On 2017 August 17 a binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors. The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray burst (GRB 170817A) with a time delay of ∼ 1.7 s with respect to the merger time. From the gravitational-wave signal, the source was initially localized to a sky region of 31 deg2 at a luminosity distance of 40 − 8 + 8 Mpc and with component masses consistent with neutron stars. The component masses were later measured to be in the range 0.86 to 2.26 M ⊙ . An extensive observing campaign was launched across the electromagnetic spectrum leading to the discovery of a bright optical transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC 4993 (at ∼ 40 Mpc ) less than 11 hours after the merger by the One-Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The optical transient was independently detected by multiple teams within an hour. Subsequent observations targeted the object and its environment. Early ultraviolet observations revealed a blue transient that faded within 48 hours. Optical and infrared observations showed a redward evolution over ∼10 days. Following early non-detections, X-ray and radio emission were discovered at the transient’s position ∼ 9 and ∼ 16 days, respectively, after the merger. Both the X-ray and radio emission likely arise from a physical process that is distinct from the one that generates the UV/optical/near-infrared emission. No ultra-high-energy gamma-rays and no neutrino candidates consistent with the source were found in follow-up searches. These observations support the hypothesis that GW170817 was produced by the merger of two neutron stars in NGC 4993 followed by a short gamma-ray burst (GRB 170817A) and a kilonova/macronova powered by the radioactive decay of r-process nuclei synthesized in the ejecta.

We present the second major release of data from the SAMI Galaxy Survey. Data Release Two includes data for 1559 galaxies, about 50% of the full survey. Galaxies included have a redshift range 0.004 < z < 0.113 and a large stellar mass range 7.5 < log(M /M ) < 11.6. The core data for each galaxy consist of two primary spectral cubes covering the blue and red optical wavelength ranges. For each primary cube we also provide three spatially binned spectral cubes and a set of standardised aperture spectra. For each core data product we provide a set of value-added data products. This includes all emission line value-added products from Data Release One, expanded to the larger sample. In addition we include stellar kinematic and stellar population value-added products derived from absorption line measurements. The data are provided online through Australian Astronomical Optics' Data Central. We illustrate the potential of this release by presenting the distribution of ∼ 350, 000 stellar velocity dispersion measurements from individual spaxels as a function of R/R e , divided in four galaxy mass bins. In the highest stellar mass bin (log(M /M ) > 11), the velocity dispersion strongly increases towards the centre, whereas below log(M /M ) < 10 we find no evidence for a clear increase in the central velocity dispersion. This suggests a transition mass around log(M /M ) ∼ 10 for galaxies with or without a dispersion-dominated bulge.

We study the properties of 66 galaxies with kinematically misaligned gas and stars from MaNGA survey. The fraction of kinematically misaligned galaxies varies with galaxy physical parameters, i.e. M * , SFR and sSFR. According to their sSFR, we further classify these 66 galaxies into three categories, 10 star-forming, 26 "Green Valley" and 30 quiescent ones. The properties of different types of kinematically misaligned galaxies are different in that the star-forming ones have positive gradient in D n 4000 and higher gas-phase metallicity, while the green valley/quiescent ones have negative D n 4000 gradients and lower gas-phase metallicity on average. There is evidence that all types of the kinematically misaligned galaxies tend to live in more isolated environment. Based on all these observational results, we propose a scenario for the formation of star forming galaxies with kinematically misaligned gas and stars − the progenitor accretes misaligned gas from a gas-rich dwarf or cosmic web, the cancellation of angular momentum from gas-gas collisions between the pre-existing gas and the accreted gas largely accelerates gas inflow, leading to fast centrally-concentrated star-formation. The higher metallicity is due to enrichment from this star formation. For the kinematically misaligned green valley and quiescent galaxies, they might be formed through gas-poor progenitors accreting kinematically misaligned gas from satellites which are smaller in mass.

We study the problem of utilizing human intelligence to categorize a large number of objects. In this problem, given a category hierarchy and a set of objects, we can ask humans to check whether an object belongs to a category, and our goal is to find the most cost-effective strategy to locate the appropriate category in the hierarchy for each object, such that the cost (i.e., the number of questions to ask humans) is minimized. There are many important applications of this problem, including image classification and product categorization. We develop an online framework, in which category distribution is gradually learned and thus an effective order of questions are adaptively determined. We prove that even if the true category distribution is known in advance, the problem is computationally intractable. We develop an approximation algorithm, and prove that it achieves an approximation factor of 2. We also show that there is a fully polynomial time approximation scheme for the problem. Furthermore, we propose an online strategy which achieves nearly the same performance guarantee as the offline optimal strategy, even if there is no knowledge about category distribution beforehand. Experiments on a real crowdsourcing platform demonstrate the effectiveness of our method.