Euclid preparation LXXIX. Using mock low surface brightness dwarf galaxies to probe Euclid Wide Survey detection capabilities

TL;DR

This study evaluates Euclid Wide Survey detection capabilities for Local Universe low surface brightness (LSB) dwarfs by injecting a comprehensive catalog of mock dwarfs into real VIS exposures and processing them through the MER pipeline. Using a large reference sample of dwarf ellipticals with a fixed Sérsic index ${n}=0.8$ and distances up to $120$ Mpc, the authors quantify detection completeness as a function of mean surface brightness inside the effective radius, $SB_{\rm e}$, and of the effective radius, $R_{\rm e}$. They find high MER detection completeness of about $91\%$ for $SB_{\rm e}\in [21,24]$ and $54\%$ for $SB_{\rm e}\in [24,28]$, which drop to $86\%$ and $38\%$ after accounting for potential contaminants; a flux loss occurs for $R_{\rm e}>10''$ due to the MER local background subtraction. The paper proposes a background-reinjected product by re-adding the MER background to recover reliable photometric properties, and discusses pipeline improvements (e.g., FoV-scale background estimation, LSB-optimised de-blending) to better preserve LSB signals in wide surveys, with implications for Euclid and other deep surveys.

Abstract

Local Universe dwarf galaxies can serve as both cosmological and mass assembly probes. Deep surveys have enabled the study of these objects down to the low surface brightness (LSB) regime. In this paper, we estimate Euclid's dwarf detection capabilities as well as limits of its MERge processing function (MER pipeline), which is responsible for producing the stacked mosaics and final catalogues. To do this, we injected mock dwarf galaxies in a real Euclid Wide Survey (EWS) field in the VIS band and compared the input catalogue to the final MER catalogue. The mock dwarf galaxies were generated using simple Sersic models with structural parameters (including size and surface brightness) drawn from observed dwarf galaxy catalogues. These simulations represent an idealised case in the sense they do not account for additional factors such as ellipticity, morphology, or crowding. To characterise the detected dwarfs, we used the mean surface brightness inside the effective radius SB_e (in mag arcsec^-2). The final MER catalogues achieve completenesses of 91% for SB_e in [21,24] and 54% for SB_e in [24,28]. These numbers do not take into account possible contaminants, including confusion with background galaxies at the location of the dwarfs. After taking those effects into account, they respectively became 86% and 38%. The MER pipeline performs a final local background subtraction with a small mesh size, leading to a flux loss for galaxies with R_e > 10 arcsec. By using the final MER mosaics and reinjecting this local background, we obtained an image in which we recover reliable photometric properties for objects under the arcminute scale. This background-reinjected product is thus suitable for the study of Local Universe dwarf galaxies. Euclid's data reduction pipeline serves as a test bed for other deep surveys, particularly regarding background subtraction methods, a key issue in LSB science.

Figures (19)

• Figure 1: Scaling relation between the effective radius and absolute magnitude in the $g$-band of the dwarfs galaxies in the reference sample. We indicate the average surface brightness within $R_{\rm e}$ in the $g$-band with dotted lines.
• Figure 2: Relation between the absolute magnitude in the $g$-band of the nucleus and dwarf galaxy host for nucleated dwarf galaxies in the Virgo and Fornax clusters as well as in the field and galaxy groups. The linear fit is shown with a dashed line.
• Figure 3: Summary of the Galfit fitting strategy, including (from left to right) the original image (i.e. the cutout of an injected dwarf galaxy at 10 Mpc), the masked image obtained with a combination of MTObjects and Source Extractor segmentation maps, the residual image obtained by subtraction of the original image with the model, and the model image.
• Figure 4: Histograms of the input ${\rm SB}_{\rm e}$ (left panel) and the input $R_{\rm e}$ (right panel), colour-coded according to their detection by eye and in the MER catalogue. Here, $N_\sfont{SEARCH}$ is the number of MER sources found by using the search radius $R_{\sfont{SEARCH}}$ for the cross-match. It is worth noting that the dwarfs in MER catalogues and those with $N_\sfont{SEARCH}>1$ are also detected by eye. In this plot, all the dwarfs (nucleated or not) are included. In complement of this plot, Appendix \ref{['AppDetectionRate']} provides a ${\rm SB}_{\rm e}$-$R_{\rm e}$ map of detection rate. Finally, in Appendix \ref{['AppA']}, we also provide the histogram of $\langle \mu_\sfont{I} \rangle$ as defined in Q1-SP001.
• Figure 5: Input $R_{\rm e}$ as a function of the total magnitude in $\IE$ used to inject the dwarfs. They are colour-coded according to their detection (in the final MER catalogue, only by eye or not detected).