Forecasting local primordial non-Gaussianities from UNIONS Lyman-break galaxies and Planck CMB lensing

¹ Université Paris-Saclay, CEA, IRFU, 91191 Gif-sur-Yvette, France
² IFAE, The Barcelona Institute of Science and Technology, Campus UAB, 08193 Bellaterra, Barcelona, Spain
³ Ruhr University Bochum, Faculty of Physics and Astronomy, Astronomical Institute, GCCL, 44780 Bochum, Germany
⁴ Perimeter Institute for Theoretical Physics, 31 Caroline St. North, Waterloo, ON N2L 2Y5, Canada
⁵ Department of Physics and Astronomy, University of Waterloo, 200 University Ave W, Waterloo, ON N2L 3G1, Canada
⁶ Institute for Astronomy, University of Hawaii, 2680 Woodlawn Drive, Honolulu, HI 96822, USA
⁷ National Research Council Herzberg Astronomy and Astrophysics, 5071 West Saanich Road, Victoria, BC V8Z 6M7, Canada
⁸ Department of Computer Science, University of British Columbia, 2366 Main Mall, Vancouver, BC V6T 1Z4, Canada

^★ Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Received: 3 December 2025
Accepted: 26 March 2026

Abstract

Context. Local primordial non-Gaussianities, characterized by the parameter f_NL^loc, provide a powerful window into the physics of inflation. Cross-correlating high-redshift tracer samples with the Cosmic Microwave Background (CMB) lensing potential offers a particularly robust probe of f_NL^loc, mitigating imaging systematics that typically affect large-scale measurements from tracer auto-spectra. In this context, the Ultraviolet Near Infrared Optical Northern Survey (UNIONS) enables the selection of u-dropout high-redshift Lyman-break galaxies (LBGs).

Aims. We aim to forecast the expected precision on f_NL^loc that is achievable from analyzing the cross-correlation power spectrum between the distribution of UNIONS-selected LBGs and the CMB lensing potential measured by the Planck satellite.

Methods. We performed a Markov chain Monte Carlo forecast to estimate the uncertainties on f_NL^loc and on a galaxy bias parameter b₀, which captured our uncertainty in the tracer bias.

Results. We forecast σ(f_NL^loc) = 34 for an idealized photometric sample of r < 24.3 LBGs selected with a Random Forest classification algorithm from UNIONS-like ugriz imaging, with a resulting surface density of 1100 deg⁻² over 3730 deg². This precision can be improved to σ(f_NL^loc) = 20 after spectroscopic follow-up with the Dark Energy Spectroscopic Instrument (DESI), during its next phase starting in 2029, DESI-II. We also tested a more realistic selection using early UNIONS data, based on a u-dropout color cut over the ugr imaging, which yields a denser sample of r < 24.2 objects at 1400 deg⁻² over 4760 deg². From this sample–covering a larger footprint and expected to have a higher large-scale galaxy bias–we forecast an improved constraint of σ(f_NL^loc) = 20, with a similar precision that is achievable after DESI-II follow-up. In addition, we performed a preliminary validation of the redshift distribution using the clustering-redshift method with DESI DR1 data, confirming the calibration from deep, small-area photometric fields. However, accounting for uncertainties in the clustering-redshift distribution significantly degrades the f_NL^loc) constraining power.