Sterile Neutrino Dark Matter - Merle, Alexander
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This book is a new look at one of the hottest topics in contemporary science, Dark Matter. It is the pioneering text dedicated to sterile neutrinos as candidate particles for Dark Matter, challenging some of the standard assumptions which may be true for some Dark Matter candidates but not for all. So, this can be seen either as an introduction to a specialized topic or an out-of-the-box introduction to the field of Dark Matter in general. No matter if you are a theoretical particle physicist, an observational astronomer, or a ground based experimentalist, no matter if you are a grad student…mehr

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Produktbeschreibung
This book is a new look at one of the hottest topics in contemporary science, Dark Matter. It is the pioneering text dedicated to sterile neutrinos as candidate particles for Dark Matter, challenging some of the standard assumptions which may be true for some Dark Matter candidates but not for all. So, this can be seen either as an introduction to a specialized topic or an out-of-the-box introduction to the field of Dark Matter in general. No matter if you are a theoretical particle physicist, an observational astronomer, or a ground based experimentalist, no matter if you are a grad student or an active researcher, you can benefit from this text, for a simple reason: a non-standard candidate for Dark Matter can teach you a lot about what we truly know about our standard picture of how the Universe works.
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Autorenporträt
Alexandre Merle obtained his Ph.D. from Heidelberg University in 2009. He is currently in a Senior Postdoc position at the Max Planck Institute for Physics. His main research is theoretical elementary particle physics and cosmology, with a particular focus on neutrinos, Dark Matter, and their interconnections. He has written more than 60 papers on various topics; is an active contributor to the field of keV sterile neutrino Dark Matter, having discovered the production mechanism (FIMP scalar decay) that is currently in best agreement with data.