Tag Archives: data mining

a new target list for ESPRI

The ESPRI team that I am part of at MPIA aspires to detect exoplanets based on the orbital recoil motion of their host stars. These are the “wobbles” like the ones our own Sun traces out due to the gravitational tug from the planets in the solar system. There are severe engineering obstacles to making this work: the extremely small angular scale of the star’s wobble as projected onto the sky, combined with relentless distortion of starlight traveling through our turbulent atmosphere. To detect a statistically meaningful population of gas giant planets by astrometry, we need to measure the position of stars so precisely that our uncertainty, in terms of angle on the celestial sphere, is less than 50 microarcseconds on each night we look. Even then, only after 2 years or more of data collecting can the signature of a planet’s orbit jump out of the data.

Over the last decade, the PRIMA instrument team, based at the European Southern Observatory, has made progress towards this formidable technological goal. How this machine works is a story for another day. But the ESPRI team (it stands for Exoplanet Survey with PRIMA) is leading the plan to make the best of use of PRIMA’s exoplanet science potential, once the instrument reaches its challenging performance specs.

There is competition on the way, however. Gaia, a satellite dedicated to high-precision astrometry, will launch into orbit within a year. Unhindered by Earth’s atmosphere, Gaia will achieve 20 microarcsecond astrometry precision for millions of stars over a five-year mission period. It was designed to answer questions about the structure and dynamical history of the Milky Way galaxy, but as a by-product, it will also uncover hundreds of gas giant planets orbiting nearby stars. Therefore, in planning our own target list, the ESPRI team is forced to ask the question, ‘What will Gaia not be able to do?’

The short answer is, the bright stars. Gaia’s detectors can only handle a certain range of brightness. So as not to lose out on many distant, faint stars, the designers of Gaia trade off sensitivity for the brightest ones. According to the official specs, stars of about 6th magnitude or brighter will overwhelm the detectors. This means that despite Gaia’s superb precision and total sky coverage, the recoil orbital motion of many stars will still be up for grabs. Furthermore, these stars are compelling for another reason. Tending to be nearby, their planetary systems will be the ones we can explore in greatest detail in the future with a suite of complementary techniques. Exciting follow-up observations will be possible with next generation, giant telescopes and satellite observatories, directly imaging the planets and measuring the colors and spectra of their atmospheres.

We’re also keeping in mind the stars that are left behind by another exoplanet discovery powerhouse, the radial velocity programs now based at large ground-based observatories. For a few scientific and technical reasons, these radial velocity surveys tend to concentrate on stars with mass near the same as the Sun, excluding the slightly larger ones.

Taking these facts together, ESPRI’s planet-hunting advantage boils down to a list of nearby and bright stars, with mass around 1.5 to 2.5 times that of the Sun (spectral types A and F). In the last week I’ve written scripts to dig through catalogs to find stars fitting our criteria: too bright for Gaia, the right color (since color tells us mass) and distance. This is complicated by the special needs of our instrument. We can’t deal with close double star systems, and we have to be careful to include only stars that have the right combination of mass and distance such that a planet orbiting it would cause a wobble big enough for our instrument to perceive.

The plot below visualizes a the process of elimination of target candidates from 795 to 222, after taking into account all the information culled from the astronomical databases. The vertical axis corresponds to brightness and the horizontal axis is color. The blue dots are the “survivors” that we will look at more closely to consider as ESPRI targets.