Towards a Cosmological Hubble Diagram for Type II-P Supernovae

We present the first high-redshift Hubble diagram for Type II-P supernovae (SNe II-P) based upon five events at redshift up to z~0.3. This diagram was constructed using photometry from the Canada-France-Hawaii Telescope Supernova Legacy Survey and absorption line spectroscopy from the Keck observatory. The method used to measure distances to these supernovae is based on recent work by Hamuy & Pinto (2002) and exploits a correlation between the absolute brightness of SNe II-P and the expansion velocities derived from the minimum of the Fe II 516.9 nm P-Cygni feature observed during the plateau phases. We present three refinements to this method which significantly improve the practicality of measuring the distances of SNe II-P at cosmologically interesting redshifts. These are an extinction correction measurement based on the V-I colors at day 50, a cross-correlation measurement for the expansion velocity and the ability to extrapolate such velocities accurately over almost the entire plateau phase. We apply this revised method to our dataset of high-redshift SNe II-P and find that the resulting Hubble diagram has a scatter of only 0.26 magnitudes, thus demonstrating the feasibility of measuring the expansion history, with present facilities, using a method independent of that based upon supernovae of Type Ia.Comment: 36 pages, 16 figures, accepted for publication in Ap

The type Ia supernova SNLS-03D3bb from a super-Chandrasekhar-mass white dwarf star

The acceleration of the expansion of the universe, and the need for Dark Energy, were inferred from the observations of Type Ia supernovae (SNe Ia). There is consensus that SNe Ia are thermonuclear explosions that destroy carbon-oxygen white dwarf stars that accrete matter from a companion star, although the nature of this companion remains uncertain. SNe Ia are thought to be reliable distance indicators because they have a standard amount of fuel and a uniform trigger -- they are predicted to explode when the mass of the white dwarf nears the Chandrasekhar mass -- 1.4 solar masses. Here we show that the high redshift supernova SNLS-03D3bb has an exceptionally high luminosity and low kinetic energy that both imply a super-Chandrasekhar mass progenitor. Super-Chandrasekhar mass SNe Ia should preferentially occur in a young stellar population, so this may provide an explanation for the observed trend that overluminous SNe Ia only occur in young environments. Since this supernova does not obey the relations that allow them to be calibrated as standard candles, and since no counterparts have been found at low redshift, future cosmology studies will have to consider contamination from such events.Comment: 9 pages, 4 figures. To appear in Nature Sept. 21. Accompanying News & Views in same issue. Supplementary information available at www.nature.com/natur

Rubin-Euclid Derived Data Products:Initial Recommendations

This report is the result of a joint discussion between the Rubin and Euclid scientific communities. The work presented in this report was focused on designing and recommending an initial set of Derived Data products (DDPs) that could realize the science goals enabled by joint processing. All interested Rubin and Euclid data rights holders were invited to contribute via an online discussion forum and a series of virtual meetings. Strong interest in enhancing science with joint DDPs emerged from across a wide range of astrophysical domains: Solar System, the Galaxy, the Local Volume, from the nearby to the primaeval Universe, and cosmology

Rapidly Rising Transients in the Supernova - Superluminous Supernova Gap

The American Astronomical Society. All rights reserved..We present observations of four rapidly rising (trise ≈ 10 days) transients with peak luminosities between those of supernovae (SNe) and superluminous SNe (Mpeak ap; -20) - one discovered and followed by the Palomar Transient Factory (PTF) and three by the Supernova Legacy Survey. The light curves resemble those of SN 2011kl, recently shown to be associated with an ultra-long-duration gamma-ray burst (GRB), though no GRB was seen to accompany our SNe. The rapid rise to a luminous peak places these events in a unique part of SN phase space, challenging standard SN emission mechanisms. Spectra of the PTF event formally classify it as an SN II due to broad Hα emission, but an unusual absorption feature, which can be interpreted as either high velocity Hα (though deeper than in previously known cases) or Si ii (as seen in SNe Ia), is also observed. We find that existing models of white dwarf detonations, CSM interaction, shock breakout in a wind (or steeper CSM), and magnetar spin down cannot readily explain the observations. We consider the possibility that a "Type 1.5 SN" scenario could be the origin of our events. More detailed models for these kinds of transients and more constraining observations of future such events should help to better determine their nature. © 2016

SNIa Detection Analysis Results from Real and Simulated Images Using Specialized Software

International audienceThe detection of transient events, Type Ia supernovae in particular, hasbecome an important research subject in today’s astronomy. We use as a base toolthe software suite for astronomical image processing called LSSTsp and adapt itto assemble a Type Ia supernova detection pipe. We study some straightforwardchanges of the overall pipeline by selecting better quality inputs to perform acoaddition of reference images, we analyze the different residual sources detected onthe difference images and, lastly, we build light curves by taking into account thefeatures of detected difference image analysis sources. Finally, we build a catalog ofsupernova candidates by using a random forest classification, and check the relevanceof these additions. We reduce the overall source detection density with our changeswhile finding between 82% and 85% of the present Type Ia supernovae

Galaxy Image Translation with Semi-supervised Noise-reconstructed Generative Adversarial Networks

