This paper starts with a brief historical review about the PNLF and its use as a distance indicator. Then the PNLF distances are compared with Surface Brightness Fluctuations (SBF) distances and Tip of the Red Giant Branch (TRGB) distances. A Monte Carlo method to generate simulated PNLFs is described, leading to the last subject: recent progress in reproducing the expected maximum final mass in old stellar populations, a stellar astrophysics enigma that has been challenging us for quite some time.

Large Synoptic Survey Telescope will make great contributions to many scientific fields. One of the modules will be time-domain astronomy and detection of transient events. In this paper, some considerations about transient events and alerts are presented.

Magnetic instability is a key consideration for filament eruptions and subsequent CMEs. In this contribution we are considering different magnetic conditions for active and non-active regions, such as coronal hole regions and quiet sun, and also active regions of a simple magnetic configuration. The aim is to assess magnetic instability through potential and non-potential field modelling and 3D evaluation of the magnetic decay index. Some eruptive examples from solar cycle 24 using HMI/SDO data are presented, complemented with observations of AIA/SDO.

An exoplanet transiting in front of the disk of its parent star may hide a dark starspot causing a detectable change in the light curve, that allows to infer physical characteristics of the spot such as size and intensity. We have analysed the Kepler Space Telescope observations of the star Kepler-71 in order to search for variabilities in 28 transit light curves. Kepler-71 is a star with 0.923 M ⊙ and 0.816 R ⊙ orbited by the hot Jupiter planet Kepler-71b with radius of 1.0452 R J . The physical parameters of the starspots are determined by fitting the data with a model that simulates planetary transits and enables the inclusion of spots on the stellar surface with different sizes, intensities, and positions. The results show that Kepler-71 is a very active star, with several spot detections, with a mean value of 6 spots per transit with size 0.6 R P and 0.5 I C , as a function of stellar intensity at disk center (maximum value).

We present Spitzer Space Telescope archival mid-infrared (mid-IR) spectroscopy of a sample of eleven planetary nebulae (PNe). The observations, acquired with the Spitzer Infrared Spectrograph (IRS), cover the spectral range 5.2-14.5 μm that includes the H2 0-0 S(2) to S(7) rotational emission lines. This wavelength coverage has allowed us to derive the Boltzmann distribution and calculate the H2 rotational excitation temperature (Tex). The derived excitation temperatures have consistent values ≃ 900 ±70 K for different sources despite their different structural components. We also report the detection of mid-IR ionic lines of [Ar iii], [S iv], and [Ne ii] in most objects, and polycyclic aromatic hydrocarbon (PAH) features in a few cases.

We describe here the parallels in astronomy and earth science datasets, their analyses, and the opportunities for methodology transfer from astroinformatics to geoinformatics. Using example of hydrology, we emphasize how meta-data and ontologies are crucial in such an undertaking. Using the infrastructure being designed for EarthCube - the Virtual Observatory for the earth sciences - we discuss essential steps for better transfer of tools and techniques in the future e.g. domain adaptation. Finally we point out that it is never a one-way process and there is enough for astroinformatics to learn from geoinformatics as well.

An introduction is given to the use of prototype-based models in supervised machine learning. The main concept of the framework is to represent previously observed data in terms of so-called prototypes, which reflect typical properties of the data. Together with a suitable, discriminative distance or dissimilarity measure, prototypes can be used for the classification of complex, possibly high-dimensional data. We illustrate the framework in terms of the popular Learning Vector Quantization (LVQ). Most frequently, standard Euclidean distance is employed as a distance measure. We discuss how LVQ can be equipped with more general dissimilarites. Moreover, we introduce relevance learning as a tool for the data-driven optimization of parameterized distances.

This conference paper reports the recent discoveries of two hot Jupiters (hJs) around weak-line T Tauri stars (wTTS) V830 Tau and TAP 26, through the analysis of spectropolarimetric data gathered within the Magnetic Topologies of Young Stars and the Survival of massive close-in Exoplanets (MaTYSSE) observation programme. HJs are thought to form in the outskirts of protoplanetary discs, then migrate inwards close to their host stars as a result of either planet-disc type II migration or planet-planet scattering. Looking for hJs around young forming stars provides key information on the nature and time scale of such migration processes, as well as how their migration impacts the subsequent architecture of their planetary system. Young stars are however extremely active, to the point that their radial velocity (RV) jitter is around an order of magnitude larger than the potential signatures of close-in gas giants, making them difficult to detect with velocimetry. Three techniques to filter out this activity jitter are presented here, two using Zeeman Doppler Imaging (ZDI) and one using Gaussian Process Regression (GPR).

