Joint measurements of thermal Sunyaev-Zel'dovich and cosmic infrared background cross-correlations with cosmic shear

Physical Review D (2026)

Authors:

Amy Wayland, Adrien La Posta, David Alonso, Baptiste Jego, Matthieu Béthermin

Abstract:

The cross-correlation between weak lensing (WL) and the thermal Sunyaev-Zel'dovich (tSZ) effect probes the connection between the matter distribution and the thermal state of baryons at low redshift (z≲1). A key systematic in such measurements is contamination by extragalactic foregrounds, particularly the cosmic infrared background (CIB). Here we describe the CIB spectral energy distribution (SED) with a small number of physically motivated spectral parameters, calibrated against external measurements of the radiation field intensity in galaxies. Their uncertainty is then propagated into the recovered WL×tSZ signal in component separation at the level of the multi-frequency cross-power spectra rather than the map level. Applying this method to cosmic shear from the Dark Energy Survey and frequency maps from Planck, we show that the resulting measurements of the WL×tSZ power spectrum are insensitive to variations in the effective CIB spectral index, radio source contamination, and the scale and redshift dependence of the SED. The measurements are robust to realistic foreground uncertainties, supporting their use as probes of baryonic feedback and cosmology, and providing a framework for future high-precision surveys. We compare our measurements with the Flamingo suite of hydrodynamical simulations and find that they are compatible with the Flamingo predictions for a low amplitude of matter fluctuations, parametrised by S8, strongly disfavouring the larger S8 preferred by Planck. This result is independent of the impact of baryonic feedback as modelled in Flamingo.

The moving lens effect: analytical modelling and foreground suppression

Journal of Cosmology and Astroparticle Physics (2026)

Authors:

Amy Wayland, David Alonso, and William Coulton

Abstract:

The moving lens (ML) effect is a secondary anisotropy of the cosmic microwave background (CMB) generated by the transverse motion of gravitational potentials, providing a direct probe of the large-scale cosmic velocity field. Its detection relies on cross-correlating a CMB map with the transverse galaxy momentum field, constructed from the galaxy overdensity and a velocity field reconstructed from it. Restricting the reconstruction to large-scale modes strongly suppresses contamination from small-scale astrophysical foregrounds while preserving the ML signal. In this work, we develop a theoretical framework for the ML estimator and its foreground contamination. We show that the signal and foregrounds have a distinct dependence on the direction of the long-wavelength mode represented by the reconstructed galaxy velocity. In the squeezed limit enforced by the velocity reconstruction filter, the bispectrum sourcing the foreground correlation becomes independent of this direction, causing its leading contribution to vanish after angular averaging. In turn, the ML signal survives by matching this directional dependence, with its amplitude reduced only by the filtered velocity variance. Using the halo model, we derive the leading corrections beyond the squeezed limit and show that the residual contamination remains parametrically suppressed. Finally, we model the cross-correlation exactly in the curved sky, and show that it is sourced solely by the longitudinal component of the galaxy momentum field, in the form of a spin-1 E-mode, with all other contributions either strongly suppressed on small scales or exactly zero.

A short introduction to cosmology and its current status

SciPost Physics Lecture Notes Stichting SciPost (2025) 109

Authors:

Pedro G Ferreira, Alexander Roskill

Abstract:

The current cosmological model, known as the \Lambda Λ -Cold Dark Matter model (or \Lambda Λ 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 Oxford University Press (OUP) (2025) staf2250

Authors:

James Pearson, Hugh Dickinson, Stephen Serjeant, Mike Walmsley, Lucy Fortson, Sandor Kruk, Karen L Masters, Brooke D Simmons, RJ Smethurst, Chris Lintott, Lukas Zalesky, Conor McPartland, John R Weaver, Sune Toft, Dave Sanders, Nima Chartab, Henry Joy McCracken, Bahram Mobasher, Istvan Szapudi, Noah East, Wynne Turner, Matthew Malkan, William J Pearson, Tomotsugu Goto, Nagisa Oi

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)

Authors:

Deaglan J Bartlett, Harry Desmond, Pedro G Ferreira, Gabriel Kronberger