A GPU based single-pulse search pipeline(GSP)with database and its application to the Commensal Radio Astronomy FAST Survey(CRAFTS)

We developed a GPU based single-pulse search pipeline(GSP)with a candidate-archiving database.Largely based upon the infrastructure of the open source PulsaR Exploration and Search Toolkit(PRESTO),GSP implements GPU acceleration of the de-dispersion and integrates a candidate-archiving database.We applied GSP to the data streams from the Commensal Radio Astronomy FAST Survey(CRAFTS),which resulted in quasi-real-time processing.The integrated candidate database facilitates synergistic usage of multiple machine-learning tools and thus improves efficient identification of radio pulsars such as rotating radio transients(RRATs)and fast radio bursts(FRBs).We first tested GSP on pilot CRAFTS observations with the FAST Ultra-Wide Band(UWB)receiver.GSP detected all pulsars known from the the Parkes multibeam pulsar survey in the corresponding sky area covered by the FAST-UWB.GSP also discovered 13 new pulsars.We measured the computational efficiency of GSP to be~120 times faster than the original PRESTO and~60 times faster than an MPI-parallelized version of PRESTO.

NUV Star Catalog from the Lunar-based Ultraviolet Telescope Survey: First Release

We present a star catalog extracted from the Lunar-based Ultraviolet Telescope （LUT） survey program. LUT＇s observable sky area is a circular belt around the Moon＇s north pole, and the survey program covers a preferred area of about 2400 deg2 which includes a region of the Galactic plane. The data are processed with an automatic pipeline which copes with stray light contamination, artificial sources, cosmic rays, flat field calibration, photometry and so on. In the first release version, the catalog provides high confidence sources which have been cross-identified with the Tycho-2 catalog. All the sources have signalto-noise ratio larger than 5, and the corresponding magnitude limit is typically 14.4 mag, but can be as deep as -16 mag if stray light contamination is at the lowest level. A total of 86 467 stars are recorded in the catalog. The full catalog in electronic form is available online.

Hydrogen Bonds of C=S, C=Se and C=Te with C-H in Small-Organic Molecule Compounds Derived from the Cambridge Structural Database (CSD)

Considerable interest in hydrogen bonding involving chalcogen has been growing since the IUPAC committee has redefined hydrogen bonding. Not only the focus is on unconventional acceptors, but also on donors not discussed before. It has been mentioned in previous studies that the proton of the H-C group could be involved in hydrogen bonding, but with conventional acceptors. In this study, we explored the ability of hydrogen bond formation of Se, S and Te acceptors with the H-C donor using Cambridge Structural Database in conjunction with Ab Initio calculations. In the CSD, there are respectively 256, 6249 and 11 R1,R2,-C=Se, R1,R2,-C=S and R1,R2,-C=Te structures that form hydrogen bonds, in which the N,N groups are majority. Except for C=S acceptor which can form a hydrogen bond with its C, C group, both C=Se and C=Te acceptors could form a hydrogen bond only with N,C and N,N groups. CSD analysis shows very similar d (norm) around -0.04 Å, while DFT-calculated interaction for N,C and N,N groups are also similar. Both interaction distances derived from CSD analysis and DFT-calculated interaction energies demonstrate that the acceptors form stable complexes with H-CF3. Besides hydrogen bonds, dispersion interactions are forces stabilizing the complexes since their contribution can reach 50%. Analysis of intra-molecular geometries and Ab Initio partial charges show that this bonding stems from resonance induced Cδ+=Xδ- dipoles. In many respects, both C=Se, C=S and C=Te are similar to C=S, with similar d (norm) and calculated interaction strengths.

Astronomical data fusion tool based on PostgreSQL

With the application of advanced astronomical technologies, equipments and methods all over the world, astronomical observations cover the range from radio, infrared, visible light, ultraviolet, X-ray and gamma-ray bands, and enter into the era of full wavelength astronomy. How to effectively integrate data from different ground- and space-based observation equipments, different observers, different bands and different observation times, requires data fusion technology. In this paper we introduce a cross-match tool that is developed in the Python language, is based on the PostgreSQL database and uses Q3C as the core index, facilitating the cross-match work of massive astronomical data. It provides four different cross- match functions, namely： （I） cross-match of the custom error range; （II） cross-match of catalog errors; （III） cross-match based on the elliptic error range; （IV） cross-match of the nearest neighbor algorithm. The resulting cross-matched set provides a good foundation for subsequent data mining and statistics based on multiwavelength data. The most advantageous aspect of this tool is a user-oriented tool applied locally by users. By means of this tool, users can easily create their own databases, manage their own data and cross- match databases according to their requirements. In addition, this tool is also able to transfer data from one database into another database. More importantly, it is easy to get started with the tool and it can be used by astronomers without writing any code.