We present optical light curves, redshifts, and classifications for 365 spectroscopically confirmed Type Ia supernovae (SNe Ia) discovered by the Pan-STARRS1 (PS1) Medium Deep Survey. We detail improvements to the PS1 SN photometry, astrometry and calibration that reduce the systematic uncertainties in the PS1 SN Ia distances. We combine the subset of 279 PS1 SN Ia (0.03 < z < 0.68) with useful distance estimates of SN Ia from SDSS, SNLS, various low-z and HST samples to form the largest combined sample of SN Ia consisting of a total of 1048 SN Ia ranging from 0.01 < z < 2.3, which we call the 'Pantheon Sample'. When combining Planck 2015 CMB measurements with the Pantheon SN sample, we find Ω m = 0.307±0.012 and w = −1.026±0.041 for the wCDM model. When the SN and CMB constraints are combined with constraints from BAO and local H 0 measurements, the analysis yields the most precise measurement of dark energy to date: w 0 = −1.007 ± 0.089 and w a = −0.222 ± 0.407 for the w 0 w a CDM model. Tension with a cosmological constant previously seen in an analysis of PS1 and low-z SNe has diminished after an increase of 2× in the statistics of the PS1 sample, improved calibration and photometry, and stricter light-curve quality cuts. We find the systematic uncertainties in our measurements of dark energy are almost as large as the statistical uncertainties, primarily due to limitations of modeling the low-redshift sample. This must be addressed for future progress in using SN Ia to measure dark energy.

Context. The Public European Southern Observatory Spectroscopic Survey of Transient Objects (PESSTO) began as a public spectroscopic survey in April 2012. PESSTO classifies transients from publicly available sources and wide-field surveys, and selects science targets for detailed spectroscopic and photometric follow-up. PESSTO runs for nine months of the year, January -April and August -December inclusive, and typically has allocations of 10 nights per month. Aims. We describe the data reduction strategy and data products that are publicly available through the ESO archive as the Spectroscopic Survey data release 1 (SSDR1). Methods. PESSTO uses the New Technology Telescope with the instruments EFOSC2 and SOFI to provide optical and NIR spectroscopy and imaging. We target supernovae and optical transients brighter than 20.5 m for classification. Science targets are selected for follow-up based on the PESSTO science goal of extending knowledge of the extremes of the supernova population. We use standard EFOSC2 set-ups providing spectra with resolutions of 13-18 Å between 3345−9995 Å. A subset of the brighter science targets are selected for SOFI spectroscopy with the blue and red grisms (0.935−2.53 μm and resolutions 23−33 Å) and imaging with broadband JHK s filters.Results. This first data release (SSDR1) contains flux calibrated spectra from the first year (April 2012(April -2013. A total of 221 confirmed supernovae were classified, and we released calibrated optical spectra and classifications publicly within 24 h of the data being taken (via WISeREP). The data in SSDR1 replace those released spectra. They have more reliable and quantifiable flux calibrations, correction for telluric absorption, and are made available in standard ESO Phase 3 formats. We estimate the absolute accuracy of the flux calibrations for EFOSC2 across the whole survey in SSDR1 to be typically ∼15%, although a number of spectra will have less reliable absolute flux calibration because of weather and slit losses. Acquisition images for each spectrum are available which, in principle, can allow the user to refine the absolute flux calibration. The standard NIR reduction process does not produce high accuracy absolute spectrophotometry but synthetic photometry with accompanying JHK s imaging can improve this. Whenever possible, reduced SOFI images are provided to allow this. Conclusions. Future data releases will focus on improving the automated flux calibration of the data products. The rapid turnaround between discovery and classification and access to reliable pipeline processed data products has allowed early science papers in the first few months of the survey.

The full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. AbstractWe use 1169 Pan-STARRS supernovae (SNe) and 195 low-z (z < 0.1) SNe Ia to measure cosmological parameters. Though most Pan-STARRS SNe lack spectroscopic classifications, in a previous paper we demonstrated that photometrically classified SNe can be used to infer unbiased cosmological parameters by using a Bayesian methodology that marginalizes over core-collapse (CC) SN contamination. Our sample contains nearly twice as many SNe as the largest previous SN Ia compilation. Combining SNe with cosmic microwave background (CMB) constraints from Planck, we measure the dark energy equation-of-state parameter w to be −0.989±0.057 (stat +sys). If w evolves with redshift as w(a)=w 0 +w a (1−a), we find w 0 =−0.912±0.149 and w a = −0.513±0.826. These results are consistent with cosmological parameters from the Joint Light-curve Analysis and the Pantheon sample. We try four different photometric classification priors for Pan-STARRS SNe and two alternate ways of modeling CC SN contamination, finding that no variant gives a w differing by more than 2% from the baseline measurement. The systematic uncertainty on w due to marginalizing over CC SN contamination, s = 0.012 w CC , is the third-smallest source of systematic uncertainty in this work. We find limited (1.6σ) evidence for evolution of the SN color-luminosity relation with redshift, a possible systematic that could constitute a significant uncertainty in future high-z analyses. Our data provide one of the best current constraints on w, demonstrating that samples with ∼5% CC SN contamination can give competitive cosmological constraints when the contaminating distribution is marginalized over in a Bayesian framework.

We present two hydrogen-rich superluminous supernovae (SLSNe), namely SN2013hx and PS15br. These objects, together with SN2008es are the only SLSNe showing a distinct, broad Hα feature during the photospheric phase and also do not show any sign of strong interaction between fast moving ejecta and circumstellar shells in their early spectra. Despite PS15br peak luminosity is fainter than the other two objects, the spectrophotometric evolution is similar to SN2013hx and different than any other supernova in a similar luminosity space. We group all of them as SLSNe II and hence distinct from the known class of SLSN IIn. Both transients show a strong, multicomponent Hα emission after 200 days past maximum which we interpret as an indication of interaction of the ejecta with an asymmetric, clumpy circumstellar material. The spectra and photometric evolution of the two objects are similar to type II supernovae, although they have much higher luminosity and evolve on slower timescales. This is qualitatively similar to how SLSNe I compare with normal type Ic in that the former are brighter and evolve more slowly. We apply a magnetar and an interaction semi-analytical codes to fit the light curves of our two objects and SN2008es. The overall observational dataset would tend to favour the magnetar, or central engine, model as the source of the peak luminosity although the clear signature of late-time interaction indicates that interaction can play a role in the luminosity evolution of SLSNe II at some phases.