Using R-based VOStat as a Low-Resolution Spectrum Analysis Tool

We describe here an online software suite VOStat written mainly for the Virtual Observatory, a novel structure in which astronomers share terabyte scale data. Written mostly in the public-domain statistical computing language and environment R, it can do a variety of statistical analysis on multidimensional, multi-epoch data with errors. Included are techniques which allow astronomers to start with multi-color data in the form of low-resolution spectra and select special kinds of sources in a variety of ways including color outliers. Here we describe the tool and demonstrate it with an example from Palomar-QUEST, a synoptic sky survey.

Using R-based VOStat as a low resolution spectrum analysis tool

We describe here an online software suite VOStat written mainly for the Virtual Observatory, a novel structure in which astronomers share terabyte scale data. Written mostly in the public-domain statistical computing language and environment R, it can do a variety of statistical analysis on multidimensional, multi-epoch data with errors. Included are techniques which allow astronomers to start with multi-color data in the form of low-resolution spectra and select special kinds of sources in a variety of ways including color outliers. Here we describe the tool and demonstrate it with an example from Palomar-QUEST, a synoptic sky survey

Novel Measures for Rare Transients

Data volumes in astronomy have been growing rapidly. Various projects and methodologies are starting to deal with this. As we cross-match and correlate datasets, the number of parameters per object—in other words dimensions we need to deal with— is also growing. This leads to more interesting issues as many values are missing, and many parameters are non-homogeneously redundant. One needs to tease apart clusters in this space which represent different physical properties, and hence phenomena. We describe measures that help to do that for transients from the Catalina Realtime Transient Survey, and project it to near future surveys. The measures are based partly on domain knowledge and are incorporated into statistical and machine learning techniques. We also describe the discriminating role of appropriate followup observations in near-real-time classification of transients. In particular such novel measures will help us find relatively rare transients

Effective image differencing with convolutional neural networks for real-time transient hunting

Large sky surveys are increasingly relying on image subtraction pipelines for real-time (and archival) transient detection. In this process one has to contend with varying point-spread function (PSF) and small brightness variations in many sources, as well as artefacts resulting from saturated stars and, in general, matching errors. Very often the differencing is done with a reference image that is deeper than individual images and the attendant difference in noise characteristics can also lead to artefacts. We present here a deep-learning approach to transient detection that encapsulates all the steps of a traditional image-subtraction pipeline – image registration, background subtraction, noise removal, PSF matching and subtraction – in a single real-time convolutional network. Once trained, the method works lightening-fast and, given that it performs multiple steps in one go, the time saved and false positives eliminated for multi-CCD surveys like Zwicky Transient Facility and Large Synoptic Survey Telescope will be immense, as millions of subtractions will be needed per night

Results from the Supernova Photometric Classification Challenge

We report results from the Supernova Photometric Classification Challenge (SNPhotCC), a publicly released mix of simulated supernovae (SNe), with types (Ia, Ibc, and II) selected in proportion to their expected rates. The simulation was realized in the griz filters of the Dark Energy Survey (DES) with realistic observing conditions (sky noise, point-spread function, and atmospheric transparency) based on years of recorded conditions at the DES site. Simulations of non–Ia-type SNe are based on spectroscopically confirmed light curves that include unpublished non-Ia samples donated from the Carnegie Supernova Project (CSP), the Supernova Legacy Survey (SNLS), and the Sloan Digital Sky Survey-II (SDSS-II). A spectroscopically confirmed subset was provided for training. We challenged scientists to run their classification algorithms and report a type and photo-z for each SN. Participants from 10 groups contributed 13 entries for the sample that included a host-galaxy photo-z for each SN and nine entries for the sample that had no redshift information. Several different classification strategies resulted in similar performance, and for all entries the performance was significantly better for the training subset than for the unconfirmed sample. For the spectroscopically unconfirmed subset, the entry with the highest average figure of merit for classifying SNe Ia has an efficiency of 0.96 and an SN Ia purity of 0.79. As a public resource for the future development of photometric SN classification and photo-z estimators, we have released updated simulations with improvements based on our experience from the SNPhotCC, added samples corresponding to the Large Synoptic Survey Telescope (LSST) and the SDSS-II, and provided the answer keys so that developers can evaluate their own analysis

Discovery of Two Spectroscopically Peculiar, Low-Luminosity Quasars at z~4

We report the discovery of two low-luminosity quasars at z~4, both of which show prominent N IV] 1486A emission. This line is extremely rare in quasar spectra at any redshift; detecting it in two out of a sample of 23 objects (i.e., ~ 9% of the sample) is intriguing and is likely due to the low-luminosity, high-redshift quasar sample we are studying. This is still a poorly explored regime, where contributions from associated, early starbursts may be significant. One interpretation of this line posits photoionization by very massive young stars. Seeing N IV] 1486A emission in a high-redshift quasar may thus be understood in the context of co-formation and early co-evolution of galaxies and their supermassive black holes. Alternatively, we may be seeing a phenomenon related to the early evolution of quasar broad emission line regions. The non-detection (and possibly even broad absorption) of N V 1240A line in the spectrum of one of these quasars may support that interpretation. These two objects may signal a new faint quasar population or an early AGN evolutionary stage at high redshifts.Comment: 15 pages, 5 figures, Accepted for publicated in ApJ Letter

Effective Radii and Color Gradients in Radio Galaxies

We present de Vaucouleurs' effective radii in B and R bands for a sample of Molonglo Reference Catalogue radio galaxies and a control sample of normal galaxies. We use the ratio of the scale lengths in the two bands as an indicator to show that the radio galaxies tend to have excess of blue color in their inner region much more frequently than the control galaxies. We show that the scale length ratio is a useful indicator of radial color variation even when the conventional color gradient is too noisy to serve the purpose.Comment: 11 pages, 4 figures, (LaTeX: aaspp4, epsfig), to appear in ApJL 199