Hintze Centre for Astrophysical Surveys

Abstract:

© 2019 The Author(s) We report the discovery of the first transient with MeerKAT, MKT J170456.2−482100, discovered in ThunderKAT images of the low-mass X-ray binary GX339−4. MKT J170456.2−482100 is variable in the radio, reaching a maximum flux density of 0.71 ± 0.11 mJy on 2019 October 12, and is undetected in 15 out of 48 ThunderKAT epochs. MKT J170456.2−482100 is coincident with the chromospherically active K-type sub-giant TYC 8332-2529-1, and ∼ 18 yr of archival optical photometry of the star shows that it varies with a period of 21.25 ± 0.04 d. The shape and phase of the optical light curve changes over time, and we detect both X-ray and UV emission at the position of MKT J170456.2−482100, which may indicate that TYC 8332-2529-1 has large star spots. Spectroscopic analysis shows that TYC 8332-2529-1 is in a binary, and has a line-of-sight radial velocity amplitude of 43 km s−1. We also observe a spectral feature in antiphase with the K-type sub-giant, with a line-of-sight radial velocity amplitude of ∼ 12 ± 10 km s−1, whose origins cannot currently be explained. Further observations and investigation are required to determine the nature of the MKT J170456.2−482100 system.

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 accelerates simulations by up to 2 billion times 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:

Tighter constraints on measurements of primordial non-Gaussianity (PNG) will allow the differentiation of inflationary scenarios. The cosmic microwave background bispectrum – the standard method of measuring the local non-Gaussianity – is limited by cosmic variance. Therefore, it is sensible to investigate measurements of non-Gaussianity using the large-scale structure. This can be done by investigating the effects of non-Gaussianity on the power spectrum on large scales. In this study, we forecast the constraints on the local PNG parameter fNL that can be obtained with future radio surveys. We utilize the multitracer method that reduces the effect of cosmic variance and takes advantage of the multiple radio galaxy populations that are differently biased tracers of the same underlying dark matter distribution. Improvements on previous work include the use of observational bias and halo mass estimates, updated simulations, and realistic photometric redshift expectations, thus producing more realistic forecasts. Combinations of Square Kilometre Array simulations and radio observations were used as well as different redshift ranges and redshift bin sizes. It was found that in the most realistic case the 1σ error on fNL falls within the range 4.07–6.58, rivalling the tightest constraints currently available.