Related papers: Supervised Machine Learning Methods with Uncertain…

The detection of exoplanets in high-contrast imaging (HCI) data hinges on post-processing methods to remove spurious light from the host star. So far, existing methods for this task hardly utilize any of the available domain knowledge about…

Atmospheric retrievals are now a standard tool to analyze observations of exoplanet atmospheres. This data-driven approach quantitatively compares atmospheric models to observations in order to estimate atmospheric properties and their…

The need of real-time of monitoring and alerting systems for Space Weather hazards has grown significantly in the last two decades. One of the most important challenge for space mission operations and planning is the prediction of solar…

This article summarizes a workshop held on March, 2014, on the potential of the James Webb Space Telescope (JWST) to revolutionize our knowledge of the physical properties of exoplanets through transit observations. JWST's unique…

We present a machine learning method to assign stellar parameters (temperature, surface gravity, metallicity) to the photometric data of large photometric surveys such as SDSS and SKYMAPPER. The method makes use of our previous effort in…

The Hubble Space Telescope (HST) has been our most prolific tool to study exoplanet atmospheres. As the age of JWST begins, there is a wealth of HST archival data that is useful to strengthen our inferences from JWST. Notably, HST/STIS and…

Exoplanets with a long orbital period are difficult to discover by extant methods. Our first publication (Lerner, P., A. Mayer, T. E. Sullivan. 2023. A new method for the discovery of the distant exoplanets. SPIE Proceedings 12680:…

A novel artificial intelligence (AI) technique that uses machine learning (ML) methodologies combines several algorithms, which were developed by ThetaRay, Inc., is applied to NASA's Transiting Exoplanets Survey Satellite (TESS) dataset to…

High-contrast imaging of exoplanets hinges on powerful post-processing methods to denoise the data and separate the signal of a companion from its host star, which is typically orders of magnitude brighter. Existing post-processing…

Aerosols are capable of having a huge influence on reflected, emitted and transmitted planetary spectra. The Near InfraRed Spectrograph (NIRSpec) using the PRISM mode on board of the James Webb Space Telescope (JWST) is providing valuable…

In this paper a homomorphic filtering scheme is proposed to improve the estimation of the planet/star radius ratio in astronomical transit signals. The idea is to reduce the effect of the short-term earth atmosphere variations. A two-step…

Atmospheres of transiting exoplanets can be studied spectroscopically using space-based or ground-based observations. Each has its own strengths and weaknesses, so there are benefits to both approaches. This is especially true for…

Atmospheric models used for weather and climate prediction are traditionally formulated in a deterministic manner. In other words, given a particular state of the resolved scale variables, the most likely forcing from the sub-grid scale…

Gaussian processes (GPs) are a popular model for spatially referenced data and allow descriptive statements, predictions at new locations, and simulation of new fields. Often a few parameters are sufficient to parameterize the covariance…

In this article, we argue that models based on machine learning (ML) can be very effective in estimating the non-linear matter power spectrum ($P(k)$). We employ the prediction ability of the supervised ML algorithms to build an estimator…

Advancements in our understanding of exoplanetary atmospheres, from massive gas giants down to rocky worlds, depend on the constructive challenges between observations and models. We are now on a clear trajectory for improvements in…

Most machine learning techniques are based upon statistical learning theory, often simplified for the sake of computing speed. This paper is focused on the uncertainty aspect of mathematical modeling in machine learning. Regression analysis…

The complexity of atmospheric retrieval models is largely data-driven and one-dimensional models have generally been considered adequate with current data quality. However, recent studies have suggested that using 1D models in retrievals…

Photometric redshifts (photo-z's) provide an alternative way to estimate the distances of large samples of galaxies and are therefore crucial to a large variety of cosmological problems. Among the various methods proposed over the years,…

As the James Webb Space Telescope (JWST) came online last summer, we entered a new era of astronomy. This new era is supported by data products of unprecedented information content that require novel reduction and analysis techniques.…