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All posts for the month September, 2014

I really enjoyed a talk from Dr. Josh Bandfield yesterday in the Geosciences Department about unusual geomorphological features on the surface of the Moon.

Bandfield works with the Diviner instrument onboard the Lunar Reconnaissance Orbiter. Diviner is a very sensitive camera that measures infrared radiation from the Moon’s surface over a wide range of wavelengths. Using these data, Bandfield and others can learn a surprising amount about the Moon’s surface and geological history.

Rock concentration values near Rima Bode (356.1°E, 12.9°N) superposed on a high-res LROC image. The white box denotes the area shown in the bottom image. (bottom) A portion of LROC image. Each square in the image covers a separate Diviner bin with the derived rock concentration value listed.

Rock concentration values near Rima Bode (356.1°E, 12.9°N) superposed on a high-res LROC image. The white box denotes the area shown in the bottom image. (bottom) A portion of LROC image. Each square in the image covers a separate Diviner bin with the derived rock concentration value listed.

For example, the rates at which different surface types cool once the Sun sets depend on the surfaces’ thermal conductivity. In general, lunar regolith or soil heats up and cools down much more quickly than bare rock. And so, if Diviner measures a surface’s temperature at a known time of lunar day, Bandfield can determine how rocky the surface is. The figure at left from Bandfield et al. (2011) compares the inferred rockiness of one surface to high-resolution images, which confirm the rockiness map from Diviner data.

Bandfield also discussed how rockiness maps can be used to determine the relative ages of lunar craters. Since the Moon is constantly bombarded by impactors, lunar rocks are continually broken down into finer and finer pieces, finally turning into powdery regolith. By mapping the rockiness of different craters, Bandfield and colleagues found the youngest craters are also the most rocky.

A theme that Bandfield highlighted several times: even though humans have studied the Moon for millenia, it still has fascinating things to reveal.

Comparison between raw K2 and corrected photometry. Figure 5 for Vanderburg & Johnson (2014).

Comparison between raw K2 and corrected photometry. Figure 5 for Vanderburg & Johnson (2014).

Read a neat paper from Andrew Vanderburg of John Johnson’s Exoplanets group at Harvard about working with data from the upcoming K2 mission.

Having suffered failures of two of its reaction wheels, required to accurately point the telescope, the Kepler mission has ended its nominal science investigation. However, clever engineering will allow the spacecraft to keep operating and doing exciting astronomy as the K2 mission.

Among other goals, the K2 mission will continue to look for transiting exoplanets, which involves looking for the shadows of planets as the occult their host stars. However, small attitude tweaks needed to keep the K2 spacecraft accurately pointed result in fairly large artificial variations in the measured brightnesses of target stars. Removing the effects of these tweaks from K2’s data can be quite challenging, but Vanderburg & Johnson’s recent paper describes one technique for doing that.

By carefully tracking the centers of target stars as they drift across the K2 CCD camera, their technique allows them to remove quite large and complex artificial variability and to recover the actual brightness variations of target stars.

From the paper, the figure at left shows how well they do: the blue dots at the top show the raw measurements, with the artificial variations from attitude tweaks apparent as discontinuous jumps. By carefully modeling the exact position of the target star and removing the effects of its motion across the CCD, the technique produces a much more accurate measurement of the actual brightness variations of the target star, orange dots at bottom.

Applying these techniques to many target stars monitored during one of the K2 engineering tests, Vanderburg & Johnson showed they can produce data nearly as precise as the original Kepler mission — almost as good as sending an astronaut repair team to fix the Kepler spacecraft, but all it took was some sophisticated numerical modeling and a laptop.