Prof. Matt Jarvis

Abstract:

We present the observed H I size–mass relation of 204 galaxies from the MIGHTEE Survey Early Science data. The high sensitivity of MeerKAT allows us to detect galaxies spanning more than 4 orders of magnitude in H I mass, ranging from dwarf galaxies to massive spirals, and including all morphological types. This is the first time the relation has been explored on a blind homogeneous data set that extends over a previously unexplored redshift range of 0 < z < 0.084, i.e. a period of around one billion years in cosmic time. The sample follows the same tight logarithmic relation derived from previous work, between the diameter (⁠DHI⁠) and the mass (⁠MHI⁠) of H I discs. We measure a slope of 0.501 ± 0.008, an intercept of −3.252+0.073−0.074⁠, and an observed scatter of 0.057 dex. For the first time, we quantify the intrinsic scatter of 0.054 ± 0.003 dex (⁠∼10 per cent⁠), which provides a constraint for cosmological simulations of galaxy formation and evolution. We derive the relation as a function of galaxy type and find that their intrinsic scatters and slopes are consistent within the errors. We also calculate the DHI−MHI relation for two redshift bins and do not find any evidence for evolution with redshift. These results suggest that over a period of one billion years in look-back time, galaxy discs have not undergone significant evolution in their gas distribution and mean surface mass density, indicating a lack of dependence on both morphological type and redshift.

Abstract:

Computer simulations are invaluable tools for scientific discovery. However, accurate simulations are often slow to execute, which limits their applicability to extensive parameter exploration, large-scale data analysis, and uncertainty quantification. A promising route to accelerate simulations by building fast emulators with machine learning requires large training datasets, which can be prohibitively expensive to obtain with slow simulations. Here we present a method based on neural architecture search to build accurate emulators even with a limited number of training data. The method successfully emulates simulations in 10 scientific cases including astrophysics, climate science, biogeochemistry, high energy density physics, fusion energy, and seismology, using the same super-architecture, algorithm, and hyperparameters. Our approach also inherently provides emulator uncertainty estimation, adding further confidence in their use. We anticipate this work will accelerate research involving expensive simulations, allow more extensive parameters exploration, and enable new, previously unfeasible computational discovery.

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 Lyα emitting galaxies between 1.88 < z < 3.52, in a 540 deg2 area encompassing a co-moving volume of 10.9 Gpc3. No pre-selection 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 COSMOS, Extended Groth Strip, and GOODS-N 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.

Abstract:

Active galactic nuclei (AGN) are typically identified through radio, mid-infrared, or X-ray emission or through the presence of broad and/or narrow emission lines. AGN can also leave an imprint on a galaxy’s spectral energy distribution (SED) through the re-processing of photons by the dusty torus. Using the SED fitting code PROSPECT with an incorporated AGN component, we fit the far-ultraviolet to far-infrared SEDs of ∼494 000 galaxies in the D10-COSMOS field and ∼230 000 galaxies from the GAMA survey. By combining an AGN component with a flexible star formation and metallicity implementation, we obtain estimates for the AGN luminosities, stellar masses, star formation histories, and metallicity histories for each of our galaxies. We find that PROSPECT can identify AGN components in 91 per cent of galaxies pre-selected as containing AGN through narrow-emission line ratios and the presence of broad lines. Our PROSPECT-derived AGN luminosities show close agreement with luminosities derived for X-ray selected AGN using both the X-ray flux and previous SED fitting results. We show that incorporating the flexibility of an AGN component when fitting the SEDs of galaxies with no AGN has no significant impact on the derived galaxy properties. However, in order to obtain accurate estimates of the stellar properties of AGN host galaxies, it is crucial to include an AGN component in the SED fitting process. We use our derived AGN luminosities to map the evolution of the AGN luminosity function for 0 < z < 2 and find good agreement with previous measurements and predictions from theoretical models.