Aaron C. Vincent
Durham University
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Featured researches published by Aaron C. Vincent.
Physical Review D | 2015
Sergio Palomares-Ruiz; Olga Mena; Aaron C. Vincent
A full energy and flavor-dependent analysis of the three-year high-energy IceCube neutrino events is presented. By means of multidimensional fits, we derive the current preferred values of the high-energy neutrino flavor ratios, the normalization and spectral index of the astrophysical fluxes, and the expected atmospheric background events, including a prompt component. A crucial assumption resides on the choice of the energy interval used for the analyses, which significantly biases the results. When restricting ourselves to the ~30 TeV - 3 PeV energy range, which contains all the observed IceCube events, we find that the inclusion of the spectral information improves the fit to the canonical flavor composition at Earth, (1:1:1), with respect to a single-energy bin analysis. Increasing both the minimum and the maximum deposited energies has dramatic effects on the reconstructed flavor ratios as well as on the spectral index. Imposing a higher threshold of 60 TeV yields a slightly harder spectrum by allowing a larger muon neutrino component, since above this energy most atmospheric tracklike events are effectively removed. Extending the high-energy cutoff to fully cover the Glashow resonance region leads to a softer spectrum and a preference for tau neutrino dominance, as none of the expected electron antineutrino induced showers have been observed so far. The lack of showers at energies above 2 PeV may point to a broken power-law neutrino spectrum. Future data may confirm or falsify whether or not the recently discovered high-energy neutrino fluxes and the long-standing detected cosmic rays have a common origin.
Journal of Cosmology and Astroparticle Physics | 2015
Aaron C. Vincent; Enrique Fernández Martínez; Pilar Hernández; Olga Mena; M. Lattanzi
We employ state-of-the art cosmological observables including supernova surveys and BAO information to provide constraints on the mass and mixing angle of a non-resonantly produced sterile neutrino species, showing that cosmology can effectively rule out sterile neutrinos which decay between BBN and the present day. The decoupling of an additional heavy neutrino species can modify the time dependence of the Universes expansion between BBN and recombination and, in extreme cases, lead to an additional matter-dominated period; while this could naively lead to a younger Universe with a larger Hubble parameter, it could later be compensated by the extra radiation expected in the form of neutrinos from sterile decay. However, recombination-era observables including the Cosmic Microwave Background (CMB), the shift parameter
The Astrophysical Journal | 2016
G. Jóhannesson; R. Ruiz de Austri; Aaron C. Vincent; I. V. Moskalenko; Elena Orlando; T. A. Porter; A. W. Strong; Roberto Trotta; Farhan Feroz; P. B. Graff; M. Hobson
R_{CMB}
Physical Review D | 2010
James M. Cline; Aaron C. Vincent; Wei Xue
and the sound horizon
Physical Review Letters | 2014
Olga Mena; Sergio Palomares-Ruiz; Aaron C. Vincent
r_s
Journal of High Energy Physics | 2016
D. G. Cerdeno; Malcolm Fairbairn; Thomas Jubb; Pedro A. N. Machado; Aaron C. Vincent; Céline Bœhm
from Baryon Acoustic Oscillations (BAO) severely constrain this scenario. We self-consistently include the full time-evolution of the coupled sterile neutrino and standard model sectors in an MCMC, showing that if decay occurs after BBN, the sterile neutrino is essentially bounded by the constraint
Journal of Cosmology and Astroparticle Physics | 2012
Aaron C. Vincent; Pierrick Martin; James M. Cline
\sin^2\theta \lesssim 0.026 (m_s/\mathrm{eV})^{-2}
Journal of High Energy Physics | 2008
Aaron C. Vincent; James M. Cline
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Journal of Cosmology and Astroparticle Physics | 2014
Aaron C. Vincent; Pat Scott
We present the results of the most complete ever scan of the parameter space for cosmic ray (CR) injection and propagation. We perform a Bayesian search of the main GALPROP parameters, using the MultiNest nested sampling algorithm, augmented by the BAMBI neural network machine learning package. This is the first such study to separate out low-mass isotopes (
Physical Review D | 2016
Ryan J. Wilkinson; Aaron C. Vincent; Céline Bœhm; Christopher McCabe
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