It has been a long week. We had the nips deadline on Friday. Fortunately, we manage to submit the papers. Let's keep the finger crossed! I was very fortunate to receive very constructive comments about my nips paper from Ross Fedely. David Hogg also gave last-minute comments which helped improve the paper further (and special thank goes to Bernhard for that). Here is a picture of us working toward the deadline:
After submitting the papers, we hanged out with many people, including Rob Fergus, in the park to celebrate our submissions.
Right. The main topic of this post is about supernova classification. Early this week, Bernhard and I had a quick meeting with astronomy people from CCPP (Center for Cosmology and Particle Physics). They are working on the problem of supernova classification (identifying the type of supernova from their spectra), and are interested in applying machine learning techniques to solve this problem. Briefly, the main challenge of this problem is the fact that the supernova itself change over time. That is, it can belong to different type depending on when it is observed. . Another challenge of this problem is that we have a small dataset, usually in the order of hundred.
According to wikipedia, a supernova is an energetic explosion of a star. The explosion can be triggered either by the reignition of nuclear fusion in a degenerate star or by the collapse of the core of a massive star, Either way, a massive amount of energy is generated. Interestingly, the expanding shock waves of supernova explosions can trigger the formation of new stars.
Supernovae are important in cosmology because maximum intensities of their explosions could be used as "standard candles". Briefly, it helps astronomers indicate the astronomical distances.
One of the previous works used the correlation between the objects' spectra and set of templates to identify their type. I will read the paper on the weekend and see if we can build something better than just simple correlation.