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Abstract As the next generation of large galaxy surveys come online, it is becoming increasingly important to develop and understand the machine-learning tools that analyze big astronomical data. Neural networks are powerful and capable of probing deep patterns in data, but they must be trained carefully on large and representative data sets. We present a new “hump” of the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project: CAMELS-SAM, encompassing one thousand dark-matter-only simulations of (100h⁻¹cMpc)³with different cosmological parameters (Ω_mandσ₈) and run through the Santa Cruz semi-analytic model for galaxy formation over a broad range of astrophysical parameters. As a proof of concept for the power of this vast suite of simulated galaxies in a large volume and broad parameter space, we probe the power of simple clustering summary statistics to marginalize over astrophysics and constrain cosmology using neural networks. We use the two-point correlation, count-in-cells, and void probability functions, and we probe nonlinear and linear scales across 0.68 <R<27h⁻¹cMpc. We find our neural networks can both marginalize over the uncertainties in astrophysics to constrain cosmology to 3%–8% error across various types of galaxy selections, while simultaneously learning about the SC-SAM astrophysical parameters. This work encompasses vital first steps toward creating algorithms able to marginalize over the uncertainties in our galaxy formation models and measure the underlying cosmology of our Universe. CAMELS-SAM has been publicly released alongside the rest of CAMELS, and it offers great potential to many applications of machine learning in astrophysics:https://camels-sam.readthedocs.io. 

Abstract We report the discovery of an accreting supermassive black hole atz= 8.679. This galaxy, denoted here as CEERS_1019, was previously discovered as a Lyα-break galaxy by Hubble with a Lyαredshift from Keck. As part of the Cosmic Evolution Early Release Science (CEERS) survey, we have observed this source with JWST/NIRSpec, MIRI, NIRCam, and NIRCam/WFSS and uncovered a plethora of emission lines. The Hβline is best fit by a narrow plus a broad component, where the latter is measured at 2.5σwith an FWHM ∼1200 km s⁻¹. We conclude this originates in the broadline region of an active galactic nucleus (AGN). This is supported by the presence of weak high-ionization lines (N V, N IV], and C III]), as well as a spatial point-source component. The implied mass of the black hole (BH) is log (M_BH/M_⊙) = 6.95 ± 0.37, and we estimate that it is accreting at 1.2 ± 0.5 times the Eddington limit. The 1–8μm photometric spectral energy distribution shows a continuum dominated by starlight and constrains the host galaxy to be massive (log M/M_⊙∼9.5) and highly star-forming (star formation rate, or SFR ∼ 30 M_⊙yr⁻¹; log sSFR ∼ − 7.9 yr⁻¹). The line ratios show that the gas is metal-poor (Z/Z_⊙∼ 0.1), dense (n_e∼ 10³cm⁻³), and highly ionized (logU∼ − 2.1). We use this present highest-redshift AGN discovery to place constraints on BH seeding models and find that a combination of either super-Eddington accretion from stellar seeds or Eddington accretion from very massive BH seeds is required to form this object.