Social Media from the SDSS Collaborating Meeting in Madrid

This week many of us are at the Instituto de Física Teórica IFT UAM-CSIC in Madrid, Spain for our 2015 collaboration meeting (jointly organized by the Instituto de Física Teórica IFT UAM-CSIC and the Instituto de Astrofísica de Canarias).

The meeting hashtag is #sdss15.

Our twitter account @sdssurveys will be run by spokesperson, Jennifer Johnson this week. We’ll also be tweeting from survey accounts @mangasurvey (Karen Masters and Anne-Marie Weijmans), @APOGEEsurvey (by Jennifer Sobeck this week) and @eBOSSurvey (Britt Lundgren and Shirley Ho).

Join the conversation and find out what’s going on with the SDSSurveys right now.

Discovering Supernova in SDSS Galaxy Spectra

The post below was contributed by Dr. Or Graur, an assistant research scientist at New York University and research associate at the American Museum of Natural History. He recently led a paper based on supernovae detected in SDSS galaxy spectra (published in the Monthly Notices of the Royal Astronomical Society; the full text is available at: http://adsabs.harvard.edu/abs/2015MNRAS.450..905G).

 paper_header

One of the great things about the SDSS is that it can be used in ways that its creators may never have envisioned. The SDSS collected ~800,000 galaxy spectra. As luck would have it, some of those galaxies happened to host supernovae, the explosions of stars, inside the area covered by the SDSS spectral fiber during the exposure time. These supernovae would then “contaminate” the galaxy spectra. In Graur & Maoz (2013), we developed a computer code that allowed us to identify such contaminated spectra and tweeze out the supernovae from the data. In Graur et al. (2015), we used this code to detect 91 Type Ia and 16 Type II supernovae.

 

GM13_method

A galaxy+supernova model (blue) fits the SDSS spectrum (grey) much better than a galaxy-only model (green). The residual spectrum (lower panel, grey), after subtracting the galaxy component, is best-fit by a Type Ia supernova template (red).

With these samples, we measured the explosion rates of Type Ia and Type II supernovae as a function of various galaxy properties: stellar mass, star-formation rate, and specific star-formation rate. All of these properties were previously measured by the SDSS MPA-JHU Galspec pipeline.

 

In 2011, the Lick Observatory Supernova Search published a curious finding: the rates of all supernovae, normalized by the stellar mass of their host galaxies, declined with increasing stellar mass (instead of being independent of it; Li et al. 2011b). We confirmed this correlation, showed that the rates were also correlated with other galaxy properties, and demostrated that all these correlations could be explained by two simple models.

 

Type Ia supernovae, which are thought to be the explosions of carbon-oxygen white dwarfs, follow a delay-time distribution. Unlike massive stars, which explode rather quickly after they are born (millions of years, typically), Type Ia supernovae take their time – some explode soon after their white dwarfs are formed, while others blow up billions of years later. We showed that this delay-time distribution (best described as a declining power law with an index of -1), coupled with galaxy downsizing (i.e., older galaxies tend to be more massive than younger ones), explained not only the correlation between the rates and the galaxies’ stellar masses, but also their correlations with other galaxy properties.

sim_mass_fit

Type Ia supernova rates as a function of galaxy stellar mass

sim_sSFR_fit

Type Ia supernova rates as a function of specific star formation rate.

Simulated rates, based on a model combining galaxy downsizing and the delay-time distribution, are shown as a grey curve on both the above plots. This model is fit to the rates as a function of mass and then re-binned and plotted on the specific star formation rate plot, without further fitting.

For Type II supernovae, which explode promptly after star formation, the correlations are easier to explain; they are simply dependent on the current star-formation rates of the galaxies: the more efficient the galaxy is at producing stars, the more efficient it will be at producing Type II supernovae.

All of the supernova spectra from Graur & Maoz (2013) and Graur et al. (2015) are publicly available from the Weizmann Interactive Supernova data REPository (http://wiserep.weizmann.ac.il/). Please note that their continuua may be warped by our detection method (for details, see section 3 of Graur & Maoz 2013).

How SDSS Uses Light to Find Rocks in Space

It’s an exciting time in solar system exploration, with the Philae lander and Rosetta orbiter exploring Comet 67P Churyumov-Gerasimenko, sending back our most details view ever of a comet. On top of this the New Horizons Mission is approaching the dwarf planet Pluto, and will make its closest pass on 15th July 2015, sending back the highest resolution images of Pluto we have ever seen.

You might think that the Sloan Digital Sky Survey has nothing to say about rocks in space, but you’d be wrong. One of the possibly unexpected discoveries from our  imaging survey has been that of many hundreds of thousands of asteroids.

Because of the way the SDSS Camera Worked, asteroids show up in the SDSS imaging at different times (and therefore different places as they are moving across the sky) in the different filters. This makes them pop out as little strings of almost traffic light coloured dots. These have been popular finds by citizen scientists at Galaxy Zoo as well as identified by computer algorithms.

Asteroids (the three coloured dots) found near galaxies by citizen scientists at Galaxy Zoo.

Asteroids (the three coloured dots) found near galaxies by citizen scientists at Galaxy Zoo.

The below animation by Alex Parker shows the orbital motions of over 100,000 of the asteroids observed by the Sloan Digital Sky Survey (SDSS), with colors illustrating the compositional diversity measured by the SDSS five-color camera. The relative sizes of each asteroid are also illustrated.

 

All main-belt asteroids and Trojan asteroids with orbits known to high precision are shown. The animation has a timestep of 3 days. The fact that the composition of asteroids in the asteroid belt varys systematically is clearly visible, with green Vesta-family members in the inner belt fading through the blue C-class asteroids in the outer belt, and the deep red Trojan swarms beyond that.

Occasional diagonal slashes that appear in the animation are the SDSS survey beams.

The average orbital distances of Mercury, Venus, Earth, Mars, and Jupiter are illustrated with rings.

Colors represented with the same scheme as Parker et al. (2008). Concept and rendering by Alex H. Parker. Music: Tamxr by LJ Kruzer.


This post is part of the SDSS Celebration of the International Year of Light 2015, in which we aim to post an article a month about how SDSS uses light in our mission to study the Universe. We’ve reached the halfway point here in June 2015!