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A Bayesian Unfolding Method Applied to the PAMELA Experiment

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If you have a question about this talk, please contact David MacKay.

PAMELA is a satellite-borne experiment that is going to study cosmic rays in a wide energy range and for a very long period (approx. 3 yrs), with an unprecedented precision. Its main scientific objectives are the indirect study of possible dark matter candidates and the search for antiparticles coming directly from antimatter domains.

These scientific tasks require a good spectrum reconstruction and particle separation in order to appreciate weak signals over a strong background (e.g positrons over protons) that could be hints for the phenomena PAMELA is going to look for. For inference problems like these, we think the Bayesian statistics (or “probabilistic approach”) offers a better tool than standard statistics, both conceptually and practically.

We present some results, based on Montecarlo simulations, that shows how we applied an unfolding algorithm (D’Agostini 1995) to reconstruct a positrons spectrum over a stronger protons background. We used only observables of a subset of PAMELA ’s, obtaining good results anyway. We show also how we solved some practical problems we faced with during our work.

This talk is part of the Inference Group series.

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