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From the 1 of 10 linked papers with an AI index.

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20242026
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10 papers

astro-ph.CO2026

Dark Matter Constraints from Small-Scale Cosmic Structure

Ethan O. Nadler, Keir K. Rogers, Alex Drlica-Wagner

The paper reviews how observations of small‑scale cosmic structures, such as dwarf galaxies and the Lyman‑α forest, constrain the microphysical properties of dark matter models.

astro-ph.CO2026

Strongest constraints on dark acoustic oscillations from the Lyman-alpha forest

Zhihan Yuan, Caleb Gemmell, Keir K. Rogers +4

We set the first constraints on a small-scale dark acoustic oscillation (DAO) in the linear matter power spectrum arising from dark sector interactions, with a full forward model o…

astro-ph.CO2026

The Atacama Cosmology Telescope: Probing new signatures of ultralight axions with gravitational lensing

Alex Laguë, Keir K. Rogers, Mathew S. Madhavacheril +14

Ultralight axions (ULAs) are well-motivated dark matter particle candidates that arise in many extensions of the Standard Model of particle physics. ULAs with mass $m_\mathrm{a} \l…

hep-ph2026

The COSMIC WISPers White Paper: The physics case for Weakly Interacting Slim Particles

Ariel Arza, Deniz Aybas, Shyam Balaji +124

Axions and other very weakly interacting slim particles (WISPs), with masses below 1 GeV, arise naturally in many extensions of the Standard Model of particle physics. In particula…

hep-th2025

Bayesian inference on Calabi--Yau moduli spaces and the axiverse: experimental data meets string theory

Mudit Jain, Elijah Sheridan, David J. E. Marsh +3

We develop tools of Bayesian inference on the moduli space of Calabi--Yau (CY) manifolds. We sample from the invariant Weil--Petersson (WP) measure using Markov Chain Monte Carlo a…

astro-ph.CO2025

Prospects for disentangling dark matter with weak lensing

Calvin Preston, Keir K. Rogers, Alexandra Amon +1

We investigate the degeneracy between the effects of ultra-light axion dark matter and baryonic feedback in suppressing the matter power spectrum. We forecast that galaxy shear dat…