THE POSSIBILITIES OF THE “CLUSTER CARTOGRAPHY” TOOL FOR THE STUDY OF THE INNER STRUCTURES OF GALAXY CLUSTERS

We describe the possibilities of the “Cluster Cartography” tool which was created for detailed study of the 2D distribution of galaxies in the clusters. The main tasks of the “Cluster Cartography” tool were the detailed study of the morphologyof galaxy clusters using the statistically significant numerical criteria as well as to detect their regular peculiarities. The tool allows to create the 2D map with positions of galaxies in the cluster field and show for each cluster member its shape and orientation as a best-fit ellipse using input catalogue data. The size of symbols for galaxies correspond to input data.It may reflect the galaxy image in arcseconds from catalogue in the map 4000×4000arcsec. Another way connects the size of the symbol with the magnitude of the galaxy. Tool is able to build the map in four modes: the symbols are dots; the symbols are circles with diameters reflected the magnitudes of galaxies; the symbols are ellipses with size reflected the magnitudesand both ellipticities and orientation from the input catalogue; the symbols illustrate the shape of galaxies in projection to the celestial sphere. The “Cluster Cartography” algorithms allow to detect the standard cases in galaxy distribution, suchas the degree of concentration to the cluster center and/or to some line on a statistically significant level using the numerical criteria. Also “Cluster Cartography” allows to detect other features, such as crosses, semi-crosses, complex crosses and short compact chains, as well as to export the list of galaxies forming the peculiarities for the futurestudy. The final version of the “Cluster Cartography” allows to realize the modern scheme for detailed morphological classification of galaxy clusters. The “Cluster Cartography” is powerful and perspective tool for study of features of galaxy clusters.

CARMENES input catalogue of M dwarfs

Context. The relevance of M dwarfs in the search for potentially habitable Earth-sized planets has grown significantly in the last years. Aims. In our on-going effort to comprehensively and accurately characterise confirmed and potential planet-hosting M dwarfs, in particular for the CARMENES survey, we have carried out a comprehensive multi-band photometric analysis involving spectral energy distributions, luminosities, absolute magnitudes, colours, and spectral types, from which we have derived basic astrophysical parameters. Methods. We have carefully compiled photometry in 20 passbands from the ultraviolet to the mid-infrared, and combined it with the latest parallactic distances and close-multiplicity information, mostly from Gaia DR2, of a sample of 2479 K5 V to L8 stars and ultracool dwarfs, including 2210 nearby, bright M dwarfs. For this, we made extensive use of Virtual Observatory tools. Results. We have homogeneously computed accurate bolometric luminosities and effective temperatures of 1843 single stars, derived their radii and masses, studied the impact of metallicity, and compared our results with the literature. The over 40 000 individually inspected magnitudes, together with the basic data and derived parameters of the stars, individual and averaged by spectral type, have been made public to the astronomical community. In addition, we have reported 40 new close multiple systems and candidates (ρ < 3.3 arcsec) and 36 overluminous stars that are assigned to young Galactic populations. Conclusions. In the new era of exoplanet searches around M dwarfs via transit (e.g. TESS, PLATO) and radial velocity (e.g. CARMENES, NIRPS+HARPS), this work is of fundamental importance for stellar and therefore planetary parameter determination.

An optimized tiling pattern for multiobject spectroscopic surveys: application to the 4MOST survey

ABSTRACT Large multiobject spectroscopic surveys require automated algorithms to optimize their observing strategy. One of the most ambitious upcoming spectroscopic surveys is the 4MOST survey. The 4MOST survey facility is a fibre-fed spectroscopic instrument on the VISTA telescope with a large enough field of view to survey a large fraction of the southern sky within a few years. Several Galactic and extragalactic surveys will be carried out simultaneously, so the combined target density will strongly vary. In this paper, we describe a new tiling algorithm that can naturally deal with the large target density variations on the sky and which automatically handles the different exposure times of targets. The tiling pattern is modelled as a marked point process, which is characterized by a probability density that integrates the requirements imposed by the 4MOST survey. The optimal tilling pattern with respect to the defined model is estimated by the tiles configuration that maximizes the proposed probability density. In order to achieve this maximization a simulated annealing algorithm is implemented. The algorithm automatically finds an optimal tiling pattern and assigns a tentative sky brightness condition and exposure time for each tile, while minimizing the total execution time that is needed to observe the list of targets in the combined input catalogue of all surveys. Hence, the algorithm maximizes the long-term observing efficiency and provides an optimal tiling solution for the survey. While designed for the 4MOST survey, the algorithm is flexible and can with simple modifications be applied to any other multiobject spectroscopic survey.

