A Simple Likelihood Method for Quasar Target Selection

We present a new method for quasar target selection using photometric fluxes and a Bayesian probabilistic approach. For our purposes we target quasars using Sloan Digital Sky Survey (SDSS) photometry to a magnitude limit of g=22. The efficiency and completeness of this technique is measured using the Baryon Oscillation Spectroscopic Survey (BOSS) data, taken in 2010. This technique was used for the uniformly selected (CORE) sample of targets in BOSS year one spectroscopy to be realized in the 9th SDSS data release. When targeting at a density of 40 objects per sq-deg (the BOSS quasar targeting density) the efficiency of this technique in recovering z>2.2 quasars is 40%. The completeness compared to all quasars identified in BOSS data is 65%. This paper also describes possible extensions and improvements for this techniqueComment: Updated to accepted version for publication in the Astrophysical Journal. 10 pages, 10 figures, 3 table

Selection, Prioritization, and Characteristics of Kepler Target Stars

The Kepler Mission began its 3.5-year photometric monitoring campaign in May 2009 on a select group of approximately 150,000 stars. The stars were chosen from the ~half million in the field of view that are brighter than 16th magnitude. The selection criteria are quantitative metrics designed to optimize the scientific yield of the mission with regards to the detection of Earth-size planets in the habitable zone. This yields more than 90,000 G-type stars on or close to the Main Sequence, >20,000 of which are brighter than 14th magnitude. At the temperature extremes, the sample includes approximately 3,000 M-type dwarfs and a small sample of O and B-type MS stars <200. Small numbers of giants are included in the sample which contains ~5,000 stars with surface gravities log(g) < 3.5. We present a brief summary of the selection process and the stellar populations it yields in terms of surface gravity, effective temperature, and apparent magnitude. In addition to the primary, statistically-derived target set, several ancillary target lists were manually generated to enhance the science of the mission, examples being: known eclipsing binaries, open cluster members, and high proper-motion stars.Comment: Submitted to Astrophysical Journal Letter

Target Selection for the SDSS-IV APOGEE-2 Survey

APOGEE-2 is a high-resolution, near-infrared spectroscopic survey observing roughly 300,000 stars across the entire sky. It is the successor to APOGEE and is part of the Sloan Digital Sky Survey IV (SDSS-IV). APOGEE-2 is expanding upon APOGEE's goals of addressing critical questions of stellar astrophysics, stellar populations, and Galactic chemodynamical evolution using (1) an enhanced set of target types and (2) a second spectrograph at Las Campanas Observatory in Chile. APOGEE-2 is targeting red giant branch (RGB) and red clump (RC) stars, RR Lyrae, low-mass dwarf stars, young stellar objects, and numerous other Milky Way and Local Group sources across the entire sky from both hemispheres. In this paper, we describe the APOGEE-2 observational design, target selection catalogs and algorithms, and the targeting-related documentation included in the SDSS data releases.Comment: 19 pages, 6 figures. Accepted to A

The Gaia-ESO Survey: the selection function of the Milky Way field stars

The Gaia-ESO Survey was designed to target all major Galactic components (i.e., bulge, thin and thick discs, halo and clusters), with the goal of constraining the chemical and dynamical evolution of the Milky Way. This paper presents the methodology and considerations that drive the selection of the targeted, allocated and successfully observed Milky Way field stars. The detailed understanding of the survey construction, specifically the influence of target selection criteria on observed Milky Way field stars is required in order to analyse and interpret the survey data correctly. We present the target selection process for the Milky Way field stars observed with VLT/FLAMES and provide the weights that characterise the survey target selection. The weights can be used to account for the selection effects in the Gaia-ESO Survey data for scientific studies. We provide a couple of simple examples to highlight the necessity of including such information in studies of the stellar populations in the Milky Way.Comment: 18 pages, 19 figures, Accepted for publication in MNRAS (April 25, 2016

Parameterized Inapproximability of Target Set Selection and Generalizations

In this paper, we consider the Target Set Selection problem: given a graph and a threshold value $thr(v)$ for any vertex $v$ of the graph, find a minimum size vertex-subset to "activate" s.t. all the vertices of the graph are activated at the end of the propagation process. A vertex $v$ is activated during the propagation process if at least $thr(v)$ of its neighbors are activated. This problem models several practical issues like faults in distributed networks or word-to-mouth recommendations in social networks. We show that for any functions $f$ and $\rho$ this problem cannot be approximated within a factor of $\rho(k)$ in $f(k) \cdot n^{O(1)}$ time, unless FPT = W[P], even for restricted thresholds (namely constant and majority thresholds). We also study the cardinality constraint maximization and minimization versions of the problem for which we prove similar hardness results

Optimising Spectroscopic and Photometric Galaxy Surveys: Efficient Target Selection and Survey Strategy

The next generation of spectroscopic surveys will have a wealth of photometric data available for use in target selection. Selecting the best targets is likely to be one of the most important hurdles in making these spectroscopic campaigns as successful as possible. Our ability to measure dark energy depends strongly on the types of targets that we are able to select with a given photometric data set. We show in this paper that we will be able to successfully select the targets needed for the next generation of spectroscopic surveys. We also investigate the details of this selection, including optimisation of instrument design and survey strategy in order to measure dark energy. We use color-color selection as well as neural networks to select the best possible emission line galaxies and luminous red galaxies for a cosmological survey. Using the Fisher matrix formalism we forecast the efficiency of each target selection scenario. We show how the dark energy figures of merit change in each target selection regime as a function of target type, survey time, survey density and other survey parameters. We outline the optimal target selection scenarios and survey strategy choices which will be available to the next generation of spectroscopic surveys.Comment: 16 pages, 22 figures, accepted to MNRAS in dec 201

Target Set Selection Parameterized by Clique-Width and Maximum Threshold

The Target Set Selection problem takes as an input a graph $G$ and a non-negative integer threshold \mbox{thr}(v) for every vertex $v$. A vertex $v$ can get active as soon as at least \mbox{thr}(v) of its neighbors have been activated. The objective is to select a smallest possible initial set of vertices, the target set, whose activation eventually leads to the activation of all vertices in the graph. We show that Target Set Selection is in FPT when parameterized with the combined parameters clique-width of the graph and the maximum threshold value. This generalizes all previous FPT-membership results for the parameterization by maximum threshold, and thereby solves an open question from the literature. We stress that the time complexity of our algorithm is surprisingly well-behaved and grows only single-exponentially in the parameters

Classification and Target Group Selection Based Upon Frequent Patterns

In this technical report , two new algorithms based upon frequent patterns are proposed. One algorithm is a classification method. The other one is an algorithm for target group selection. In both algorithms, first of all, the collection of frequent patterns in the training set is constructed. Choosing an appropriate data structure allows us to keep the full collection of frequent patterns in memory. The classification method utilizes directly this collection. Target group selection is a known problem in direct marketing. Our selection algorithm is based upon the collection of frequent patterns.classification;association rules;frequent item sets;target group selection