Probing baryonic feedback with fast radio bursts: joint analyses with cosmic shear and galaxy clustering
Monthly notices of the Royal Astronomical Society (2026)
Abstract:
Cosmological inference from weak lensing (WL) surveys is increasingly limited by uncertainties in baryonic physics, which suppress the non-linear matter power spectrum on small scales. Multi-probe analyses that incorporate complementary tracers of the gas distribution around haloes offer a pathway to calibrate these effects and recover unbiased cosmological information. In this work, we forecast the constraining power of a joint analysis combining fiducial data from a Stage-IV WL survey with measurements of the dispersion measure from fast radio bursts (FRBs). We evaluate the ability of this approach to simultaneously constrain cosmological parameters and the astrophysical processes governing baryonic feedback, and we quantify the impact of key FRB systematics, including redshift uncertainties and source clustering. We find that, even after accounting for these effects, a 3×2-point analysis of WL and FRBs significantly improves cosmological constraints, reducing the degradation factor on S8 by ∼80% compared to WL alone. We further show that FRBs alone are sensitive only to a degenerate combination of the key baryonic parameters, log10Mc and ηb, and that the inclusion of WL measurements breaks this degeneracy. Finally, we extend our framework to incorporate galaxy clustering measurements using Luminous Red Galaxy and Emission Line Galaxy samples, performing a unified 6×2-point analysis of WL, dispersion measures of FRBs, and galaxy clustering. While this combined approach tightens constraints on Ωm and log10Mc, it does not lead to a significant improvement in S8 constraints beyond those obtained from WL and FRBs alone.
A short introduction to cosmology and its current status
SciPost Physics Lecture Notes Stichting SciPost (2025) 109
Abstract:
The current cosmological model, known as the \Lambda -Cold Dark Matter model (or \Lambda CDM for short) is one of the most astonishing accomplishments of contemporary theoretical physics. It is a well-defined mathematical model which depends on very few ingredients and parameters and is able to make a range of predictions and postdictions with astonishing accuracy. It is built out of well-known physics – general relativity, quantum mechanics and atomic physics, statistical mechanics and thermodynamics – and predicts the existence of new, unseen components. Again and again it has been shown to fit new data sets with remarkable precision. Despite these successes, we have yet to understand the unseen components of the Universe and there has been evidence for inconsistencies in the model. In these lectures, we lay the foundations of modern cosmology.Galaxy Zoo: Cosmic Dawn – morphological classifications for over 41 000 galaxies in the Euclid Deep Field North from the Hawaii Two-0 Cosmic Dawn survey
Monthly Notices of the Royal Astronomical Society 91̽»¨ University Press (OUP) (2025) staf2250
Abstract:
Abstract We present morphological classifications of over 41 000 galaxies out to zphot ∼ 2.5 across six square degrees of the Euclid Deep Field North (EDFN) from the Hawaii Twenty Square Degree (H20) survey, a part of the wider Cosmic Dawn survey. Galaxy Zoo citizen scientists play a crucial role in the examination of large astronomical data sets through crowdsourced data mining of extragalactic imaging. This iteration, Galaxy Zoo: Cosmic Dawn (GZCD), saw tens of thousands of volunteers and the deep learning foundation model Zoobot collectively classify objects in ultra-deep multiband Hyper Suprime-Cam (HSC) imaging down to a depth of mHSC − i = 21.5. Here, we present the details and general analysis of this iteration, including the use of Zoobot in an active learning cycle to improve both model performance and volunteer experience, as well as the discovery of 51 new gravitational lenses in the EDFN. We also announce the public data release of the classifications for over 45 000 subjects, including more than 41 000 galaxies (median zphot of 0.42 ± 0.23), along with their associated image cutouts. This data set provides a valuable opportunity for follow-up imaging of objects in the EDFN as well as acting as a truth set for training deep learning models for application to ground-based surveys like that of the Ultraviolet Near-Infrared Optical Northern Survey (UNIONS) collaboration and the newly operational Vera C. Rubin Observatory.Introduction to Symbolic Regression in the Physical Sciences
(2025)
Strong Bars, Strong Inflow: The Effect of Bar Strength on Gas Inflow
Research Notes of the American Astronomical Society IOP Publishing 9:12 (2025) 341