English

Full sky Integrated Sachs-Wolfe maps for the MICE Grand Challenge lightcone simulation

Cosmology and Nongalactic Astrophysics 2021-08-17 v2

Abstract

We present full-sky maps of the Integrated Sachs-Wolfe effect (ISW) for the MICE Grand Challenge lightcone simulation up to redshift 1.4. The maps are constructed in the linear regime using spherical Bessel transforms. We compare and contrast this procedure against analytical approximations found in the literature. By computing the ISW in the linear regime, we remove the substantial computing and storage resources required to calculate the non-linear Rees-Sciama effect. Since the linear ISW at low redshift z1z\leq1, at large angular scales, and after matter domination is 102×\sim 10^{2}\times larger in ΔT/T\Delta T/T, this has a negligible impact on the maps produced and only becomes relevant on scales which are dominated by cosmic microwave background (CMB) anisotropies. The MICE simulation products have been extensively used for studies involving current and future galaxy surveys. The availability of these maps will allow MICE to be used for future galaxy and CMB cross-correlation studies, ISW reconstruction studies, and ISW void-stacking studies probed by galaxy surveys such as Dark Energy Survey, Dark Energy Spectroscopic Instrument, Euclid, and Rubin Legacy Survey of Space and Time. The pipeline developed in this study is provided as a public Python package pyGenISW. This could be used in the future studies for constructing the ISW from existing and future simulation suites probing vast sets of cosmological parameters and models.

Keywords

Cite

@article{arxiv.2103.14654,
  title  = {Full sky Integrated Sachs-Wolfe maps for the MICE Grand Challenge lightcone simulation},
  author = {Krishna Naidoo and Pablo Fosalba and Lorne Whiteway and Ofer Lahav},
  journal= {arXiv preprint arXiv:2103.14654},
  year   = {2021}
}

Comments

11 pages, 6 figures, minor edits to match version published in Monthly Notices of the Royal Astronomical Society. The analysis presented in this paper was calculated using pyGenISW which is available here: https://github.com/knaidoo29/pyGenISW

R2 v1 2026-06-24T00:35:53.082Z