Related papers: Using Independent Component Analysis to detect exo…
In recent years, there has been growing interest in jointly analyzing a foreground dataset, representing an experimental group, and a background dataset, representing a control group. The goal of such contrastive investigations is to…
In this paper we explore the possibility that the recently detected reflected light signal of 51\,Peg\,b could be caused by a ring system around the planet. We use a simple model to compare the observed signal with the expected signal from…
Independent component analysis (ICA) is a method to extract a set of time-series data using ``statistical independency" of each component. We applied ICA to extract gravitational wave (GW) signals directly from the detector data. Our idea…
We analyzed four Spitzer/IRAC observations at 3.6 and 4.5 {\mu}m of the primary transit of the exoplanet GJ436b, by using blind source separation techniques. These observations are important to investigate the atmospheric composition of the…
AIMS: One of the most challenging and important problem of digital signal processing in Cosmology is the separation of foreground contamination from cosmic microwave background (CMB). This problem becomes even more difficult in situations,…
The direct detection of reflected light from exoplanets is an excellent probe for the characterization of their atmospheres. The greatest challenge for this task is the low planet-to-star flux ratio, which even in the most favourable case…
In this work we study the relevance of the component separation technique based on the Independent Component Analysis (ICA) and investigate its performance in the context of a limited sky coverage observation and from the viewpoint of our…
Independent Component Analysis (ICA) is a technique for unsupervised exploration of multi-channel data that is widely used in observational sciences. In its classic form, ICA relies on modeling the data as linear mixtures of non-Gaussian…
Context: Infrared spectroscopy of primary and secondary eclipse events probes the composition of exoplanet atmospheres and, using space telescopes, has detected H2O, CH4 and CO2 in three hot Jupiters. However, the available data from space…
To date, the ability for observers to reveal the composition or thermal structure of an exoplanet's atmosphere has rested on two techniques: high-contrast direct imaging and time-series observations of transiting exoplanets. The former is…
In this paper, we apply a new statistical analysis technique, Mean Field approach to Bayesian Independent Component Analysis (MF-ICA), on galaxy spectral analysis. This algorithm can compress the stellar spectral library into a few…
Independent component analysis (ICA) is a computational method for separating a multivariate signal into subcomponents assuming the mutual statistical independence of the non-Gaussian source signals. The classical Independent Components…
Principal Components Analysis (PCA) and Independent Component Analysis (ICA) are used to identify global patterns in solar and space data. PCA seeks orthogonal modes of the two-point correlation matrix constructed from a data set. It…
Independent component analysis (ICA) is a method for recovering statistically independent signals from observations of unknown linear combinations of the sources. Some of the most accurate ICA decomposition methods require searching for the…
Independent component analysis (ICA) is a powerful tool for decomposing a multivariate signal or distribution into fully independent sources, not just uncorrelated ones. Unfortunately, most approaches to ICA are not robust against outliers.…
The extreme contrast ratios between stars and their planets at optical wavelengths make it challenging to isolate the light reflected by exoplanet atmospheres. Yet, these reflective properties reveal key processes occurring in the…
Transmission spectroscopy can be used to constrain the properties of exoplanetary atmospheres. During a transit, the light blocked from the atmosphere of the planet leaves an imprint in the light coming from the star. This has been shown…
Independent Component Analysis (ICA) is an algorithm originally developed for finding separate sources in a mixed signal, such as a recording of multiple people in the same room speaking at the same time. Unlike Principal Component Analysis…
Context: Combining high-contrast imaging with medium- or high-resolution integral field spectroscopy has the potential to boost the detection rate of exoplanets, especially at small angular separations. Furthermore, it immediately provides…
Independent Component Analysis (ICA) is a technique for unsupervised exploration of multi-channel data widely used in observational sciences. In its classical form, ICA relies on modeling the data as a linear mixture of non-Gaussian…