Euclid preparation. Controlling angular systematics in the Euclid spectroscopic galaxy sample

Abstract

We present the strategy to identify and mitigate potential sources of angular systematics in the Euclid spectroscopic galaxy survey, and we quantify their impact on galaxy clustering measurements and cosmological parameter estimation. We first survey the Euclid processing pipeline to identify all evident, potential sources of systematics, and classify them into two broad classes: angular systematics, which modulate the galaxy number density across the sky, and catastrophic redshift errors, which lead to interlopers in the galaxy sample. We then use simulated spectroscopic surveys to test our ability to mitigate angular systematics by constructing a random catalogue that represents the visibility mask of the survey; this is a dense set of intrinsically unclustered objects, subject to the same selection effects as the data catalogue. The construction of this random catalogue relies on a detection model, which gives the probability of reliably measuring the galaxy redshift as a function of the signal-to-noise ratio (S/N) of its emission lines. We demonstrate that, in the ideal case of a perfect knowledge of the visibility mask, the galaxy power spectrum in the presence of systematics is recovered, to within sub-percent accuracy, by convolving a theory power spectrum with a window function obtained from the random catalogue itself. In the case of only approximate knowledge of the visibility mask, we test the stability of power spectrum measurements and cosmological parameter posteriors by using perturbed versions of the random catalogue. We find that significant effects are limited to very large scales, and parameter estimation remains robust, with the most impacting effects being connected to the calibration of the detection model.

Figures (18)

• Figure 1: Main emission lines visible in the galaxy spectra, together with their visibility range for a detector that is sensitive in the wavelength range from 1.2 to 1.9 $\mu$m. The red lines correspond to the complex and the blue lines to the main line interlopers we expect in the EWS. In spectra the +[NII] complex is not resolved, so the measured ' flux' is the integrated flux of the three lines.
• Figure 2: Schematic view of the SGS pipeline for galaxy clustering. The two boxes mark level-2 and level-3 processing, with the circles corresponding to OUs in the left box and to OU-LE3 PFs in the right box. Black segments outline the dependences of the various blocks, some of which are commented by coloured text. The text highlighted by blue boxes gives the final products of the galaxy clustering pipeline.
• Figure 3: Left: detection probability $P_{\rm det}(S)$, that is the probability of detecting a galaxy in the spectroscopic sample as a function of the S/N of its line. The points give the measurements from the calibration set used to fix the parameters of the detection model. The five curves correspond to the best-fit detection model, together with those obtained by varying the two fitting parameters by 1 $\sigma$ (see the legend for the assumed parameter values). Right: the standard deviation of the ratio of the measurements from the calibration set and the best fit model.
• Figure 4: Adopted model for NISP detector geometry, the smaller squares denote the 16 detectors, crosses are the centres of healpix sky pixels (with $N_{\rm side}=4096$) that are inside (blue) or outside (cyan) the detectors.
• Figure 5: Gnomonic projection of maps used for the average MVM, the marginalised visibility mask. Left panel: the exposure time map at the photometric position of the galaxy, in units of the number of times (dithers) the sky pixel is visited; a full dithering sequence can provide up to four visits, higher values happen at pointing overlaps. Mid panel: the $E(B-V)$ reddening map. Right panel: the noise map in units of $e$ pix$^{-1}$ s$^{-1}$.