Galaxy formation and evolution

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 present deep ALMA dust continuum observations for a sample of luminous (MUV < −22) star-forming galaxies at z ≃ 7. We detect five of the six sources in the far-infrared (FIR), providing key constraints on the obscured star-formation rate (SFR) and the infrared-excess-β (IRX–β) relation without the need for stacking. Despite the galaxies showing blue rest-frame UV slopes (β ≃ −2) we find that 35–75 percent of the total SFR is obscured. We find the IRX–β relation derived for these z ≃ 7 sources is consistent with that found for local star-burst galaxies. Using our relatively high-resolution (FWHM ≃ 0.7 arcsec) observations we identify a diversity of dust morphologies in the sample. We find both compact emission that appears offset relative to the unobscured components and extended dust emission that is co-spatial with the rest-frame UV light. In the majority of the sources we detect strong rest-frame UV colour gradients (with up to Δβ ≃ 0.7–1.4) as probed by the multi-band UltraVISTA ground-based data. The observed redder colours are spatially correlated with the location of the FIR detection. Our results show that even in bright Lyman-break galaxies at z ≃ 7 the peak of the star-formation is typically hosted by the fainter, redder, regions in the rest-frame UV, which have an obscured fraction of fobs ≥ 0.8. As well as demonstrating the importance of dust obscured star-formation within the Epoch of Reionization, these observations provide an exciting taster of the rich spatially resolved datasets that will be obtained from JWST and high-resolution ALMA follow-up at these redshifts.

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.