T-Tauri stars (TTS) are late-type pre-main-sequence (PMS) stars that are gravitationally contracting towards the MS. Those that possess a massive accretion disc are known as classical T-Tauri stars (cTTSs), and those that have exhausted the gas in their inner discs are known as weak-line T-Tauri stars (wTTSs). Magnetic fields largely dictate the angular momentum evolution of TTS and can affect the formation and migration of planets. Thus, characterizing their magnetic fields is critical for testing and developing stellar dynamo models, and trialling scenarios currently invoked to explain low-mass star and planet formation. The MaTYSSE programme (Magnetic Topologies of Young Stars and the Survival of close-in Exoplanets) aims to determine the magnetic topologies of ~30 wTTSs and monitor the long-term topology variability of ~5 cTTSs. We present several wTTSs that have been magnetically mapped thus far (using Zeeman Doppler Imaging), where we find a much wider range of field topologies compared to cTTSs and MS dwarfs with similar internal structures.

Currently large sky area spectral surveys like SDSS, 2dF, and LAMOST, using the new generation of telescopes and observatories, have provided massive spectral data sets for astronomical research. Most of the data can be automatically handled with pipelines, but visually inspection by human eyes is still necessary in several situations, like low SNR spectra, QSO recognition and peculiar spectra mining. Using ASERA, A Spectrum Eye Recognition Assistant, we can set up a team spectral inspection platform. On a preselected spectral data set, members of a team can individually view spectra one by one, find the best match template and estimate the redshift. Results from different members will be gathered and merged to raise the team work efficiency. ASERA mainly targets the spectra of SDSS and LAMOST fits data formats. Other formats can be supported with some conversion. Spectral templates from SDSS and LAMOST pipelines are embedded and users can easily add their own templates. Convenient cross identification interfaces with SDSS, SIMBAD, VIZIER, NED and DSS are also provided. An application example targeting finding strong emission line spectra from LAMOST DR2 is presented.

The discrepancy between abundances computed using optical recombination lines (ORLs) and collisionally excited lines (CELs) is a major, unresolved problem with significant implications for the determination of chemical abundances throughout the Universe. In planetary nebulae (PNe), the most common explanation for the discrepancy is that two different gas phases coexist: a hot component with standard metallicity, and a much colder plasma enhanced in heavy elements. This dual nature is not predicted by mass loss theories, and direct observational support for it is still weak. In this work, we present our recent findings that demonstrate that the largest abundance discrepancies are associated with close binary central stars. OSIRIS-GTC tunable filter imaging of the faint O ii ORLs and MUSE-VLT deep 2D spectrophotometry confirm that O ii ORL emission is more centrally concentrated than that of [Oiii] CELs and, therefore, that the abundance discrepancy may be closely linked to binary evolution.

Astronomy cloud computing environment is a cyber-Infrastructure for Astronomy Research initiated by Chinese Virtual Observatory (China-VO) under funding support from NDRC (National Development and Reform commission) and CAS (Chinese Academy of Sciences). Based on virtualization technology, astronomy cloud computing environment was designed and implemented by China-VO team. It consists of five distributed nodes across the mainland of China. Astronomer can get compuitng and storage resource in this cloud computing environment. Through this environments, astronomer can easily search and analyze astronomical data collected by different telescopes and data centers , and avoid the large scale dataset transportation.

The progress in planetary nebulae (PN) research reported in this symposium is reviewed in the context of our current understanding of the PN phenomenon.

In this article we described CoLiTec software for full automated frames processing. CoLiTec software allows processing the Big Data of observation results as well as processing of data that is continuously formed during observation. The scope of solving tasks includes frames brightness equalization, moving objects detection, astrometry, photometry, etc. Along with the high efficiency of Big Data processing CoLiTec software also ensures high accuracy of data measurements. A comparative analysis of the functional characteristics and positional accuracy was performed between CoLiTec and Astrometrica software. The benefits of CoLiTec used with wide field and low quality frames were observed. The efficiency of the CoLiTec software was proved by about 700.000 observations and over 1.500 preliminary discoveries.