Probabilistic fibre-to-target assignment algorithm for multi-object spectroscopic surveys

Context. Several new multi-object spectrographs are currently planned or under construction that are capable of observing thousands of Galactic and extragalactic objects simultaneously. Aims. In this paper we present a probabilistic fibre-to-target assignment algorithm that takes spectrograph targeting constraints into account and is capable of dealing with multiple concurrent surveys. We present this algorithm using the 4-m Multi-Object Spectroscopic Telescope (4MOST) as an example. Methods. The key idea of the proposed algorithm is to assign probabilities to fibre-target pairs. The assignment of probabilities takes the fibre positioner’s capabilities and constraints into account. Additionally, these probabilities include requirements from surveys and take the required exposure time, number density variation, and angular clustering of targets across each survey into account. The main advantage of a probabilistic approach is that it allows for accurate and easy computation of the target selection function for the different surveys, which involves determining the probability of observing a target, given an input catalogue. Results. The probabilistic fibre-to-target assignment allows us to achieve maximally uniform completeness within a single field of view. The proposed algorithm maximises the fraction of successfully observed targets whilst minimising the selection bias as a function of exposure time. In the case of several concurrent surveys, the algorithm maximally satisfies the scientific requirements of each survey and no specific survey is penalised or prioritised. Conclusions. The algorithm presented is a proposed solution for the 4MOST project that allows for an unbiased targeting of many simultaneous surveys. With some modifications, the algorithm may also be applied to other multi-object spectroscopic surveys.

Efficient selection of quasar candidates based on optical and infrared photometric data using machine learning

ABSTRACT We aim to select quasar candidates based on the two large survey databases, Pan-STARRS and AllWISE. Exploring the distribution of quasars and stars in the colour spaces, we find that the combination of infrared and optical photometry is more conducive to select quasar candidates. Two new colour criterions (yW1W2 and iW1zW2) are constructed to distinguish quasars from stars efficiently. With iW1zW2, 98.30 per cent of star contamination is eliminated, while 99.50 per cent of quasars are retained, at least to the magnitude limit of our training set of stars. Based on the optical and infrared colour features, we put forward an efficient schema to select quasar candidates and high-redshift quasar candidates, in which two machine learning algorithms (XGBoost and SVM) are implemented. The XGBoost and SVM classifiers have proven to be very effective with accuracy of $99.46{{\ \rm per\ cent}}$ when 8Color as input pattern and default model parameters. Applying the two optimal classifiers to the unknown Pan-STARRS and AllWISE cross-matched data set, a total of 2 006 632 intersected sources are predicted to be quasar candidates given quasar probability larger than 0.5 (i.e. PQSO &gt; 0.5). Among them, 1 201 211 have high probability (PQSO &gt; 0.95). For these newly predicted quasar candidates, a regressor is constructed to estimate their redshifts. Finally 7402 z &gt; 3.5 quasars are obtained. Given the magnitude limitation and site of the LAMOST telescope, part of these candidates will be used as the input catalogue of the LAMOST telescope for follow-up observation, and the rest may be observed by other telescopes.

CARMENES input catalogue of M dwarfs

Aims. The main goal of this work is to measure rotation periods of the M-type dwarf stars being observed by the CARMENES exoplanet survey to help distinguish radial-velocity signals produced by magnetic activity from those produced by exoplanets. Rotation periods are also fundamental for a detailed study of the relation between activity and rotation in late-type stars. Methods. We look for significant periodic signals in 622 photometric time series of 337 bright, nearby M dwarfs obtained by long-time baseline, automated surveys (MEarth, ASAS, SuperWASP, NSVS, Catalina, ASAS-SN, K2, and HATNet) and for 20 stars which we obtained with four 0.2–0.8 m telescopes at high geographical latitudes. Results. We present 142 rotation periods (73 new) from 0.12 d to 133 d and ten long-term activity cycles (six new) from 3.0 a to 11.5 a. We compare our determinations with those in the existing literature; we investigate the distribution of Prot in the CARMENES input catalogue, the amplitude of photometric variability, and their relation to v sini and pEW(Hα); and we identify three very active stars with new rotation periods between 0.34 d and 23.6 d.

KIC 5110739: A new Red Giant in NGC 6819

The Kepler Input Catalogue (KIC) misclassified a number of red giant stars as sub giants. This could have resulted from the large uncertainties in the KIC surface gravities. This resulted in 1523 stars which were recently classified as red giant stars. The cluster membership of the 1523 red giant stars was determined using age, distance modulus, and variation of colour magnitude with large frequency separation. We found that one star, KIC 5110739, is a member of NGC 6819.