output
19922026
most citedThe Cosmic Linear Anisotropy Solving System (CLASS) II: Approximation schemes

2.3k citations

1,438 papers

astro-ph.CO2026

In-depth analysis of the clustering of dark matter particles around primordial black holes. Part III: CMB constraints

Julien Lavalle, Vivian Poulin, Pierre Salati

In a mixed dark matter scenario in which primordial black holes (PBHs) would co-exist with thermally produced self-annihilating particles, one expects the former to be surrounded b…

astro-ph.CO2026

Euclid preparation. Galaxy power spectrum and bispectrum modelling

Euclid Collaboration, K. Pardede, A. Eggemeier +306

Higher-order correlation functions of the large-scale galaxy distribution offer access to information beyond that contained in standard 2-point statistics such as the power spectru…

astro-ph.HE2026

Pulsar Discoveries from the TRAPUM UHF Survey of Fermi-LAT Sources

Tinn Thongmeearkom, Colin J. Clark, Rene P. Breton +25

The Fermi Large Area Telescope (LAT) provides advantages for radio pulsar searches by enabling efficient target selection. We can confidently point radio telescopes to the position…

astro-ph.HE2026

Search for TeV emission from spider millisecond pulsars with HAWC

R. Alfaro, E. Anita-Rangel, M. Araya +90

Millisecond pulsars (MSPs) are observed to emit multi-wavelength radiation, from radio to GeV. Spider MSPs, which interact with their low-mass companion in close orbit (orbital per…

hep-ph2026

A framework for missing-energy searches with anomalous light vectors

Luca Di Luzio, Marco Nardecchia, Stefano Scacco +1

We study light spin-1 gauge bosons coupled to electroweak-anomalous currents. For generic charge assignments, anomaly cancellation requires new fermions (anomalons) that are chiral…

astro-ph.CO2026★ 1 cited

Towards an optimal extraction of cosmological parameters from galaxy cluster surveys using convolutional neural networks

Iñigo Sáez-Casares, Matteo Calabrese, Davide Bianchi +4

The possibility to constrain cosmological parameters from galaxy surveys using field-level machine learning methods that bypass traditional summary statistics analyses, depends cru…