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ABSTRACT The dark matter (DM) distribution in dwarf galaxies provides crucial insights into both structure formation and the particle nature of DM. GraphNPE (Graph Neural Posterior Estimator), first introduced in Nguyen et al. (2023), is a novel simulation-based inference framework that combines graph neural networks and normalizing flows to infer the DM density profile from line-of-sight stellar velocities. Here, we apply GraphNPE to satellite dwarf galaxies in the FIRE-2 Latte simulation suite of Milky Way-mass haloes, testing it against both Cold and Self-Interacting DM scenarios. Our method demonstrates superior precision compared to conventional Jeans-based approaches, recovering DM density profiles to within the 95 per cent confidence level even in systems with as few as 30 tracers. Moreover, we present the first evaluation of mass modelling methods in constraining two key parameters from realistic simulations: the peak circular velocity, $$V_\mathrm{max}$$, and the peak virial mass, $$M_\mathrm{200m}^\mathrm{peak}$$. Using only line-of-sight velocities, GraphNPE can reliably recover both $$V_\mathrm{max}$$ and $$M_\mathrm{200m}^\mathrm{peak}$$ within our quoted uncertainties, including those experiencing tidal effects ($$\gtrsim 63~{{\rm per\ cent}}$$ of systems are recovered within our 68 per cent confidence intervals and $$\gtrsim 92~{{\rm per\ cent}}$$ within our 95 per cent confidence intervals). The method achieves $$10-20~{{\rm per\ cent}}$$ accuracy in $$V_\mathrm{max}$$ recovery, while $$M_\mathrm{200m}^\mathrm{peak}$$ is recovered to $$0.1-0.4 \, \mathrm{dex}$$ accuracy. This work establishes GraphNPE as a robust tool for inferring DM density profiles in dwarf galaxies, offering promising avenues for constraining DM models. The framework’s potential extends beyond this study, as it can be adapted to non-spherical and disequilibrium models, showcasing the broader utility of simulation-based inference and graph-based learning in astrophysics. 

ABSTRACT The Merian survey is mapping ∼ 850 deg2 of the Hyper Suprime-Cam Strategic Survey Program (HSC-SSP) wide layer with two medium-band filters on the 4-m Victor M. Blanco telescope at the Cerro Tololo Inter-American Observatory, with the goal of carrying the first high signal-to-noise (S/N) measurements of weak gravitational lensing around dwarf galaxies. This paper presents the design of the Merian filter set: N708 (λc = 7080 Å, Δλ = 275 Å) and N540 (λc = 5400 Å, Δλ = 210 Å). The central wavelengths and filter widths of N708 and N540 were designed to detect the $$\rm H\alpha$$ and $$\rm [OIII]$$ emission lines of galaxies in the mass range $$8\lt \rm \log M_*/M_\odot \lt 9$$ by comparing Merian fluxes with HSC broad-band fluxes. Our filter design takes into account the weak lensing S/N and photometric redshift performance. Our simulations predict that Merian will yield a sample of ∼ 85 000 star-forming dwarf galaxies with a photometric redshift accuracy of σΔz/(1 + z) ∼ 0.01 and an outlier fraction of $$\eta =2.8~{{\ \rm per\ cent}}$$ over the redshift range 0.058 < z < 0.10. With 60 full nights on the Blanco/Dark Energy Camera (DECam), the Merian survey is predicted to measure the average weak lensing profile around dwarf galaxies with lensing S/N ∼32 within r < 0.5 Mpc and lensing S/N ∼90 within r < 1.0 Mpc. This unprecedented sample of star-forming dwarf galaxies will allow for studies of the interplay between dark matter and stellar feedback and their roles in the evolution of dwarf galaxies.