Rubin-LSST

Abstract:

Recent work has developed a formalism for computing angular power spectra directly from catalogues containing field values at discrete positions on the sky, thereby circumventing the need to create pixelized maps of the fields, as well as avoiding aliasing and finite-resolution effects. We adapt this formalism to incorporate template deprojection for mitigating systematic biases in the measured angular power spectra. We also introduce an alternative method of mitigating the ‘deprojection bias’ – the loss of modes induced by deprojection – employing simple simulations to compute a transfer function. We find that this approach performs at least as well as existing methods, and is relatively insensitive to how well one can guess the true power spectrum of the observed field, except at the largest scales (). Additionally, we develop exact expressions for the bias introduced by deprojection in the shot-noise component, which further improves the accuracy of this approach. We test our formalism on simulated data sets, demonstrating its applicability both to discretely sampled fields, and to the special case of galaxy clustering, with the survey selection function defined in terms of a random catalogue or as a continuous sky map. After removing the bias in the shot noise and correcting for the remaining mode loss using a transfer function, our formalism produces unbiased measurements of the angular power spectrum in all scenarios tested here. Finally, we apply our formalism to real data and show it produces results consistent with the standard map-based pseudo- formalism. We implement our method in the public code NaMaster.

Abstract:

As radio emission from normal galaxies is a dust-free tracer of star formation, tracing the star formation history of the Universe is a key goal of the Square Kilometre Array and the Next-Generation Very Large Array. In order to investigate how well radio luminosity traces star formation rate (SFR) in the early Universe, we have examined the radio properties of a JWST Paα sample of galaxies at 1.0 ≲ z ≲ 1.8. In the GOODS-S field, we cross-matched a sample of 506 FRESCO Paα emitters with the 1.23 GHz radio continuum data from the MeerKAT MIGHTEE survey, finding 47 detections. After filtering for active galactic nuclei (via X-ray detections, hot mid-infrared dust, and extended radio emission), as well as blended sources, we obtained a sample of star-forming galaxies comprising 11 cataloged radio detections, 18 noncataloged detections (at ≈3σ–5σ), and 298 undetected sources. Stacking the 298 undetected sources, we obtain a 3.3σ detection in the radio. This sample, along with a local sample of Paα emitters, lies along previous radio luminosity/SFR relations from local (<0.2) to high redshift (z ∼ 1). Fitting the FRESCO data at 1.0 ≲ z ≲ 1.8, we find log(L1.4GHz)= (1.31 ± 0.17) × log(SFRPaα)+ (21.36 ± 0.17), which is consistent with other literature relations. We can explain some of the observed scatter in the L1.4GHz/SFRPaα correlation by a toy model in which the synchrotron emission is a delayed/averaged tracer of the instantaneous Paα SFR by ∼10/75 Myr.

Abstract:

We analyse the large-scale angular clustering of quasars in the Gaia-unWISE quasar catalog, Quaia, and their cross-correlation with maps of the lensing convergence of the Cosmic Microwave Background (CMB), to constrain the level of primordial non-Gaussianity (PNG). Specifically, we target the scale-dependent bias that would be induced by PNG on biased tracers of the matter inhomogeneities on large scales. The Quaia sample is particularly well suited for this analysis, given the large effective volume covered, and our ability to map out the main potential sources of systematic contamination and mitigate their impact. Using the universality relation to characterise the response of the quasar overdensity to PNG (pϕ = 1), we report constraints on the local-type PNG parameter f NL of f NL = -20.5+19.0 -18.1 (68% C.L.) by combining the quasar auto-correlation and its cross-correlation with CMB lensing in two tomographic redshift bins (or f NL = -28.7+26.1 -24.6 if assuming a lower response for quasars, pϕ = 1.6). The error on f NL can be further improved if the cross-correlation between the tomographic redshift bins is included. Using the CMB lensing cross-correlations alone, we find fNL = -13.8+26.7 -25.0 and fNL = -15.6+42.3 -34.8 for pϕ = 1 and pϕ = 1.6 respectively. These are the tightest constraints on fNL to date from angular clustering statistics and cross-correlations with CMB lensing.