Aomawa Shields '97 was equally enticed by the prospect of studying stars and the dream of becoming one herself. Today, she draws from her exploration of acting and astronomy to search for life on other planets. Few people, if any, contemplate stars--celestial or cinematic--the way Aomawa Shields does. An astronomer and astrobiologist, Shields explores the potential habitability of planets beyond our solar system. But she is also a classically trained actor--and that's helped shape her professional trajectory in unexpected ways. Today, Shields is an associate professor in the Department of Physics and Astronomy at the University of California, Irvine, where she oversees a research team that uses computer models to explore conditions on exoplanets, or planets that revolve around stars other than the sun.

Astronomers initially thought the dramatic burst of light was a new exoplanet. Breakthroughs, discoveries, and DIY tips sent every weekday. What astronomers initially suspected to be a new exoplanet is actually a never-before-seen, head-on cosmic crash. As detailed in a study published today in the journal, researchers describe the aftermath of two separate collisions between two small, rocky cosmic objects called planetesimals . However, their findings were only made possible by some eagle eye imaging courtesy of the Hubble Space Telescope .

In this work, we propose a new flow-matching Markov chain Monte Carlo (FM-MCMC) algorithm for estimating the orbital parameters of exoplanetary systems, especially for those only one exoplanet is involved. Compared to traditional methods that rely on random sampling within the Bayesian framework, our approach first leverages flow matching posterior estimation (FMPE) to efficiently constrain the prior range of physical parameters, and then employs MCMC to accurately infer the posterior distribution. For example, in the orbital parameter inference of beta Pictoris b, our model achieved a substantial speed-up while maintaining comparable accuracy-running 77.8 times faster than Parallel Tempered MCMC (PTMCMC) and 365.4 times faster than nested sampling. Moreover, our FM-MCMC method also attained the highest average log-likelihood among all approaches, demonstrating its superior sampling efficiency and accuracy. This highlights the scalability and efficiency of our approach, making it well-suited for processing the massive datasets expected from future exoplanet surveys. Beyond astrophysics, our methodology establishes a versatile paradigm for synergizing deep generative models with traditional sampling, which can be adopted to tackle complex inference problems in other fields, such as cosmology, biomedical imaging, and particle physics.

From lava worlds to gas giants, NASA says the variety of these worlds is staggering--and that signs of a further 8,000 distant planets are awaiting confirmation. The number of confirmed planets outside of our solar system--known as exoplanets-- has risen to 6,000, NASA has said. There is huge variety across these distant worlds, the space agency says, with discoveries including rocky planets, lava worlds, and gas giants enveloping their stars. Plenty more discoveries are likely on the way. As a result of continued monitoring by NASA's Exoplanet Science Institute (NExScI), there are more than 8,000 potential planets that have been identified and are awaiting confirmation.

Exoplanet imaging is a major challenge in astrophysics due to the need for high angular resolution and high contrast. We present a multi-scale statistical model for the nuisance component corrupting multivariate image series at high contrast. Integrated into a learnable architecture, it leverages the physics of the problem and enables the fusion of multiple observations of the same star in a way that is optimal in terms of detection signal-to-noise ratio. Applied to data from the VLT/SPHERE instrument, the method significantly improves the detection sensitivity and the accuracy of astrometric and photometric estimation.

With manual searching processes, the rate at which scientists and astronomers discover exoplanets is slow because of inefficiencies that require an extensive time of laborious inspections. In fact, as of now there have been about only 5,000 confirmed exoplanets since the late 1900s. Recently, machine learning (ML) has proven to be extremely valuable and efficient in various fields, capable of processing massive amounts of data in addition to increasing its accuracy by learning. Though ML models for discovering exoplanets owned by large corporations (e.g. NASA) exist already, they largely depend on complex algorithms and supercomputers. In an effort to reduce such complexities, in this paper, we report the results and potential benefits of various, well-known ML models in the discovery and validation of extrasolar planets. The ML models that are examined in this study include logistic regression, k-nearest neighbors, and random forest. The dataset on which the models train and predict is acquired from NASA's Kepler space telescope. The initial results show promising scores for each model. However, potential biases and dataset imbalances necessitate the use of data augmentation techniques to further ensure fairer predictions and improved generalization. This study concludes that, in the context of searching for exoplanets, data augmentation techniques significantly improve the recall and precision, while the accuracy varies for each model.

