Detection of the characteristic pion-decay signature in supernova remnants
Cosmic rays are particles (mostly protons) accelerated to relativistic speeds. Despite wide agreement that supernova remnants (SNRs) are the sources of galactic cosmic rays, unequivocal evidence for the acceleration of protons in these objects is still lacking. When accelerated protons encounter int...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
15 February 2013
|
| In: |
Science
Year: 2013, Jahrgang: 339, Heft: 6121, Pages: 807-811 |
| ISSN: | 1095-9203 |
| DOI: | 10.1126/science.1231160 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1126/science.1231160 Verlag, lizenzpflichtig, Volltext: https://science.sciencemag.org/content/339/6121/807 
|
| Verfasserangaben: | M. Ackermann, M.-H. Grondin, und weitere 167 |
| Zusammenfassung: | Cosmic rays are particles (mostly protons) accelerated to relativistic speeds. Despite wide agreement that supernova remnants (SNRs) are the sources of galactic cosmic rays, unequivocal evidence for the acceleration of protons in these objects is still lacking. When accelerated protons encounter interstellar material, they produce neutral pions, which in turn decay into gamma rays. This offers a compelling way to detect the acceleration sites of protons. The identification of pion-decay gamma rays has been difficult because high-energy electrons also produce gamma rays via bremsstrahlung and inverse Compton scattering. We detected the characteristic pion-decay feature in the gamma-ray spectra of two SNRs, IC 443 and W44, with the Fermi Large Area Telescope. This detection provides direct evidence that cosmic-ray protons are accelerated in SNRs. - The gamma-ray spectra of two supernova remnants in our Galaxy show the signature of proton acceleration. - The gamma-ray spectra of two supernova remnants in our Galaxy show the signature of proton acceleration. |
|---|---|
| Beschreibung: | Gesehen am 05.05.2021 |
| Beschreibung: | Online Resource |
| ISSN: | 1095-9203 |
| DOI: | 10.1126/science.1231160 |