, the challenges of the field.

Keywords: gamma-ray astronomy, supervised machine learning, event classification, feature regression


  1. Y. Becherini et al., 2011 (Astrop. Phys., Vol 34, 12, 2011, 858-870)
  2. Y. Becherini et al., 2012 (Gamma 2012)
  3. Y. Becherini et al., 2012 (arXiv:1211.5997) 
  4. M. Senniappan et al
  5. T. Bylund et al., 
Diana Unander

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