NEXUS: A Spectroscopic Census of Broad-line AGNs and Little Red Dots at 3 ≲ <i>z</i> ≲ 6

Abstract We present a spectroscopic sample of 24 broad-line AGNs (BLAGNs) at 3 ≲ z ≲ 6 selected using F322W2+F444W NIRCam/WFSS grism spectroscopy of the central 100 arcmin 2 area of the NEXUS survey. Among these BLAGNs, 15 are classified as Little Red Dots (LRDs) based on their rest-frame UV–optical spectral slopes and compact morphology. The number density of LRDs is ∼10 −5 cMpc −3 , with a hint of declining towards lower redshift. These BLAGNs span broad H α luminosities of ∼10 42.2 –10 43.5 erg s −1 , black hole masses of ∼10 6.3 –10 8.4 M ⊙ , and Eddington ratios of ∼0.1–1, though the estimates of black hole mass and Eddington ratio carry large systematic uncertainties. Half of the LRDs show strong Balmer absorption, suggesting high-density gas surrounding the line-emitting region. We detect extended (hundreds of parsecs) rest-frame UV–optical emission from the host galaxy in the majority of these LRDs, which contributes significantly or even dominantly to their total UV emission and largely accounts for the peculiar UV upturn of their spectral energy distribution. We also measure the small-scale (≲1 cMpc) clustering of these BLAGNs and LRDs by cross-correlating with a photometric galaxy sample. Extrapolating the power-law two-point correlation function model to large linear scales, we infer a linear bias of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>3.6</mml:mn> <mml:msubsup> <mml:mrow> <mml:mn>9</mml:mn> </mml:mrow> <mml:mrow> <mml:mo>−</mml:mo> <mml:mn>2.21</mml:mn> </mml:mrow> <mml:mrow> <mml:mo>+</mml:mo> <mml:mn>2.78</mml:mn> </mml:mrow> </mml:msubsup> </mml:math> and typical halo masses of a few ×10 11 h −1 M ⊙ for BLAGNs at the sample median redshift of z ∼ 4.5. However, the inferred linear bias and halo masses of LRDs, while formally consistent with those for BLAGNs at ∼1.5 σ , appear too large to be compatible with their space density, suggesting LRDs may have strong excess clustering on small scales.