Local primordial non-Gaussianity from the large-scale clustering of photometric DESI luminous red galaxies

Abstract

We use angular clustering of luminous red galaxies from the Dark Energy Spectroscopic Instrument (DESI) imaging surveys to constrain the local primordial non-Gaussianity parameter $\fnl$. Our sample comprises over 12 million targets, covering 14,000 square degrees of the sky, with redshifts in the range $0.2< z < 1.35$. We identify Galactic extinction, survey depth, and astronomical seeing as the primary sources of systematic error, and employ linear regression and artificial neural networks to alleviate non-cosmological excess clustering on large scales. Our methods are tested against simulations with and without $\fnl$ and systematics, showing superior performance of the neural network treatment. The neural network with a set of nine imaging property maps passes our systematic null test criteria, and is chosen as the fiducial treatment. Assuming the universality relation, we find $\fnl = 34^{+24(+50)}_{-44(-73)}$ at 68\%(95\%) confidence. We apply a series of robustness tests (e.g., cuts on imaging, declination, or scales used) that show consistency in the obtained constraints. We study how the regression method biases the measured angular power-spectrum and degrades the $\fnl$ constraining power. The use of the nine maps more than doubles the uncertainty compared to using only the three primary maps in the regression. Our results thus motivate the development of more efficient methods that avoid over-correction, protect large-scale clustering information, and preserve constraining power. Additionally, our results encourage further studies of $\fnl$ with DESI spectroscopic samples, where the inclusion of 3D clustering modes should help separate imaging systematics and lessen the degradation in the $\fnl$ uncertainty.

Figures (26)

• Figure 1: The redshift distribution (solid line and vertical scale on the left) and bias evolution (dashed line and vertical scale on the right) of the DESI LRG targets. The redshift distribution is determined from DESI spectroscopy desi2023sv. The redshift evolution of the linear bias is supported by HOD fits to the angular clustering of the DESI LRG targets zhou2021clustering, where $D(z)$ represents the growth factor.
• Figure 2: Top: The DESI LRG target density map before correcting for imaging systematic effects in Mollweide projection. The disconnected islands from the North footprint and parts of the South footprint with declination below $-30$ are removed from the sample for the analysis due to potential calibration issues (see text). Bottom: Mollweide projections of the imaging systematic maps (survey depth, astronomical seeing/psfsize, Galactic extinction, and local stellar density) in celestial coordinates. Not shown here are two external maps for the neutral hydrogen column density and photometric calibration, which are only employed for the robustness tests. The imaging systematic maps are colour-coded to show increasing values from blue to red.
• Figure 3: Top: The Pearson correlation coefficient between the DESI LRG target density and imaging properties in BASS+MzLS, DECaLS North, and DECaLS South. Solid horizontal curves represent the $95\%$ confidence intervals estimated from simulations of lognormal density fields with $\fnl=0$. Bottom: The Pearson correlation matrix of imaging properties for the DESI footprint.
• Figure 4: The survey mask correlation functions across the imaging regions forming the DESI footprint, plotted against angular separation. The inset focuses on correlations within the angular range of $100$ to $180$ degrees.
• Figure 5: The mean power spectrum from the $\fnl=0$ mocks (no contamination) and best-fitting theoretical prediction after accounting for the survey geometry and integral constraint effects. Bottom panel shows the residual power spectrum relative to the mean power spectrum. The dark and light shades represent the $68\%$ error on the mean and one realization, respectively. No imaging systematic cleaning is applied to these mocks.