activity
20162024
most citedDirect detection of a break in the teraelectronvolt cosmic-ray spectrum of electrons and positrons

541 citations · 1.5k across the 15 of their papers we have counts for

collaborators

15 papers

astro-ph.HE2024

Observation of a spectral hardening in cosmic ray boron spectrum with the DAMPE space mission

DAMPE Collaboration, F. Alemanno, C. Altomare +143

Secondary cosmic ray fluxes are important probes of the propagation and interaction of high-energy particles in the Galaxy. Recent measurements of primary and secondary cosmic ray…

hep-ex2024★ 3 cited

Hadronic cross section measurements with the DAMPE space mission using 20GeV-10TeV cosmic-ray protons and He

F. Alemanno, Q. An, P. Azzarello +148

Precise direct cosmic-ray (CR) measurements provide an important probe to study the energetic particle sources in our Galaxy, and the interstellar environment through which these p…

astro-ph.IM2023★ 3 cited

HAGRID -- High Accuracy GRB Rapid Inference with Deep learning

Merlin Kole, Gilles Koziol, David Droz

Since their discoveries in 1967, Gamma-Ray Bursts (GRBs) continue to be one of the most researched objects in astrophysics. Multi-messenger observations are key to gaining a deeper…

astro-ph.HE2023★ 19 cited

Measurement of the cosmic p+He energy spectrum from 50 GeV to 0.5 PeV with the DAMPE space mission

DAMPE Collaboration, F. Alemanno, C. Altomare +152

Recent observations of the light component of the cosmic-ray spectrum have revealed unexpected features that motivate further and more precise measurements up to the highest energi…

astro-ph.HE2022★ 8 cited

Search for relativistic fractionally charged particles in space

DAMPE Collaboration, F. Alemanno, C. Altomare +148

More than a century after the performance of the oil drop experiment, the possible existence of fractionally charged particles FCP still remains unsettled. The search for FCPs is c…

astro-ph.IM2022★ 20 cited

A deep learning method for the trajectory reconstruction of cosmic rays with the DAMPE mission

Andrii Tykhonov, Andrii Kotenko, Paul Coppin +8

A deep learning method for the particle trajectory reconstruction with the DAMPE experiment is presented. The developed algorithms constitute the first fully machine-learned track…