The Dispersed Matter Planet Project: Machine Learning applied to space telescope data to find transits and new host stars

The Dispersed Matter Planet Project (DMPP) uses signatures of absorption by circumstellar
gas to identify the probable host stars of hot rocky exoplanets for study in the coming era of
exoplanetology (Haswell+2020). In our first small supersample of 6000 stars, we identified
39 bright, nearby target stars. Ourstate-of-the-art programme of high cadence, high precision
radial velocity (RV) observations has an essentially 100% record of detecting rocky exoplanets
in short period orbits around them. Our first five planetary systems are DMPP-1: a compact
multiplanet system, with multiple super-Earth planets orbiting a bright nearby star
(Staab+2020); DMPP-2, the joint first RV discovery of a planet orbiting a strongly pulsating
star (Haswell+2020); DMPP-3 which is a ~500d eccentric binary star system hosting one or
two circum-primary super-Earths (Barnes+2020, Stevenson+2023); DMPP-4 is a northern
hemisphere naked-eye host star of one or more sub-Neptune mass planets; DMPP-5 is a
young compact multi-planet system in the Hyades open cluster (Ross+2024) . Because the
stars were identified by the signatures of absorbing gas ablated from the close-in planets,
these systems are amenable to transmission spectroscopy which can reveal the planet
composition. Furthermore, angular momentum considerations suggest these planets have a
high probability of transiting. Thus, potentially, our planet discoveries will yield planet masses,
radii and compositions, all with small uncertainty ranges. This paves the way for comparative
exogeology.
This project will exploit the rapidly growing archival holdings of uniform, high quality data
from space telescopes.
Gaia Data Release 3 provides precise distances and uniform high resolution spectra
covering the CaII infrared triplet for a million stars. These data are revolutionising
stellar astrophysics, and make it possible to identify stars with absorbing circumstellar
gas for much larger samples than we have done so far. Crucially, this will allow us to
extend the Dispersed Matter Planet Project to stars at a range of ages. The Neptune
desert and the radius valley are prominent features in the demographics of short
period exoplanets resulting from the loss of gaseous envelopes / atmospheres from
hot planets. It seems likely that they are predominantly carved out early in the main
sequence lifetime of the host star. A large-scale analysis of Gaia DR3 data for the
signatures of circumstellar absorption should allow us to quantify this planetary mass
loss as a function of host star age.
The Transiting Exoplanet Survey Satellite (TESS) is observing most of the bright stars
in the sky, searching for transiting exoplanets. There is a huge international
collaboration working through the TESS data in a uniform way. This PhD project will
complement the on-going large-scale survey data analysis. Using the proprietary
DMPP target selection technique we know a priori the likely host stars of ablating, hot,
rocky, transiting planets. Having only a few dozen targets rather than hundreds of
thousands means we can sensibly invest significant effort in custom analysis of the
TESS data for our targets. We have already demonstrated this works, with the
discovery of a transit right at the detection threshold in TESS data on DMPP-1
(Jones+2020). This work drives down the detection threshold for transits in TESS
lightcurves. it is among the most shallow transits yet detected in TESS data with a dip
of only ~80 parts per million. We have a TESS guest investigator program which
ensures the best possible cadence data is collected for our targets. We have secured
a very significant award of CHaracterising ExOPlanet Satellite (CHEOPS) space
telescope time to follow-up the DMPP-1 transit