Context.A new class of super-luminous transients has recently been identified. These objects reach absolute luminosities of M u < −21, lack hydrogen in their spectra, and are exclusively discovered by non-targeted surveys because they are associated with very faint galaxies. Aims. We aim to contribute to a better understanding of these objects by studying SN 2006oz, a newly-recognized member of this class. Methods. We present multi-color light curves of SN 2006oz from the SDSS-II SN Survey that cover its rise time, as well as an optical spectrum that shows that the explosion occurred at z ∼ 0.376. We fitted black-body functions to estimate the temperature and radius evolution of the photosphere and used the parametrized code SYNOW to model the spectrum. We constructed a bolometric light curve and compared it with explosion models. In addition, we conducted a deep search for the host galaxy with the 10 m GTC telescope. Results. The very early light curves show a dip in the g-and r-bands and a possible initial cooling phase in the u-band before rising to maximum light. The bolometric light curve shows a precursor plateau with a duration of 6-10 days in the rest-frame. A lower limit of M u < −21.5 can be placed on the absolute peak luminosity of the SN, while the rise time is constrained to be at least 29 days. During our observations, the emitting sphere doubled its radius to ∼2 × 10 15 cm, while the temperature remained hot at ∼15 000 K. As for other similar SNe, the spectrum is best modeled with elements including O ii and Mg ii, while we tentatively suggest that Fe iii might be present. The host galaxy is detected in gri with 25.74 ± 0.19, 24.43 ± 0.06, and 24.14 ± 0.12, respectively. It is a faint dwarf galaxy with M g = −16.9. Conclusions. We suggest that the precursor plateau might be related to a recombination wave in a circumstellar medium (CSM) and discuss whether this is a common property of all similar explosions. The subsequent rise can be equally well described by input from a magnetar or by ejecta-CSM interaction, but the models are not well constrained owing to the lack of post-maximum observations, and CSM interaction has difficulties accounting for the precursor plateau self-consistently. Radioactive decay is less likely to be the mechanism that powers the luminosity. The host is a moderately young and star-forming, but not a starburst, galaxy.

This paper describes the data release of the Sloan Digital Sky Survey-II (SDSS-II) Supernova Survey conducted between 2005 and 2007. Light curves, spectra, classifications, and ancillary data are presented for 10,258 variable and transient sources discovered through repeat ugriz imaging of SDSS Stripe 82, a 300 deg 2 area along the celestial equator. This data release is comprised of all transient sources brighter than r ≃ 22.5 mag with no history of variability prior to 2004. Dedicated spectroscopic observations were performed on a subset of 889 transients, as well as spectra for thousands of transient host galaxies using the SDSS-III BOSS spectrographs. Photometric classifications are provided for the candidates with good multi-color light curves that were not observed spectroscopically. From these observations, 4607 transients are either spectroscopically confirmed, or likely to be, supernovae, making this the largest sample of supernova candidates ever compiled. We present a new method for SN host-galaxy identification and derive host-galaxy properties including stellar masses, star-formation rates, and the average stellar population ages from our SDSS multi-band photometry. We derive SALT2 distance moduli for a total of 1443 SN Ia with spectroscopic redshifts as well as photometric redshifts for a further 677 purely-photometric SN Ia candidates. Using the spectroscopically confirmed subset of the three-year SDSS-II SN Ia sample and assuming a flat ΛCDM cosmology, we determine Ω M = 0.315 ± 0.093 (statistical error only) and detect a non-zero cosmological constant at 5.7 σ. −0.47 2.0 60.31 · · · a This table is a portion of the full SN catalog, which is published in its entirety as Table 1 in the electronic edition of The Astrophysical Journal.Selected columns relating to host galaxy properties are shown here for guidance regarding the form and content of these columns. b In the electronic edition log(M) is called logMassFSPS and the upper limit is logMasshiFSPS and the lower limit is logMassloFSPS. log(sSF R) is called logSSFRFSPS and the upper and lower limits are logSSFRhiFSPS and logSSFRloFSPS, respectively, and age is called ageFSPS with upper and lower limits agehiFSPS and ageloFSPS. c The reduced χ 2 value of the fit. This column is called minredchi2FSPS in the electronic edition. d In the electronic edition log(M) is called logMassPEGASE and the upper limit is logMasshiPEGASE and the lower limit is logMassloPEGASE. log(SF R)is called logSFRPEGASE and the upper and lower limits are logSFRhiPEGASE and logSFRloPEGASE, respectively, and age is called agePEGASE. e The χ 2 value of the fit. This column is called minchi2PEGASE in the electronic edition.

We describe an algorithm for identifying point-source transients and moving objects on reference-subtracted optical images containing artifacts of processing and instrumentation. The algorithm makes use of the supervised machine learning technique known as Random Forest. We present results from its use in the Dark Energy Survey Supernova program (DES-SN), where it was trained using a sample of 898,963 signal and background events generated by the transient detection pipeline. After reprocessing the data collected during the first DES-SN observing season (Sep. 2013 through Feb. 2014) using the algorithm, the number of transient candidates eligible for human scanning decreased by a factor of 13.4, while only 1.0 percent of the artificial Type Ia supernovae (SNe) injected into search images to monitor survey efficiency were lost, most of which were very faint events. Here we characterize the algorithm's performance in detail, and we discuss how it can inform pipeline design decisions for future time-domain imaging surveys, such as the Large Synoptic Survey Telescope and the Zwicky Transient Facility. An implementation of the algorithm and the training data used in this paper are available at http://portal.nersc.gov/project/dessn/autoscan.

Host galaxy identification is a crucial step for modern supernova (SN) surveys such as the Dark Energy Survey and the Large Synoptic Survey Telescope, which will discover SNe by the thousands. Spectroscopic resources are limited, and so in the absence of real-time SN spectra these surveys must rely on host galaxy spectra to obtain accurate redshifts for the Hubble diagram and to improve photometric classification of SNe. In addition, SN luminosities are known to correlate with host-galaxy properties. Therefore, reliable identification of host galaxies is essential for cosmology and SN science. We simulate SN events and their locations within their host galaxies to develop and test methods for matching SNe to their hosts. We use both real and simulated galaxy catalog data from the Advanced Camera for Surveys General Catalog and MICECATv2.0, respectively. We also incorporate "hostless" SNe residing in undetected faint hosts into our analysis, with an assumed hostless rate of 5%. Our fully automated algorithm is run on catalog data and matches SNe to their hosts with 91% accuracy. We find that including a machine learning component, run after the initial matching algorithm, improves the accuracy (purity) of The Astronomical Journal, 152:154 (20pp), 2016 December doi:10.3847/0004-6256/152/6/154 © 2016. The American Astronomical Society. All rights reserved.1 the matching to 97% with a 2% cost in efficiency (true positive rate). Although the exact results are dependent on the details of the survey and the galaxy catalogs used, the method of identifying host galaxies we outline here can be applied to any transient survey.

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