The Square Kilometre Array (SKA)

Abstract:

We present the confusion-limited 1.28 GHz MeerKAT DEEP2 image covering one $\approx 68'$ FWHM primary beam area with $7.6''$ FWHM resolution and $0.55 \pm 0.01$ $\mu$Jy/beam rms noise. Its J2000 center position $\alpha=04^h 13^m 26.4^s$, $\delta=-80^\circ 00' 00''$ was selected to minimize artifacts caused by bright sources. We introduce the new 64-element MeerKAT array and describe commissioning observations to measure the primary beam attenuation pattern, estimate telescope pointing errors, and pinpoint $(u,v)$ coordinate errors caused by offsets in frequency or time. We constructed a 1.4 GHz differential source count by combining a power-law count fit to the DEEP2 confusion $P(D)$ distribution from $0.25$ to $10$ $\mu$Jy with counts of individual DEEP2 sources between $10$ $\mu$Jy and $2.5$ mJy. Most sources fainter than $S \sim 100$ $\mu$Jy are distant star-forming galaxies obeying the FIR/radio correlation, and sources stronger than $0.25$ $\mu$Jy account for $\sim93\%$ of the radio background produced by star-forming galaxies. For the first time, the DEEP2 source count has reached the depth needed to reveal the majority of the star formation history of the universe. A pure luminosity evolution of the 1.4 GHz local luminosity function consistent with the Madau & Dickinson (2014) model for the evolution of star-forming galaxies based on UV and infrared data underpredicts our 1.4 GHz source count in the range $-5 \lesssim \log[S(\mathrm{Jy})] \lesssim -4$.

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:

We describe system verification tests and early science results from the pulsar processor (PTUSE) developed for the newly commissioned 64-dish SARAO MeerKAT radio telescope in South Africa. MeerKAT is a high-gain low-system temperature radio array that currently operates at 580-1 670 MHz and can produce tied-array beams suitable for pulsar observations. This paper presents results from the MeerTime Large Survey Project and commissioning tests with PTUSE. Highlights include observations of the double pulsar, pulse profiles from 34 millisecond pulsars (MSPs) from a single 2.5-h observation of the Globular cluster Terzan 5, the rotation measure of Ter5O, a 420-sigma giant pulse from the Large Magellanic Cloud pulsar PSR , and nulling identified in the slow pulsar PSR J0633-2015. One of the key design specifications for MeerKAT was absolute timing errors of less than 5 ns using their novel precise time system. Our timing of two bright MSPs confirm that MeerKAT delivers exceptional timing. PSR exhibits a jitter limit of whilst timing of PSR over almost 11 months yields an rms residual of 66 ns with only 4 min integrations. Our results confirm that the MeerKAT is an exceptional pulsar telescope. The array can be split into four separate sub-arrays to time over 1 000 pulsars per day and the future deployment of S-band (1 750-3 500 MHz) receivers will further enhance its capabilities.

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.