Prof Suzanne Aigrain

Abstract:

In recent years, supergranulation has emerged as one of the biggest challenges for the detection of Earth-twins in radial velocity planet searches. We used eight years of Sun-as-a-star radial velocity observations from HARPS-N to measure the quiet-Sun’s granulation and supergranulation properties of most of its 11-yr activity cycle, after correcting for the effects of magnetically active regions using two independent methods. In both cases, we observe a clear, order of magnitude variation in the time-scale of the supergranulation component, which is largest at activity minimum and is strongly anticorrelated with the relative Sunspot number. We also explored a range of observational strategies which could be employed to characterize supergranulation in stars other than the Sun, showing that a comparatively long observing campaign of at least 23 nights is required, but that up to 10 stars can be monitored simultaneously in the process. We conclude by discussing plausible explanations for the ‘supergranulation’ cycle.

Abstract:

In the context of large scale photometric surveys, monitoring hundreds of thousands of stars in the search for exoplanets, one of the main bottlenecks remains reliable and rapid identification of exoplanet candidates. As it stands, the detection of exoplanets in light curves remains a complicated process, which can be thrown off by stellar activity, or instrument systematics. The task becomes increasingly harder for long period planets, taking away the ability to search for periodic signals within the high precision light curves. In an effort to find Earth-analogs, which are by definition long period planets, often with shallow transits, our ability to avoid periodicity in the detection process is key. Additionally, since current filtering methods are not well suited to filter unique, shallow, transits, they risk erasing the presence of these signals altogether before the detection step can be run. Such cases not only lead to missed planets, but they also induce a bias in the final distribution, by removing key planets in our sample.To this end, we develop the Panopticon deep learning model, trained to identify transits individually in unfiltered light curves. First trained on simulated PLATO data [1], we report the model’s ability to correctly identify >99% of the light curves containing transits with a SNR>3 (Fig.1), while keeping a false alarm rate of less than 0.01% [2]. When applied on a new, independent, dataset in a blind search scenario, we are able to confidently recover the transiting planets in >98% of the cases. In a second time, a dedicated version of the model was trained on TESS data to measure the impact of real world data on the model. As for previously, we find the model to be highly effective at recovering transits, correctly reporting >93% of the light curves containing transits, while achieving a false alarm rate of

Abstract:

Automated planetary transit detection has become vital to identify and prioritize candidates for expert analysis and verification given the scale of modern telescopic surveys. Current methods for short-period exoplanet detection work effectively due to periodicity in the transit signals, but a robust approach for detecting single-transit events is lacking. However, volunteer-labeled transits collected by the Planet Hunters TESS (PHT) project now provide an unprecedented opportunity to investigate a data-driven approach to long-period exoplanet detection. In this work, we train a 1D convolutional neural network to classify planetary transits using PHT volunteer scores as training data. We find that this model recovers planet candidates (TESS objects of interest; TOIs) at a precision and recall rate exceeding those of volunteers, with a 20% improvement in the area under the precision-recall curve and 10% more TOIs identified in the top 500 predictions on average per sector. Importantly, the model also recovers almost all planet candidates found by volunteers but missed by current automated methods (PHT community TOIs). Finally we retrospectively utilise the model to simulate live deployment in PHT to reprioritize candidates for analysis. We also find that multiple promising planet candidates, originally missed by PHT, would have been found using our approach, showing promise for upcoming real-world deployment.

Abstract:

We present a JWST Near Infrared Imager and Slitless Spectrograph/Single Object Slitless Spectroscopy transmission spectrum of the super-Earth GJ 357 b: the first atmospheric observation of this exoplanet. Despite missing the first 40 per cent of the transit due to using an out-of-date ephemeris, we still recover a transmission spectrum that does not display any clear signs of atmospheric features. We perform a search for Gaussian-shaped absorption features within the data but find that this analysis yields comparable fits to the observations as a flat line. We compare the transmission spectrum to a grid of atmosphere models and reject, to 3 confidence, atmospheres with metallicities solar (4 g mol−1) with clouds at pressures down to 0.01 bar. We analyse how the retention of a secondary atmosphere on GJ 357 b may be possible due to its higher escape velocity compared to an Earth-sized planet and the exceptional inactivity of its host star relative to other M2.5V stars. The star’s XUV luminosity decays below the threshold for rapid atmospheric escape early enough that the volcanic revival of an atmosphere of several bars of CO is plausible, though subject to considerable uncertainty. Finally, we model the feasibility of detecting an atmosphere on GJ 357 b with MIRI/LRS, MIRI photometry, and NIRSpec/G395H. We find that, with two eclipses, it would be possible to detect features indicative of an atmosphere or surface. Further to this, with three to four transits, it would be possible to detect a 1 bar nitrogen-rich atmosphere with 1000 ppm of CO.