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arxiv: 2606.22432 · v1 · pith:3T6CX6ZHnew · submitted 2026-06-21 · 🌌 astro-ph.CO · astro-ph.GA

Tracing Large-scale Structure with the MeerKLASS On-the-Fly Survey: Angular Clustering of Radio Sources at 816 MHz

classification 🌌 astro-ph.CO astro-ph.GA
keywords circclusteringmeerklassthetamodellingangularbiaslarge
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We present the first measurement of the angular two-point correlation function \(w(\theta)\) of radio sources from the MeerKAT Large Area Synoptic Survey (MeerKLASS) UHF on-the-fly (OTF) continuum Data Release~1. DR1 provides interferometric Stokes-\(I\) imaging at a reference frequency of 816\,MHz over \(\sim 800~\mathrm{deg}^2\) within the DESI footprint. We detect a positive clustering signal over \(0.02^\circ \lesssim \theta \lesssim 10^\circ\). The measurement is stable to reasonable variations of the depth mask and flux threshold on intermediate and large scales. Modelling the intermediate-scale signal (\(0.112^\circ\le\theta\le 1.36^\circ\)) with a fixed-slope power law (\(\gamma=1.8\)) yields \(A(1^\circ)=(1.434\pm0.475)\times10^{-3}\), corresponding to \(\log_{10}A=-2.843^{+0.124}_{-0.175}\). We infer an effective large-scale bias by fitting \(\Lambda\)CDM projected-matter templates \(w_{\rm DM}(\theta)\) computed with \textsc{CAMB} and Limber projection, including an integral-constraint correction evaluated from random--random weights. Using two bracketing T-RECS redshift-distribution priors, we obtain \(b_{\rm eff}=1.998\pm0.350\) (AGN prior) and \(b_{\rm eff}=1.530\pm0.265\) (TOTAL prior), demonstrating that the dominant modelling uncertainty arises from \(N(z)\). As a derived summary we Limber-invert the power-law amplitude to obtain \(r_0=6.18\pm1.13\) and \(5.59\pm1.02~h^{-1}\mathrm{Mpc}\) for the AGN and TOTAL priors, respectively. These results establish MeerKLASS UHF DR1 as a new wide-area, intermediate-frequency dataset for radio-continuum clustering. As MeerKLASS expands and overlapping optical/IR spectroscopy provides improved redshift calibration, future releases will enable population-split clustering and bias evolution measurements with substantially reduced modelling uncertainty.

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