Cosmologists have been waiting a long time for the release of the Planck satellite’s cosmic microwave background data. The wait is over:
The Planck data has finer resolution (i.e., can see sharper details) than the previous all-sky WMAP data, as illustrated in this picture (which, like the previous one, is from the European Space Agency, which runs Planck):
There are other important differences. Both data sets are made from maps at multiple different frequencies, but Planck’s frequencies cover a wider range. This is nice, because it makes it possible to separate out the different constituents of the map more precisely. To be specific, the raw data include both the cosmic microwave background radiation and other, more local “foregrounds,” such as emission from stuff in our own Galaxy. The pictures above show just the CMB component.
Along with the data, the Planck collaboration released 29 papers‘ worth of analysis, which have been submitted for publication but have not yet undergone peer review. Obviously, people will be digesting all this information, and using the data for their own studies, for a long time.
As far as I can tell, there are no huge surprises. The most important scientific product you get from a data set like this is the power spectrum, which looks like this:
The red points are the measurements, which match the theoretical prediction very well. You can estimate various parameters from this fit, including the amount of dark matter (somewhat bigger than previously thought) and the expansion rate of the Universe (a bit slower than previously thought).
The value of the parameter known as the spectral index n is of interest to specialists, although not to anyone else: it’s about 0.96 plus or minus 0.01. The main point is that it’s significantly different from 1. Here’s what that means: if you take the variations in density in the early Universe, and model them as a sum of sine waves of different wavelengths, the long-wavelength waves are slightly more important than the short-wavelength waves. n=1 would have corresponded to all wavelengths being equally important. If you’re not in the business, you probably still don’t care, and that’s OK, but it’s interesting to some people (basically because some theoretical models predict this and others don’t).
Finally, there’s the fact that the so-called “large-scale anomalies” in the maps didn’t go away. These are patterns or features that showed up in the earlier WMAP data but were not expected by our best theories:
- The level of fluctuations on very large angular scales is smaller than expected.
- One half of the sky has slightly smaller temperature variations than the other.
- The largest-scale multipoles (i.e., the longest-wavelength waves you get if you model the data as a combination of ripples of different wavelengths) tend to line up in a certain direction, whereas our theories say the directions should be random.
People argue over how much (if any) interest we should take in the anomalies. Before Planck, you could reasonably adopt any of the following stances:
- The anomalies are a result of some sort of problem in the data processing.
- The anomalies are due to some sort of foreground contaminant (i.e., local stuff, not stuff from the early Universe).
- The anomalies are present in the CMB, but they’re just chance fluctuations without any greater significance.
- The anomalies are telling us something new and important about the Universe.
At a quick glance, the Planck data make it much harder to believe the first two. The differences between the two data sets are such that you wouldn’t expect the anomalies to look similar if they were processing artifacts or foregrounds, but they do look similar. Both 3 and 4 are still viable.
It’s hard to decide between 3 and 4. You might think that you could assess the validity of #3 by the usual methods of calculating statistical significances. The problem is that the anomalies were noticed in the data first, and their level of improbability was only calculated after the fact. That means that you can’t calculate statistical significances in a meaningful way. In any large data set, there are so many things you could choose to look at that you’re bound to find some that are unlikely. Maybe that’s all the CMB anomalies are. (Here’s a perhaps slightly labored analogy with the Super Bowl coin toss.)
Personally, I don’t know whether the CMB anomalies are telling us anything, but I’m glad they’re still around. I have a bit of a soft spot for them.