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.

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In this paper, we have selected a sample of 64 teraelectronvolt blazars, with redshift, from those classified in the fourth Fermi Large Area Telescope source catalog a) . We have obtained the values of the relevant physical parameters by performing a log-parabolic fitting of the average-state multiwavelength spectral energy distributions. We estimate the range of the radiation zone parameters, such as the Doppler factor (D), the magnetic field strength (B), the radiative zone radius (R) and the peak Lorentz factor (γ p ) of nonthermal electrons. Here, we show that (1) there is a strong linear positive correlation between the intrinsic synchrotron peak frequency and the intrinsic inverse Compton scattering (ICs) peak frequency among different types of blazars; (2) if radio bands are excluded, the spectral index of each band is negatively correlated with the intrinsic peak frequency; (3) there is a strong linear negative correlation between the curvature at the peak and the intrinsic peak frequency of the synchrotron bump, and a weak positive correlation between the curvature at the peak and the intrinsic peak frequency of the ICs bump; (4) there is a strong linear positive correlation between the intrinsic ICs peak luminosity and intrinsic γ-ray luminosity and between the intrinsic ICs peak frequency and peak Lorentz factor;(5) there is a strong negative linear correlation between log B and log γ p ; and (6) there is no correlation between log R and log γ p .

In the era of large photometric surveys, the importance of automated and accurate classification is rapidly increasing. Specifically, the separation of resolved and unresolved sources in astronomical imaging is a critical initial step for a wide array of studies, ranging from Galactic science to large scale structure and cosmology. Here, we present our method to construct a large, deep catalog of point sources utilizing Pan-STARRS1 (PS1) 3π survey data, which consists of ∼3×10 9 sources with m 23.5 mag. We develop a supervised machine-learning methodology, using the random forest (RF) algorithm, to construct the PS1 morphology model. We train the model using ∼5×10 4 PS1 sources with HST COSMOS morphological classifications and assess its performance using ∼4×10 6 sources with Sloan Digital Sky Survey (SDSS) spectra and ∼2×10 8 Gaia sources. We construct 11 "white flux" features, which combine PS1 flux and shape measurements across 5 filters, to increase the signal-to-noise ratio relative to any individual filter. The RF model is compared to 3 alternative models, including the SDSS and PS1 photometric classification models, and we find that the RF model performs best. By number the PS1 catalog is dominated by faint sources (m 21 mag), and in this regime the RF model significantly outperforms the SDSS and PS1 models. For time-domain surveys, identifying unresolved sources is crucial for inferring the Galactic or extragalactic origin of new transients. We have classified ∼1.5×10 9 sources using the RF model, and these results are used within the Zwicky Transient Facility real-time pipeline to automatically reject stellar sources from the extragalactic alert stream.