Sky Surveys

Abstract

Abstract:Sky surveys represent a fundamental data basis for astronomy. We use them to map in a systematic way the universe and its constituents, and to discover new types of objects or phenomena. We review the subject, with an emphasis on the wide-field, imaging surveys, placing them in a broader scientific and historical context. Surveys are now the largest data generators in astronomy, propelled by the advances in information and computation technology, and have transformed the ways in which astronomy is done. This trend is bound to continue, especially with the new generation of synoptic sky surveys that cover wide areas of the sky repeatedly, and open a new time domain of discovery. We describe the variety and the general properties of surveys, illustrated by a number of examples, the ways in which they may be quantified and compared, and offer some figures of merit that can be used to compare their scientific discovery potential. Surveys enable a very wide range of science, and that is perhaps their key unifying characteristic. As new domains of the observable parameter space open up thanks to the advances in technology, surveys are often the initial step in their exploration. Some science can be done with the survey data alone (or a combination of data from different surveys), and some requires a targeted follow-up of potentially interesting sources selected from surveys. Surveys can be used to generate large, statistical samples of objects that can be studied as populations, or as tracers of larger structures to which they belong. They can be also used to discover or generate samples of rare or unusual objects, and may lead to discoveries of some previously unknown types. We discuss a general framework of parameter spaces that can be used for an assessment and comparison of different surveys, and the strategies for their scientific exploration. As we are moving into the Petascale regime and beyond, an effective processing and scientific exploitation of such large data sets and data streams poses many challenges, some of which are specific to any given survey, and some of which may be addressed in the framework of Virtual Observatory and Astroinformatics. The exponential growth of data volumes and complexity makes a broader application of data mining and knowledge discovery technologies critical in order to take a full advantage of this wealth of information. Finally, we discuss some outstanding challenges and prospects for the future.* We dedicate this chapter to the memory of three pioneers of sky surveys, Fritz Zwicky (1898-1974), Bogdan Paczynski (1940), and John Huchra (1948

Analysis of the Cramér-Rao Bound in the Joint Estimation of Astrometry and Photometry

“…Since some recent large-area surveys and robotic telescopes are exploring the undersampled regime, e.g., SuperWASP 6 , 13.7 arcsec/pix (described by Pollacco et al (2006)), TRAPPIST 7 , 0.64 arcsec/pix (described by Gillon et al (2011)), the Catalina Real-Time Transient Survey 8 , 0.98, 1.84 and 2.57 arcsec/pix (described by Djorgovski et al (2011), or the La Silla-QUEST Variability Survey 9 , 0.88 arcsec/pix (described by Baltay et al (2013)) among others, it is interesting to quantify the impact of this design feature into the predicted Cramér-Rao bound. To estimate the effect of neglecting the cross-dependency between flux and astrometry, Table 1 compares the 1D and 2D Cramér-Rao limits as a function of the pixel size ∆x, and the S/N of the source, adopting the same parameters as those of Figure 3.…”

Intelligence and Security Databases (ISD) vs. 'Hard Core' Sciences Databases (HCSD): Challenges and Opportunities

“…Two clusters are merged in the case when the discrepancy of parameter values between the clusters is comparable to the internal scatter of the parameter values within each cluster. An example of such a clustering algorithm is Chameleon [18]. Another approach to the problem of identifying astronomical populations is unsupervised clustering: for example, the expectation maximization (EM) algorithm with mixture models to detect groups of interest, making descriptive summaries, and building density estimates for large data sets.…”

Merged or monolithic? Using machine-learning to reconstruct the dynamical history of simulated star clusters

“…ML techniques are by now ubiquitous in Astronomy, where they have been successfully applied to photometric redshift estimation in large surveys such as the Sloan Digital Sky Survey (Tagliaferri et al 2003;Li et al 2007;Ball et al 2007;Gerdes et al 2010;Singal et al 2011;Geach 2012;Carrasco Kind & Brunner 2013;Cavuoti et al 2014;Hoyle et al 2015a,b), automatic identification of quasi stellar objects (Yèche et al 2010), galaxy morphology classification (Banerji et al 2010;Shamir et al 2013;Kuminski et al 2014), detection of HI bubbles in the interstellar medium (Thilker et al 1998;Daigle et al 2003), classification of diffuse interstellar bands in the Milky Way (Baron et al 2015), prediction of solar flares (Colak & Qahwaji 2009;Yu et al 2009), automated classification of astronomical transients and detection of variability (Mahabal et al 2008;Djorgovski et al 2012;Brink et al 2013;du Buisson et al 2015;Wright et al 2015), cataloguing of impact craters on Mars (Stepinski et al 2009), prediction of galaxy halo occupancy in cosmological simulations (Xu et al 2013), dynamical mass measurement of galaxy clusters (Ntampaka et al 2015), and supernova identification in supernova searches (Bailey et al 2007). Software tools developed specifically for astronomy are also becoming available to the community, still mainly with large observational datasets in mind (VanderPlas et al 2012;Vander Plas et al 2014;VanderPlas et al 2014;Ball & Gray 2014).…”