Halo Lightcones with Optimised Orientation and Interpolation in Cosmological Simulations -- an application to mock H$α$ selected galaxies
Sujatha Ramakrishnan, Francisco J. Castander, Elizabeth J. Gonzalez, Martin Eriksen, Zahra Baghkhani, Pablo Fosalba, Jorge Carretero, Gabriele Parimbelli, Pau Tallada-Crespí
TL;DR
HolCon delivers a scalable, post-processing framework to generate realistic halo lightcones and galaxy mocks for Stage IV surveys. It combines an orientation-optimizing mask with linear halo interpolation and distributed computing to efficiently produce multiple lightcone realizations. The Uchuu-based lightcone is validated against halo mass functions and clustering, and is used to build SciPIC-based galaxy catalogs extended to $z\leq 10$, including $\mathrm{H}\alpha$-emitter tracers. The resulting mocks, calibrated with COSMOS2020 and extended LF evolution, provide valuable predictions for halo-galaxy connections and the clustering of emission-line galaxies in future cosmological surveys.
Abstract
A critical step to create realistic mock catalogs that support large-scale photometric and spectroscopic sky surveys is the production of cosmological simulations that accurately model the survey observables taking into account the redshift-dependent galaxy formation and evolution processes. Here we develop an efficient framework, HOLCon (Halo Optimised Lightcone Constructor), for post-facto construction of dark matter halo lightcones from simulations and use them to generate a mock galaxy catalogue. HOLCon includes a module to optimise the lightcone's orientation within the simulation box, minimising repeated structures when the survey volume exceeds a single box -- a common challenge in modern surveys. A linear interpolation scheme tracks the evolution of halo properties across snapshots. Applied to the publicly available Uchuu simulation, we construct a lightcone of 50 ${\rm deg}^2$ and extending up to $z = 10$, providing representative coverage of deep fields of Stage IV surveys. We validate the lightcone for cosmological applications by comparing the dark matter halo clustering in the lightcone with those from the original simulation snapshots. Subsequently, we make the galaxy-halo connection on the lightcone with a redshift extended version of the SciPIC algorithm producing a comprehensive set of descriptive galaxy attributes. HOLCon leverages Dask, a scalable parallel computing pythonic framework for fast construction of dark matter halo lightcones enabling rapid creation of multiple statistical realizations essential for robust cosmological inference. The produced galaxy mock makes predictions for clustering of H$α$ emitters, making it a useful cosmology resource.
