DESI DR2 and ACT DR6 data yield 17σ LRG-velocity, 8.3σ ELG-velocity, and 6.8σ QSO-velocity detections plus a 3.1σ velocity-velocity signal, producing f_NL^loc = 15.9_{-34.4}^{+34.6} from the velocity field.
Local primordial non-Gaussianity from the large-scale clustering of photometric DESI luminous red galaxies
6 Pith papers cite this work. Polarity classification is still indexing.
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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.
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Neglecting linear GR effects biases f_NL at 1–3σ for Euclid/SPHEREx in SFB forecasts; multi-tracer improves Doppler detection and weakly breaks b_ϕ f_NL degeneracy.
DESI DR1 full-shape galaxy clustering constrains Omega_m = 0.296 ± 0.010, H0 = 68.63 ± 0.79 km/s/Mpc, and sigma_8 = 0.841 ± 0.034, consistent with LambdaCDM and Planck.
Reports f_NL = -20.5^{+19.0}_{-18.1} (68% CL) from combined Quaia quasar auto-correlation and CMB lensing cross-correlation assuming p_phi=1, or -28.7^{+26.1}_{-24.6} for p_phi=1.6.
Property-dependent systematic weights derived separately on ELG subsamples, with separate DES footprint treatment, mitigate spurious clustering in ~10% of subsamples but are not optimal for the full sample.
Dust-cleaned CIB and CMB lensing cross-correlations yield f_NL^local = 43 ± 23, tightening constraints on local primordial non-Gaussianity.
citing papers explorer
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Measurement of the galaxy-velocity power spectrum of DESI tracers with the kinematic Sunyaev-Zeldovich effect using DESI DR2 and ACT DR6
DESI DR2 and ACT DR6 data yield 17σ LRG-velocity, 8.3σ ELG-velocity, and 6.8σ QSO-velocity detections plus a 3.1σ velocity-velocity signal, producing f_NL^loc = 15.9_{-34.4}^{+34.6} from the velocity field.
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Impact and measurability of linear relativistic effects in galaxy surveys
Neglecting linear GR effects biases f_NL at 1–3σ for Euclid/SPHEREx in SFB forecasts; multi-tracer improves Doppler detection and weakly breaks b_ϕ f_NL degeneracy.
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DESI 2024 V: Full-Shape Galaxy Clustering from Galaxies and Quasars
DESI DR1 full-shape galaxy clustering constrains Omega_m = 0.296 ± 0.010, H0 = 68.63 ± 0.79 km/s/Mpc, and sigma_8 = 0.841 ± 0.034, consistent with LambdaCDM and Planck.
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Constraints on primordial non-Gaussianity from Quaia
Reports f_NL = -20.5^{+19.0}_{-18.1} (68% CL) from combined Quaia quasar auto-correlation and CMB lensing cross-correlation assuming p_phi=1, or -28.7^{+26.1}_{-24.6} for p_phi=1.6.
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DESI Data Release 2 ELGs: Property-dependent subsamples, imaging systematics, and clustering
Property-dependent systematic weights derived separately on ELG subsamples, with separate DES footprint treatment, mitigate spurious clustering in ~10% of subsamples but are not optimal for the full sample.
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New constraints on primordial non-Gaussianity from large-scale cross-correlations of CMB lensing and the cosmic infrared background
Dust-cleaned CIB and CMB lensing cross-correlations yield f_NL^local = 43 ± 23, tightening constraints on local primordial non-Gaussianity.