High-resolution, long-slit spectroscopic observations of two planetary nebulae, M1-32 and M3-15 are presented. The observations were obtained with the 2.1-m telescope at the OAN-SPM, and MES spectrograph. M1-32 shows wide wings on the base of its emission lines and M3-15 has two very faint high-velocity knots. To model both PNe we built a 3D model consisting in a jet interacting with an equatorially concentrated slow wind, emulating the presence of a dense torus, by using the hydrodynamical code Yguazú. The synthetic position-velocity (PV) diagrams obtained from our models reproduce well the observed PV diagrams.

Strong gravitational microlensing (GM) events provide us a possibility to determine both the parameters of microlensed source and microlens. GM can be an important clue to understand the nature of dark matter on comparably small spatial and mass scales (i.e. substructure), especially when speaking about the combination of astrometrical and photometrical data about high amplification microlensing events (HAME). In the same time, fitting of HAME lightcurves of microlensed sources is quite time-consuming process. That is why we test here the possibility to apply the statistical machine learning techniques to determine the source and microlens parameters for the set of HAME lightcurves, using the simulated set of amplification curves of sources microlensed by point masses and clumps of DM with various density profiles.

Initiation and development of a M 1.0 class flare of June 12, 2014, was observed by space and ground-based telescopes, including EUV and X-ray imaging spectroscopy by IRIS and RHESSI, and high-resolution optical imaging by 1.6 m New Solar Telescope (NST). Analyzing the NST data, we found small-scale loop-like structures in the region of the magnetic field Polarity Inversion Line (PIL), the emergence and interaction of which caused photospheric brightenings temporarily coinciding with hard X-ray impulses. Detailed studies of the PIL region reveal signatures of photospheric plasma downflows and dissipation of electric currents. The reconstructed magnetic field topology shows a bundle of lines connecting the PIL region with the flare ribbons which were places of chromospheric evaporation observed by IRIS. The observations suggest a scenario with the primary energy release processes located in the low atmospheric layers of the PIL, energizing the overlying large-scale magnetic structure and causing “gentle” chromospheric evaporation.

The Large Synoptic Survey Telescope (LSST), the next-generation optical imaging survey sited at Cerro Pachon in Chile, will provide an unprecedented database of astronomical measurements. The LSST design, with an 8.4m (6.7m effective) primary mirror, a 9.6 sq. deg. field of view, and a 3.2 Gigapixel camera, will allow about 10,000 sq. deg. of sky to be covered twice per night, every three to four nights on average, with typical 5-sigma depth for point sources of r=24.5 (AB). With over 800 observations in ugrizy bands over a 10-year period, these data will enable a deep stack reaching r=27.5 (about 5 magnitudes deeper than SDSS) and faint time-domain astronomy. The measured properties of newly discovered and known astrometric and photometric transients will be publicly reported within 60 sec after observation. The vast database of about 30 trillion observations of 40 billion objects will be mined for the unexpected and used for precision experiments in astrophysics. In addition to a brief introduction to LSST, we discuss a number of astro-statistical challenges that need to be overcome to extract maximum information and science results from LSST dataset.

The current archives of LAMOST multi-object spectrograph contain millions of fully reduced spectra, from which the automatic pipelines have produced catalogues of many parameters of individual objects, including their approximate spectral classification. This is, however, mostly based on the global shape of the whole spectrum and on integral properties of spectra in given bandpasses, namely presence and equivalent width of prominent spectral lines, while for identification of some interesting object types (e.g. Be stars or quasars) the detailed shape of only a few lines is crucial. Here the machine learning is bringing a new methodology capable of improving the reliability of classification of such objects even in boundary cases.

We present results of Spark-based semi-supervised machine learning of LAMOST spectra attempting to automatically identify the single and double-peak emission of Hα line typical for Be and B[e] stars. The labelled sample was obtained from archive of 2m Perek telescope at Ondřejov observatory. A simple physical model of spectrograph resolution was used in domain adaptation to LAMOST training domain. The resulting list of candidates contains dozens of Be stars (some are likely yet unknown), but also a bunch of interesting objects resembling spectra of quasars and even blazars, as well as many instrumental artefacts. The verification of a nature of interesting candidates benefited considerably from cross-matching and visualisation in the Virtual Observatory environment.

This review presents the latest advances in the nebular studies of post-AGB objects. Post-AGB stars are great tools to test nucleosynthesis and evolution models for stars of low and intermediate masses, and the evolution of dust in harsh environment. I will present the newly discovered class of post-RGB stars, formed via binary interaction on the RGB. Binary systems can also lead to the formation of two class of aspherical post-AGB, the Proto-Planetary Nebulae and the naked post-AGBs (dusty RV Taus, a.k.a. Van Winckel’s stars).