Spectral Data-cube Cleaning for CCAT Deep Spectroscopic Survey. I. Effect of correlated noise and filtering on the power spectrum

arXiv:2607.20404 · astro-ph.GA, astro-ph.IM · Submitted 2026-07-22 · Read on arXiv

A. Dev, C. Karoumpis, Y. Okada, K. Basu, F. Bertoldi, D. Chung, J. Clarke, R. Freundt, T. Nikola, T. Oak, D. Riechers

astro-ph.GA, astro-ph.IM

Submitted: 2026-07-22

Comments: 19 pages, 15 figures, submitted to A&A

Code: https://github.com/hpc4cmb/toast

Project page: https://hpc4cmb.github.io/toast

License: http://creativecommons.org/licenses/by/4.0/

The gist: The Epoch of Reionization Spectrometer (EoR-Spec) on the Fred Young Submillimeter Telescope (FYST) will conduct the CCAT Deep Spectroscopic Survey (DSS) to perform line-intensity mapping of

Terminology

Abstract

The Epoch of Reionization Spectrometer (EoR-Spec) on the Fred Young Submillimeter Telescope (FYST) will conduct the CCAT Deep Spectroscopic Survey (DSS) to perform line-intensity mapping of redshifted [C II] emission. Atmospheric 1/f noise and instrumental systematics affect power-spectrum recovery. We present realistic end-to-end simulations to quantify these effects and evaluate a Filter-and-Bin (F&B) pipeline. The simulated observations include instrument response, astrophysical emission, atmospheric noise, and observing strategy. The pipeline suppresses atmospheric 1/f noise by about four orders of magnitude at low temporal frequencies while leaving only minor residual correlated noise. For a single EoR-Spec module operating at 50% observing efficiency, the DSS is expected to detect the combined [C II] + CO power spectrum on shot-noise-dominated scales (k > 0.1, Mpc-1). With two modules operating at full efficiency, detections are achievable over all targeted spatial scales. The transfer function exceeds 80% at k 0.5, Mpc-1 but falls below 20% at k 0.1, Mpc-1, indicating significant suppression of large-scale modes. These results demonstrate that the F&B pipeline is effective for recovering the shot-noise regime, while improved map-making techniques will be required for accurate large-scale clustering measurements.

Sources

Related papers