Project Description

The Nearby Supernova Factory (SNfactory) is an international astrophysics experiment designed to discover and measure Type Ia supernovae in greater number and detail than has ever been done before. These supernovae are stellar explosions that have a consistent maximum brightness, allowing them to be used as standard candles to measure distances to other galaxies and to trace the rate of expansion of the universe and how dark energy affects the structure of the cosmos. The SNfactory receives 50-80 GB of image data per night, which must be processed and examined by teams of domain experts within 12-24 hours to obtain maximum scientific benefit from the study of these rare and short-lived stellar events. It is the largest data volume supernova search currently in operation. To achieve the desired science goals, the SNfactory must collect both visual and spectroscopic data on each supernova found.

In order to analyze the spectral data, the scientists often need to compare spectra over different dimensions. In the past, when only a handful of supernovae were discovered each year, supernova scientists were able to recognize spectral signatures by supernova name. However, as the SNfactory has generated a large and growing supernova spectral database, exploring the dataset for analysis has become more difficult.

SpectraVis is an interactive visual interface for browsing and displaying supernova data. Implemented in Java using Piccolo, a toolkit providing support for 2D graphics and the development of zoomable user interfaces, the tool is designed for use by astrophysicists studying supernova spectra in order to learn more about dark energy.

Back to top

Prototype #1

The goal of this first prototype was to create a cluster visualization of spectra based on similarity scores. The first prototype was based on work done by Raquel Romano, who calculated similarity scores for a gold standard dataset of spectral data. We set various threshold cutoffs and were able to construct an adjacency matrix based on the similarity score data. The Prefuse toolkit was then used to visualize the data as clusters with a graph topology.

In the above screenshot, there are two selected spectra shown with all 'similar' spectra. Selecting another spectrum would change the graph to only show spectra 'similar' to the chosen spectrum. Double clicking on the spectrum node opens up a window with a detailed spectral image. Dynamic sliders could be used to make the similarity score cutoff more or less restrictive.

One limitation of this visualization was that the edges of the graph were unweighted. Ideally, we wanted distance in the graph to represent similarity. The force-directed graph layout algorithm in Prefuse does not allow for weighted edges, so if we decided to continue using Prefuse, we would have to extend the toolkit to do so. Another limitation is that the clusters are essentially unlabeled, making it difficult to interpret the clusters.

Back to top

Prototype #2

The next prototype was based on the understanding that Type Ia spectra of around the same phase are very similar to one another, and scientists are often interested in seeing spectra at the same phase. We felt that clustering by phase would result in a meaningful topography for this visualization. In this prototype, we also addressed the fact that, with over a thousand spectra, there would be a need to see an overview of the entire dataset as well as be able to focus in on a subset of the data. Again using Prefuse, we implemented semantic zoom as a way to explore the clustered data. As a section of the graph is zoomed in on, more detail about each spectrum appears. Below are two screenshots of the design, the first displaying an overview of a demo dataset, the second, zoomed in on a portion of the graph.

Zoomed out view of a demo dataset

Zoomed in, displaying more information on each spectrum

One problem with this design was that, while exploring the dataset through zooming, the user could lose context and become disoriented. As an implementation issue, we would have to develop a layout algorithm for the weighted cluster visualization.

Back to top

Final Prototype

The final design was inspired by an email from Saul Perlmutter, a co-Principal Investigator at the SNfactory, who requested timeseries plots of several SN Ia's for a talk. This email made it clear that scientists were interested both in seeing SN Ia's of similar phase as well as all the spectra of one SN.

Spectral timeseries plots

For our final design, instead of clustering by some criteria, we decided to display all the spectra on a 2D grid, SN (specifically, target name) by phase. In order to zoom in on areas of interest, we implemented a semantic fisheye zoom, which enlarges and provides more detail for a specific area of the graph while retaining context.

The Piccolo Toolkit

We decided to use the Piccolo toolkit instead of Prefuse for this prototype primarily because Piccolo had several already implemented fisheye applications that we could use as starting points for our application. In addition, we felt that Piccolo was better documented and simpler to start using.

Interface Description

The fisheye browser uses a grid layout to preserve the timeseries concept behind the study of SNe. Each dot of the grid corresponds to a supernova spectrum. The vertical axis contains the target names of the supernovae and on the horizontal has the age in days of the supernova or the phase. The period of scientific interest in the study of type Ia SNe ranges between 15 days before peak brightness and 40 days after peak brightness.

In order to see a more detailed spectrum, the user clicks on one of the spectrum nodes and the fisheye technique makes the clicked dot and its neighbors bigger. The user can click one more time on the spectrum of interest and the zooming is taken at a higher level.

The semantic zooming is taking place at four levels: the initial dots, the two small spectra of focus and neighbor size, and the enlarged graph:

The fisheye lens approach to the visual browser was combined with standard information visualization techniques such as highlighting and filtering. While the fisheye changes the display size relative to focus, the highlighting changes the display type relative to focus.

Rolling over the phase or target names, the corresponding column or row is highlighted in red. A similar highlight is activated when the user rolls over the grid.

Filtering is one of the features in progress. A double range slider was created to remove or select data for a certain phase interval. The slider can be operated by either adjusting its length or by entering values in the text fields and pressing return.

A similar double range slider will be implemented for the number of targets, allowing the user to add or remove as many supernovae as s/he wants.

Back to top

User Evaluation

We demoed SpectraVis to several key scientists to gather feedback for future efforts. One scientist commented that he had never been able to see the whole dataset of spectra before. While exploring the data, the scientists found several cases where the data looked "weird" and worthy of further study.

The scientists had several requests for the visual browser. They wanted to be able to sort the target name axis based on key features such as redshift. They also wanted to be able to color-code each spectra, based on whether the observation was successful, marginal, or a failure. Finally, they wanted to be able to see more points upon each zoom.

One scientist expressed the desire to select different features for each axis. He felt that the fisheye zoom over a 2D plot was highly useful and would like to be able to use this visualization to explore the dataset in different ways.

Back to top

Future Work

SpectraVis gives the SNfactory scientists a way to browse a large dataset of spectra. We hope that SpectraVis will facilitate scientific analysis. We would like to be able to add access to data analysis tools from the visual browser, feeding in the datafiles from selected SNe into those additional tools.

We are very interested in following up on the suggestion for selectable axis variables. We feel that, in doing so, the visual browser could be used by a variety of groups to aid in data exploration.

Back to top


Useful Links

The Prefuse Toolkit website
The Piccolo Toolkit website
The SNfactory website
The LBNL Visualization Group website
Elena Caraba's presentation on SpectraVis

Back to top