SPHEREx conducts the first all-sky near-infrared spectral survey over 0.75-5.0 $\mu$m, spanning the transition from host-galaxy starlight to AGN-heated torus emission and enabling spectroscopic identification of active galactic nuclei (AGNs). We develop a data-driven framework combining weighted non-negative matrix factorization with forward modeling to learn empirical rest-frame templates from 2,777 normal galaxies and 1,064 AGNs in the North Ecliptic Pole (NEP) field. The resulting 14-component dictionary contains seven galaxy templates spanning stellar-population continua and nebular emission and seven AGN templates capturing emission lines and warm and hot torus continua, providing an additive, operational decomposition. Inferred near-infrared host fractions correlate with independent SDSS spectral and HSC imaging decompositions (at Pearson $r=0.68$ and $r=0.59$), comparable to the SDSS-HSC correlation ($r=0.66$) and supporting the reliability of the AGN-host separation. In the NEP deep field, the same templates recover redshifts with $|z_{\rm fit}-z_{\rm spec}|<0.01$ for 93.0% of galaxies and 93.5% of AGNs and distinguish galaxies, narrow-line AGNs, and broad-line AGNs with 81.3% accuracy. At all-sky depth, classification retains 72.9% accuracy. The near-infrared coefficients also flag questionable DESI spectral classifications and redshifts, showing how SPHEREx complements optical spectroscopy. Learned directly from survey spectrophotometry and extending beyond the $K$ band, this empirical dictionary fills a long-standing gap in near-to-mid-infrared templates and provides a data-driven foundation for population-scale studies of AGN activity and its connection to host galaxies across the SPHEREx all-sky survey.
Dwarf galaxies, in the $\Lambda$CDM framework, are expected to dominate the galaxy number density at all redshifts. As such, studying AGN in these systems, including characterizing their local environments and emission properties, is essential in obtaining a comprehensive view of the AGN population and their influence...
T. Bohn, A. Aravindan, G. Canalizo et al.· 0 citations
We introduce the JWST Ultramassive Galaxy Sample, comprising 8 extreme early-type galaxies ($M_\star \gtrsim 2\times10^{12}\,\mathrm{M}_\odot$) drawn as a representative subset from a comprehensive full-sky census of over 100 such objects. This program aims to dynamically measure their central supermassive black holes...
M. Cappellari, Dieu D. Nguyen, S. Kassin· 1 citation
The Palomar-Green (PG) Quasars have been used extensively to establish the spectral properties of luminous active galactic nuclei (AGNs) at low redshifts in many wavebands, but not the near-infrared (NIR). We present NIR spectra of all 87 PG quasars with z<0.5 observed with the Apache Point Observatory 3.5-m telescope...
Amy Cavanaugh, M. Brotherton, J. McLane et al.· 0 citations
The properties of galaxies that host active galactic nuclei (AGNs) offer clues to the conditions that enable accretion onto supermassive black holes. A galaxy’s morphology can provide significant information on the evolutionary processes underway. By modeling the surface-brightness profiles of galaxies that host AGN, w...
Callum Dewsnap, P. Barmby, S. Gallagher· Publications of the Astronom...· 0 citations
High-redshift radio galaxies (HzRGs) are among the earliest and most massive galaxies whose powerful radio emission allows us to probe the large-scale structure, galaxy evolution, and the epoch of reionisation. We present a new sample of HzRG candidates constructed using the 1.28 GHz data from the MeerKAT Galaxy Clus...
Vasco Cossa, K. Knowles, O. Smirnov· Monthly notices of the Royal...· 0 citations
Dwarf galaxies, in the ΛCDM framework, are expected to dominate the galaxy number density at all redshifts. As such, studying active galactic nuclei (AGN) in these systems, including characterizing their local environments and emission properties, is essential in obtaining a comprehensive view of the AGN population and...
T. Bohn, A. Aravindan, G. Canalizo et al.· Astrophysical Journal· 0 citations
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