相关论文: Optimizing exoplanet atmosphere retrieval using un…
We employ unsupervised machine learning techniques to learn latent parameters which best describe states of the two-dimensional Ising model and the three-dimensional XY model. These methods range from principal component analysis to…
Inverse Compton (IC) emission associated with the non-thermal component of the intracluster medium (ICM) has been a long sought phenomenon in cluster physics. Traditional spectral fitting often suffers from the degeneracy between the…
Principal Component Analysis (PCA) minimizes the reconstruction error given a class of linear models of fixed component dimensionality. Probabilistic PCA adds a probabilistic structure by learning the probability distribution of the PCA…
Studying the atmospheres of exoplanets is one of the most promising ways to learn about distant worlds beyond our solar system. The composition of an exoplanet's atmosphere can provide critical insights into its geology and potential…
We introduce a new machine learning based technique to detect exoplanets using the transit method. Machine learning and deep learning techniques have proven to be broadly applicable in various scientific research areas. We aim to exploit…
In health-pollution cohort studies, accurate predictions of pollutant concentrations at new locations are needed, since the locations of fixed monitoring sites and study participants are often spatially misaligned. For multi-pollution data,…
We present a new method to retrieve molecular abundances and temperature profiles from exoplanet atmosphere photometry and spectroscopy. We run millions of 1D atmosphere models in order to cover the large range of allowed parameter space,…
The exoplanet archive is an incredible resource of information on the properties of discovered extrasolar planets, but statistical analysis has been limited by the number of missing values. One of the most informative bulk properties is…
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…
The majority of exoplanets found to date have been discovered via the transit method, and transmission spectroscopy represents the primary method of studying these distant worlds. Currently, in-depth atmospheric characterization of…
The characterization of exoplanetary atmospheres through spectral analysis is a complex challenge. The NeurIPS 2024 Ariel Data Challenge, in collaboration with the European Space Agency's (ESA) Ariel mission, provided an opportunity to…
We propose a method for observing transiting exoplanets with near-infrared high-resolution spectrometers. We aim to create a robust data analysis method for recovering atmospheric transmission spectra from transiting exoplanets over a wide…
We extend a data-based model-free multifractal method of exoplanet detection to probe exoplanetary atmospheres. Whereas the transmission spectrum is studied during the primary eclipse, we analyze the emission spectrum during the secondary…
In current models used to interpret exoplanet atmospheric observations, the planet mass is treated as a prior and is estimated independently with external methods, such as RV or TTV techniques. This approach is necessary as available…
Future space telescopes now in the concept and design stage aim to observe reflected light spectra of extrasolar planets. Assessing whether given notional mission and instrument design parameters will provide data suitable for constraining…
Interpreting the observations of exoplanet atmospheres to constrain physical and chemical properties is typically done using Bayesian retrieval techniques. Because these methods require many model computations, a compromise is made between…
We consider an online version of the robust Principle Component Analysis (PCA), which arises naturally in time-varying source separations such as video foreground-background separation. This paper proposes a compressive online robust PCA…
Atmospheric correction is a fundamental task in remote sensing because observations are taken either of the atmosphere or looking through the atmosphere. Atmospheric correction errors can significantly alter the spectral signature of the…
Recent spectroscopic observations of transiting hot Jupiters have permitted the derivation of the thermal structure and molecular abundances of H2O, CO2, CO, and CH4 in these extreme atmospheres. Here, for the first time, we apply the…
Thermal emission spectra of exoplanets provide constraints on the chemical compositions, pressure-temperature (P-T) profiles, and energy transport in exoplanetary atmospheres. Accurate inferences of these properties rely on the robustness…