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In order to fully understand current and future climate impacts from rising carbon emissions, it is crucial to accurately quantify the air-sea CO2 flux and the ocean carbon sink in space and time. Air-sea flux estimates from observation-based data products used in the Global Carbon Budget show a large spread, and suggest a stronger carbon sink than global ocean biogeochemistry models (GOBMs) in the last decade. Output from GOBMs and Earth system models (ESMs) can be used as ‘testbeds’ to better understand current estimates of ocean carbon uptake in time and space through sub-sampling experiments. Recent testbed studies show improvement in reconstruction skill with increasing observational coverage, but the direction (over- vs. underestimation) and magnitude of bias for ocean carbon uptake vary significantly. Here, we use a collection of CMIP6 ESMs as a testbed to better understand the causes of the spread of sink estimates in observation-based products. Specifically, we assess how the choice of hyperparameters for the machine learning algorithm and the testbed structure impact reconstruction skill of surface ocean pCO2 (spCO2) using the pCO2-Residual method. We find that, when negative mean squared error (nMSE) is used as error metric during hyperparameter optimization, the reconstruction significantly underestimates spCO2 over 2017-2022, irrespective of which CMIP6 ESM is used as a testbed; this results in an overestimation of the global ocean sink, assessed through comparison to the ‘testbed truth’. If hyperparameters are selected based on bias as the error metric, this trend of increasingly negative bias is eliminated. When applied to real-world SOCAT data, this leads to a significantly weaker global ocean carbon sink in 2021-2022 (up to ~ 0.5 Pg C/yr), and less divergence from GOBM estimates. This suggests that the increasingly stronger sink showed by the pCO2-Residual method in recent years might not represent a real trend, but may be due to algorithmic design choices in the context of sparse and biased observational coverage. 

ABSTRACT Forward-modeling observables from galaxy simulations enables direct comparisons between theory and observations. To generate synthetic spectral energy distributions (SEDs) that include dust absorption, re-emission, and scattering, Monte Carlo radiative transfer is often used in post-processing on a galaxy-by-galaxy basis. However, this is computationally expensive, especially if one wants to make predictions for suites of many cosmological simulations. To alleviate this computational burden, we have developed a radiative transfer emulator using an artificial neural network (ANN), ANNgelina, that can reliably predict SEDs of simulated galaxies using a small number of integrated properties of the simulated galaxies: star formation rate, stellar and dust masses, and mass-weighted metallicities of all star particles and of only star particles with age <10 Myr. Here, we present the methodology and quantify the accuracy of the predictions. We train the ANN on SEDs computed for galaxies from the IllustrisTNG project’s TNG50 cosmological magnetohydrodynamical simulation. ANNgelina is able to predict the SEDs of TNG50 galaxies in the ultraviolet (UV) to millimetre regime with a typical median absolute error of ∼7 per cent. The prediction error is the greatest in the UV, possibly due to the viewing-angle dependence being greatest in this wavelength regime. Our results demonstrate that our ANN-based emulator is a promising computationally inexpensive alternative for forward-modeling galaxy SEDs from cosmological simulations. 

Abstract We present Ly α and ultraviolet (UV)-continuum luminosity functions (LFs) of galaxies and active galactic nuclei (AGNs) at z = 2.0–3.5 determined by the untargeted optical spectroscopic survey of the Hobby–Eberly Telescope Dark Energy Experiment (HETDEX). We combine deep Subaru imaging with HETDEX spectra resulting in 11.4 deg 2 of fiber spectra sky coverage, obtaining 18,320 galaxies spectroscopically identified with Ly α emission, 2126 of which host type 1 AGNs showing broad (FWHM > 1000 km s −1 ) Ly α emission lines. We derive the Ly α (UV) LF over 2 orders of magnitude covering bright galaxies and AGNs in log L Ly α / [ erg s − 1 ] = 43.3 – 45.5 (−27 < M UV < −20) by the 1/ V max estimator. Our results reveal that the bright-end hump of the Ly α LF is composed of type 1 AGNs. In conjunction with previous spectroscopic results at the faint end, we measure a slope of the best-fit Schechter function to be α Sch = − 1.70 − 0.14 + 0.13 , which indicates that α Sch steepens from z = 2–3 toward high redshift. Our UV LF agrees well with previous AGN UV LFs and extends to faint-AGN and bright-galaxy regimes. The number fraction of Ly α -emitting objects ( X LAE ) increases from M UV * ∼ − 21 to bright magnitude due to the contribution of type 1 AGNs, while previous studies claim that X Ly α decreases from faint magnitudes to M UV * , suggesting a valley in the X Ly α –magnitude relation at M UV * . Comparing our UV LF of type 1 AGNs at z = 2–3 with those at z = 0, we find that the number density of faint ( M UV > −21) type 1 AGNs increases from z ∼ 2 to 0, as opposed to the evolution of bright ( M UV < −21) type 1 AGNs, suggesting AGN downsizing in the rest-frame UV luminosity. 

Abstract We present the results of a stellar population analysis of 72 Lyα-emitting galaxies (LAEs) in GOODS-N at 1.9 <z< 3.5 spectroscopically identified by the Hobby−Eberly Telescope Dark Energy Experiment (HETDEX). We provide a method for connecting emission-line detections from the blind spectroscopic survey to imaging counterparts, a crucial tool needed as HETDEX builds a massive database of ∼1 million Lyαdetections. Using photometric data spanning as many as 11 filters covering 0.4 <λ(μm) < 4.5 from the Hubble Space Telescope and Spitzer Space Telescope, we study the objects’ global properties and explore which properties impact the strength of Lyαemission. We measure a median stellar mass of ${0.8}_{-0.5}^{+2.9}\times {10}^9{M}_{\odot }$and conclude that the physical properties of HETDEX spectroscopically selected LAEs are comparable to LAEs selected by previous deep narrowband studies. We find that stellar mass and star formation rate correlate strongly with the Lyαequivalent width. We then use a known sample ofz> 7 LAEs to perform a protostudy of predicting Lyαemission from galaxies in the epoch of reionization, finding agreement at the 1σlevel between prediction and observation for the majority of strong emitters. 

Abstract We describe the survey design, calibration, commissioning, and emission-line detection algorithms for the Hobby–Eberly Telescope Dark Energy Experiment (HETDEX). The goal of HETDEX is to measure the redshifts of over a million Lyαemitting galaxies between 1.88 <z< 3.52, in a 540 deg²area encompassing a comoving volume of 10.9 Gpc³. No preselection of targets is involved; instead the HETDEX measurements are accomplished via a spectroscopic survey using a suite of wide-field integral field units distributed over the focal plane of the telescope. This survey measures the Hubble expansion parameter and angular diameter distance, with a final expected accuracy of better than 1%. We detail the project’s observational strategy, reduction pipeline, source detection, and catalog generation, and present initial results for science verification in the Cosmological Evolution Survey, Extended Groth Strip, and Great Observatories Origins Deep Survey North fields. We demonstrate that our data reach the required specifications in throughput, astrometric accuracy, flux limit, and object detection, with the end products being a catalog of emission-line sources, their object classifications, and flux-calibrated spectra.