The Extremely Luminous Quasar Survey (ELQS) in the SDSS footprint I.: Infrared Based Candidate Selection

Studies of the most luminous quasars at high redshift directly probe the evolution of the most massive black holes in the early Universe and their connection to massive galaxy formation. However, extremely luminous quasars at high redshift are very rare objects. Only wide area surveys have a chance to constrain their population. The Sloan Digital Sky Survey (SDSS) has so far provided the most widely adopted measurements of the quasar luminosity function (QLF) at $z>3$. However, a careful re-examination of the SDSS quasar sample revealed that the SDSS quasar selection is in fact missing a significant fraction of $z\gtrsim3$ quasars at the brightest end. We have identified the purely optical color selection of SDSS, where quasars at these redshifts are strongly contaminated by late-type dwarfs, and the spectroscopic incompleteness of the SDSS footprint as the main reasons. Therefore we have designed the Extremely Luminous Quasar Survey (ELQS), based on a novel near-infrared JKW2 color cut using WISE AllWISE and 2MASS all-sky photometry, to yield high completeness for very bright ($m_{\rm{i}} < 18.0$) quasars in the redshift range of $3.0\leq z\leq5.0$. It effectively uses random forest machine-learning algorithms on SDSS and WISE photometry for quasar-star classification and photometric redshift estimation. The ELQS will spectroscopically follow-up $\sim 230$ new quasar candidates in an area of $\sim12000\,\rm{deg}^2$ in the SDSS footprint, to obtain a well-defined and complete quasars sample for an accurate measurement of the bright-end quasar luminosity function at $3.0\leq z\leq5.0$. In this paper we present the quasar selection algorithm and the quasar candidate catalog.Comment: 16 pages, 8 figures, 9 tables; ApJ in pres

Biomarker discovery and redundancy reduction towards classification using a multi-factorial MALDI-TOF MS T2DM mouse model dataset

Diabetes like many diseases and biological processes is not mono-causal. On the one hand multifactorial studies with complex experimental design are required for its comprehensive analysis. On the other hand, the data from these studies often include a substantial amount of redundancy such as proteins that are typically represented by a multitude of peptides. Coping simultaneously with both complexities (experimental and technological) makes data analysis a challenge for Bioinformatics

How to Find More Supernovae with Less Work: Object Classification Techniques for Difference Imaging

We present the results of applying new object classification techniques to difference images in the context of the Nearby Supernova Factory supernova search. Most current supernova searches subtract reference images from new images, identify objects in these difference images, and apply simple threshold cuts on parameters such as statistical significance, shape, and motion to reject objects such as cosmic rays, asteroids, and subtraction artifacts. Although most static objects subtract cleanly, even a very low false positive detection rate can lead to hundreds of non-supernova candidates which must be vetted by human inspection before triggering additional followup. In comparison to simple threshold cuts, more sophisticated methods such as Boosted Decision Trees, Random Forests, and Support Vector Machines provide dramatically better object discrimination. At the Nearby Supernova Factory, we reduced the number of non-supernova candidates by a factor of 10 while increasing our supernova identification efficiency. Methods such as these will be crucial for maintaining a reasonable false positive rate in the automated transient alert pipelines of upcoming projects such as PanSTARRS and LSST.Comment: 25 pages; 6 figures; submitted to Ap

Support Vector Machine classification of strong gravitational lenses

The imminent advent of very large-scale optical sky surveys, such as Euclid and LSST, makes it important to find efficient ways of discovering rare objects such as strong gravitational lens systems, where a background object is multiply gravitationally imaged by a foreground mass. As well as finding the lens systems, it is important to reject false positives due to intrinsic structure in galaxies, and much work is in progress with machine learning algorithms such as neural networks in order to achieve both these aims. We present and discuss a Support Vector Machine (SVM) algorithm which makes use of a Gabor filterbank in order to provide learning criteria for separation of lenses and non-lenses, and demonstrate using blind challenges that under certain circumstances it is a particularly efficient algorithm for rejecting false positives. We compare the SVM engine with a large-scale human examination of 100000 simulated lenses in a challenge dataset, and also apply the SVM method to survey images from the Kilo-Degree Survey.Comment: Accepted by MNRA

Near-infrared spectroscopy of AGB star candidates in Fornax, Sculptor and NGC 6822

Context: The Asymptotic Giant Branch (AGB) phase is characterised by substantial mass loss that is accompanied by the formation of dust. In extreme cases this will make the star no longer visible in the optical. For a better understanding of AGB evolution it is important to identify and characterise these very red AGB stars. Aims: The first aim of this article is to improve the census of red AGB stars in three Local Group galaxies, based on near-IR spectroscopic observations of new candidates with red 2MASS (J-K) colours. The opportunity is taken to compare the near-IR spectra with those of Milky Way stars. Methods: We used ISAAC on the ESO VLT to take J and H-band spectra of 36 targets in Fornax, Sculptor and NGC 6822. Results: Twelve new C-stars are found in Fornax, and one is confirmed in Sculptor. All C-stars have (J-K) > 1.6, and are brighter than -3.55 in bolometric magnitude. Ten new oxygen-rich late-type giant stars are identified in Fornax, but none is extremely red or very luminous. Five luminous O-rich AGB stars are identified in NGC 6822, of which 3 show water absorption, indicative of spectral type M. Again, none is as red as Milky Way OH/IR stars, but in this galaxy the list of candidate AGB stars is biased against very red objects. In some C-stars with (J-K)>2 an extremely strong 1.53 $\mu$m absorption band is found. These stars are probably all Mira variables and the feature is related to the low temperature, high density chemistry that is a first step towards dust formation and mass loss.Comment: A&A accepte