Research into light curves from stars (temporal variation of brightness) has completely changed how exoplanets are discovered or characterised. This study including star light curves from the Kepler dataset as a way to discover exoplanets (planetary transits) and derive some estimate of their physical characteristics by the light curve and machine learning methods. The dataset consists of measured flux (recordings) for many individual stars and we will examine the light curve of each star and look for periodic dips in brightness due to an astronomical body making a transit. We will apply variables derived from an established method for deriving measurements from light curve data to derive key parameters related to the planet we observed during the transit, such as distance to the host star, orbital period, radius. The orbital period will typically be measured based on the time between transit of the subsequent timelines and the radius will be measured based on the depth of transit. The density of the star and planet can also be estimated from the transit event, as well as very limited information on the albedo (reflectivity) and atmosphere of the planet based on transmission spectroscopy and/or the analysis of phase curve for levels of flux. In addition to these methods, we will employ some machine learning classification of the stars (i.e. likely have an exoplanet or likely do not have an exoplanet) based on flux change. This could help fulfil both the process of looking for exoplanets more efficient as well as providing important parameters for the planet. This will provide a much quicker means of searching the vast astronomical datasets for the likelihood of exoplanets.

With over 1,000,000 images from more than 10,000 exposures using state-of-the-art high-contrast imagers (e.g., Gemini Planet Imager, VLT/SPHERE) in the search for exoplanets, can artificial intelligence (AI) serve as a transformative tool in imaging Earth-like exoplanets in the coming decade? In this paper, we introduce a benchmark and explore this question from a polarimetric image representation learning perspective. Despite extensive investments over the past decade, only a few new exoplanets have been directly imaged. Existing imaging approaches rely heavily on labor-intensive labeling of reference stars, which serve as background to extract circumstellar objects (disks or exoplanets) around target stars. With our POLARIS (POlarized Light dAta for total intensity Representation learning of direct Imaging of exoplanetary Systems) dataset, we classify reference star and circumstellar disk images using the full public SPHERE/IRDIS polarized-light archive since 2014, requiring less than 10 percent manual labeling. We evaluate a range of models including statistical, generative, and large vision-language models and provide baseline performance. We also propose an unsupervised generative representation learning framework that integrates these models, achieving superior performance and enhanced representational power. To our knowledge, this is the first uniformly reduced, high-quality exoplanet imaging dataset, rare in astrophysics and machine learning. By releasing this dataset and baselines, we aim to equip astrophysicists with new tools and engage data scientists in advancing direct exoplanet imaging, catalyzing major interdisciplinary breakthroughs.

In a'transformational' discovery, scientists have discovered the strongest evidence of life on a distant alien planet. Using data from the James Webb Space Telescope, astronomers found huge quantities of chemicals produced by life on Earth in the atmosphere of the planet K2-18b. According to scientists from the University of Cambridge, an'ocean that is teeming with life' is the best explanation for this stunning discovery. MailOnline has used AI to take scientists' best predictions and imagine what life might be like on K2-18b. The most likely scenario is that K2-18b's oceans are filled with something like phytoplankton - microscopic organisms that feed on the energy from the nearby star.

The search for exoplanets is an active field in astronomy, with direct imaging as one of the most challenging methods due to faint exoplanet signals buried within stronger residual starlight. Successful detection requires advanced image processing to separate the exoplanet signal from this nuisance component. This paper presents a novel statistical model that captures nuisance fluctuations using a multi-scale approach, leveraging problem symmetries and a joint spectral channel representation grounded in physical principles. Our model integrates into an interpretable, end-to-end learnable framework for simultaneous exoplanet detection and flux estimation. The proposed algorithm is evaluated against the state of the art using datasets from the SPHERE instrument operating at the Very Large Telescope (VLT). It significantly improves the precision-recall trade-off, notably on challenging datasets that are otherwise unusable by astronomers. The proposed approach is computationally efficient, robust to varying data quality, and well suited for large-scale observational surveys.