Paleo-detectors can achieve high sensitivity to sub-GeV dark matter boosted by cosmic rays and supernovae, covering previously inaccessible parameter space with orders of magnitude better reach than current experiments.
Adrianiet al.(PAMELA), Nature458, 607 (2009), arXiv:0810.4995 [astro-ph]
4 Pith papers cite this work. Polarity classification is still indexing.
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The bow shock pulsar wind nebula around PSR J0437-4715 explains the GeV-TeV positron excess and hundreds-of-GeV antiproton flux with an energy-independent ratio by using 25% of the pulsar's wind power.
Updated predictions for cosmic antinuclei fluxes from dark matter yield tighter upper limits on light DM annihilation cross sections from AMS-02 antiproton data and show that GAPS could improve those limits by up to an order of magnitude below 50 GeV.
A review summarizing machine learning methods for multi-messenger probes of dark matter and new physics, with a proposed plan for future integrated analyses.
citing papers explorer
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Probing Cosmic-Ray-Boosted and Supernova-Sourced Sub-GeV Dark Matter with Paleo-Detectors
Paleo-detectors can achieve high sensitivity to sub-GeV dark matter boosted by cosmic rays and supernovae, covering previously inaccessible parameter space with orders of magnitude better reach than current experiments.
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On the contribution of the bow shock pulsar wind nebula PSR J0437-4715 to the observed fluxes of GeV-TeV positrons and antiprotons
The bow shock pulsar wind nebula around PSR J0437-4715 explains the GeV-TeV positron excess and hundreds-of-GeV antiproton flux with an energy-independent ratio by using 25% of the pulsar's wind power.
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Revisiting predictions for cosmic-ray antinucleon fluxes from Galactic Dark Matter
Updated predictions for cosmic antinuclei fluxes from dark matter yield tighter upper limits on light DM annihilation cross sections from AMS-02 antiproton data and show that GAPS could improve those limits by up to an order of magnitude below 50 GeV.
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Machine Learning for Multi-messenger Probes of New Physics and Cosmology: A Review and Perspective
A review summarizing machine learning methods for multi-messenger probes of dark matter and new physics, with a proposed plan for future integrated analyses.