Abstract We investigate the stellar mass–black hole mass ( ) relation with type 1 active galactic nuclei (AGNs) down to , corresponding to a ≃ −21 absolute magnitude in rest-frame ultraviolet, atz= 2–2.5. Exploiting the deep and large-area spectroscopic survey of the Hobby–Eberly Telescope Dark Energy Experiment (HETDEX), we identify 66 type 1 AGNs with ranging from 107–1010M⊙that are measured with single-epoch virial method using Civemission lines detected in the HETDEX spectra. of the host galaxies are estimated from optical to near-infrared photometric data taken with Spitzer, the Wide-field Infrared Survey Explorer, and ground-based 4–8 m class telescopes byCIGALEspectral energy distribution (SED) fitting. We further assess the validity of SED fitting in two cases by host-nuclear decomposition performed through surface brightness profile fitting on spatially resolved host galaxies with the James Webb Space Telescope/NIRCam CEERS data. We obtain the relation covering the unexplored low-mass ranges of , and conduct forward modeling to fully account for the selection biases and observational uncertainties. The intrinsic relation atz∼ 2 has a moderate positive offset of 0.52 ± 0.14 dex from the local relation, suggestive of more efficient black hole growth at higher redshift even in the low-mass regime of . Our relation is inconsistent with the suppression at the low- regime predicted by recent hydrodynamic simulations at a 98% confidence level, suggesting that feedback in the low-mass systems may be weaker than those produced in hydrodynamic simulations.
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Fast ℓ1 -regularized EEG source localization using variable projection
Abstract Electroencephalograms (EEG) are invaluable for treating neurological disorders, however, mapping EEG electrode readings to brain activity requires solving a challenging inverse problem. For time series data, the use of regularization quickly becomes intractable for many solvers, and, despite the reconstruction advantages of regularization, -based approaches such as standardized low-resolution brain electromagnetic tomographysLORETAare used in practice. In this work, we formulate EEG source localization as a graphical generalized elastic net inverse problem and present avariable projectedaugmented Lagrangian algorithm (VPAL) suitable for fast EEG source localization. We prove convergence of this solver for a broad class of separable convex, potentially non-smooth functions subject to linear constraints. Leveraging the efficiency of the proposedVPALalgorithm, we introduce a windowed variation,VPAL , that computes time dynamics in sequence suitable for real-time reconstruction. Our proposed methods are compared to state-of-the-art approaches includingsLORETAand other methods for -regularized inverse problems.
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- PAR ID:
- 10599286
- Publisher / Repository:
- IOP Publishing
- Date Published:
- Journal Name:
- Inverse Problems
- Volume:
- 41
- Issue:
- 6
- ISSN:
- 0266-5611
- Format(s):
- Medium: X Size: Article No. 065014
- Size(s):
- Article No. 065014
- Sponsoring Org:
- National Science Foundation
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