Software for Analyzing Supernova Light Curve Data for Cosmology

Kyle Barbary

My name is Kyle Barbary and I am currently a postdoc in the physics department and a Data Science Fellow in the Institute for Data Science at the University of California, Berkeley. I am an observational cosmologist. More specifically, I use a particular variety of exploding stars, known as Type Ia supernovae, as markers to measure how the universe has expanded over its history. To make this measurement as precisely as possible, it is necessary to combine supernova data from many different surveys targeting different distances. The workflow I describe is about the creation of software tools used to combine and analyze that data in a uniform way.


Diagram I will describe the development of software for analyzing supernova light curve data. A "light curve" in the parlance of my domain is simply the brightness of a supernova as a function of time. These brightness measurements are derived from images of the same patch of sky spaced in time, ideally showing the supernova growing brighter and then fainter. Analyzing these light curves is a key step in deriving final results for most supernova cosmology studies. The software in question was originally developed for analyzing data from the Dark Energy Survey, but it can be (and has been) used for analyzing data from other surveys, as I will discuss below.

The analysis starts from reduced light curve data produced by a separate pipeline (not discussed here). A Python script reads the data, performs analysis tasks such as model fitting or parameter sampling, and saves the results or produces plots allowing the user to visualize the results. There are generally multiple scripts for performing different analyses or variations on an analysis, and these can be written by several different scientists on the project. The key aspect of the process is that all commonly useful functionality is split out into a Python library (SNCosmo). The top-level analysis scripts contain logic specific to the analysis and to the survey, and the SNCosmo library contains functionality applicable to a variety of surveys and analyses.

The development of the SNCosmo library itself is an iterative process where features of the library are added or refined in response to the needs of various analyses or users. Although there are official release versions of the library, several users stay up-to-date with the development version to keep this feedback loop tighter.

We use git for version control of the library and GitHub to coordinate development, where work is centered around an "SNCosmo" GitHub organization. Users who follow the development version periodically pull changes from the copy of the repository owned by the "SNCosmo" organization. We use two services in conjunction with GitHub. First, continuous integration is done with Travis: every time a change is made to the GitHub repository, this service is triggered. It builds the library and runs the full suite of unit tests for multiple combinations of supported library versions. This allows the developers to catch and fix problems before they are reported by users. Second, automated documentation builds are done by Read the Docs. This service builds the library and runs the documentation builder which produces a set of HTML pages (and also a PDF with the same content). This allows users to see the documentation for the latest development version immediately if needed. These two services are free for open-source projects and are widely used.

Within the repository, we use a number of standard tools: there is a script which can be used to build the library via build or to run the tests using test. The py.test package is used internally to run the tests.

Finally, at some point we make an official release version of the library. This is typically done after features have been user-tested for some time and the API is stable enough to be supported in future release versions. This is often a difficult judgement call.

Pain points

  • Feature stability: There is a trade-off between adding some feature immediately versus waiting until it is obvious whether to include it and what the specific interface should be. In the past I've marked such features as "experimental" with a warning in the documentation that users might have to change their code in the next library release version.

  • Multiple platforms: I develop on Linux but most users are on Mac OS X day-to-day. This hasn't been a huge problem yet, but it has produced a few headaches. Automated build services are starting to support OS X for free, so this will help.

Key benefits

The separation of common software functionality into a library is surprisingly unique in this subfield of supernova cosmology. It is a boon for reproducibility: published results can include the (relatively short) analysis scripts that were used, along with the version of the SNCosmo library used. The fact that the core software is a well-documented library means that readers and practitioners can more easily understand the specifics of the algorithms used.


What does "reproducibility" mean to you?

To me, reproducibility has two facets: the availability of usable software (preferably under an open-source license), and the availability of data (preferably in both raw and reduced forms). Together, these should give an outsider the ability to reproduce the results of a study from start to finish.

I separate these two aspects because each can be beneficial without the other. For example, even without releasing data, it can still be quite beneficial to release software. If released under an open-source licence, this provides a different flavor of reproducibility - the ability to reproduce an algorithm described in a paper and use and improve that algorithm in subsequent work.

As a side note, in my domain we often settle for a weaker form of full reproducibility, where a "reduced" data product and the software to analyze it is released, but not the raw data and not the software to go from raw to reduced data.

Why do you think that reproducibility in your domain is important?

Efficiency. Reproducibility makes cosmology research more efficient in the following ways:

  • Reuse of code. Cosmologists are as guilty as any of reinventing the wheel, particularly when the blueprints for the wheel are not made available.

  • Better understanding of algorithms spreads more rapidly. Algorithms are often explained coarsely in papers but without the detail necessary to reimplement them. Allowing the reader to directly read the code (if desired) solves this problem.

  • Fewer unexplained conflicting results. Research is often held up or lead down the wrong track by conflicting results from multiple groups. Allowing different groups to reproduce each other's results will help resolve such situations more quickly.

How or where did you learn about reproducibility?

Mainly through working on the AstroPy project, which develops a community astronomy Python package. I got involved in AstroPy when it was started in 2011. Like many other large open-source projects, AstroPy is developed on GitHub and follows typical best practices such as extensive unit testing, automated documentation builds and continuous integration on multiple platforms. In short, I learned these practices by interacting with more experienced programmers also working on the project.

What do you see as the major challenges to doing reproducible research in your domain, and do you have any suggestions?

In astronomy, like other fields, observers have a desire to carefully guard their hard-won data until they have eeked out every possible analysis. I'm sympathetic to this; acquiring the data often requires designing, building and deploying a new instrument or even an entire telescope. It can be a very large fraction of the work that goes into a project. The threat that someone else will download your data and use it to publish a result that you could have published is very real.

I'm less sympathetic about the reluctance to release software. Some of the reasons that I've experienced:

  • perceived lack of quality

  • perceived extra work to clean it up, maintain and support it

  • perceived competitive advantage or that the software is an asset or bargaining chip

Even for those who do wish to release their software under an open-source license, it is often difficult to do so in a fully legal manner through "official" channels due to university or lab copyright. Often, scientists just release the software without official permission.

Finally, one technical issue with releasing data is data volume. Raw imaging data from an entire survey can be many terabytes. Making this data publicly available often requires dedicated servers and support staff.

What do you view as the major incentives for doing reproducible research?

  • Long term project efficiency: Projects are often carried out over multiple generations of grad students and postdocs. Doing things reproducibly within a collaboration makes the transition between generations much less lossy.

  • Ability to back up claims: It often happens that two competing research groups make the same measurement and find results that differ by a marginally significant amount. The differences can often be due to specific statistical choices that were made in the analysis. In such disputes, having reproducible research means that you can invite the competing group to inspect your analysis in detail (and hopefully be proven right!).

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