International audienceImage-to-image translation with Deep Learning neural networks, particularly with Generative Adversarial Networks (GANs), is one of the most powerful methods for simulating astronomical images. However, current work is limited to utilizing paired images with supervised translation, and there has been rare discussion on reconstructing noise background that encodes instrumental and observational effects. These limitations might be harmful for subsequent scientific applications in astrophysics. Therefore, we aim to develop methods for using unpaired images and preserving noise characteristics in image translation. In this work, we propose a two-way image translation model using GANs that exploits both paired and unpaired images in a semi-supervised manner, and introduce a noise emulating module that is able to learn and reconstruct noise characterized by high-frequency features. By experimenting on multi-band galaxy images from the Sloan Digital Sky Survey (SDSS) and the Canada France Hawaii Telescope Legacy Survey (CFHT), we show that our method recovers global and local properties effectively and outperforms benchmark image translation models. To our best knowledge, this work is the first attempt to apply semi-supervised methods and noise reconstruction techniques in astrophysical studies

PELICAN: deeP architecturE for the LIght Curve ANalysis

International audienceWe developed a deeP architecturE for the LIght Curve ANalysis (PELICAN) for the characterization and the classification of light curves. It takes light curves as input, without any additional features. PELICAN can deal with the sparsity and the irregular sampling of light curves. It is designed to remove the problem of non-representativeness between the training and test databases coming from the limitations of the spectroscopic follow-up. We applied our methodology on different supernovae light curve databases. First, we evaluated PELICAN on the Supernova Photometric Classification Challenge for which we obtained the best performance ever achieved with a non-representative training database, by reaching an accuracy of 0.811. Then we tested PELICAN on simulated light curves of the LSST Deep Fields for which PELICAN is able to detect 87.4% of supernovae Ia with a precision higher than 98%, by considering a non-representative training database of 2k light curves. PELICAN can be trained on light curves of LSST Deep Fields to classify light curves of LSST main survey, that have a lower sampling rate and are more noisy. In this scenario, it reaches an accuracy of 96.5% with a training database of 2k light curves of the Deep Fields. It constitutes a pivotal result as type Ia supernovae candidates from the main survey might then be used to increase the statistics without additional spectroscopic follow-up. Finally we evaluated PELICAN on real data from the Sloan Digital Sky Survey. PELICAN reaches an accuracy of 86.8% with a training database composed of simulated data and a fraction of 10% of real data. The ability of PELICAN to deal with the different causes of non-representativeness between the training and test databases, and its robustness against survey properties and observational conditions, put it on the forefront of the light curves classification tools for the LSST era

ConvEntion: Classification des séries chronologiques d'images astronomiques à l'aide d'attention convolutive

International audienceL'utilisation de séries temporelles d'images astronomiques suscite un intérêt grandissant dans la communauté scientifique. Par ailleurs, avec la massification des données, il est nécessaire de proposer des solutions d'analyse automatique. Dans cet article, nous proposons une nouvelle approche basée sur l'apprentissage profond pour classer différents types d'objets célestes en utilisant les séquences d'images issues des télescopes. Nous appelons notre approche ConvEntion (abréviation de CONVolutional attENTION). Elle est basée sur l'utilisation conjointe de convolutions et de transformeurs. Ceci constitue une innovation dans le domaine du traitement des séries temporelles d'images. Sur un sousensemble de données issues de la base SDDS nous améliorons la précision de 7 % par rapport aux approches de l'état de l'art utilisant des séries temporelles d'images

ConvEntion: Classification des séries chronologiques d'images astronomiques à l'aide d'attention convolutive

International audienceL'utilisation de séries temporelles d'images astronomiques suscite un intérêt grandissant dans la communauté scientifique. Par ailleurs, avec la massification des données, il est nécessaire de proposer des solutions d'analyse automatique. Dans cet article, nous proposons une nouvelle approche basée sur l'apprentissage profond pour classer différents types d'objets célestes en utilisant les séquences d'images issues des télescopes. Nous appelons notre approche ConvEntion (abréviation de CONVolutional attENTION). Elle est basée sur l'utilisation conjointe de convolutions et de transformeurs. Ceci constitue une innovation dans le domaine du traitement des séries temporelles d'images. Sur un sousensemble de données issues de la base SDDS nous améliorons la précision de 7 % par rapport aux approches de l'état de l'art utilisant des séries temporelles d'images

ConvEntion: Classification des séries chronologiques d'images astronomiques à l'aide d'attention convolutive

International audienceL'utilisation de séries temporelles d'images astronomiques suscite un intérêt grandissant dans la communauté scientifique. Par ailleurs, avec la massification des données, il est nécessaire de proposer des solutions d'analyse automatique. Dans cet article, nous proposons une nouvelle approche basée sur l'apprentissage profond pour classer différents types d'objets célestes en utilisant les séquences d'images issues des télescopes. Nous appelons notre approche ConvEntion (abréviation de CONVolutional attENTION). Elle est basée sur l'utilisation conjointe de convolutions et de transformeurs. Ceci constitue une innovation dans le domaine du traitement des séries temporelles d'images. Sur un sousensemble de données issues de la base SDDS nous améliorons la précision de 7 % par rapport aux approches de l'état de l'art utilisant des séries temporelles d'images