Related papers: TA-DA: a Tool for Astrophysical Data Analysis
The second Gaia data release (Gaia-DR2) contains, beyond the astrometry, three-band photometry for 1.38 billion sources. We have used these three broad bands to infer stellar effective temperatures, Teff, for all sources brighter than G=17…
The rapid growth in scale and complexity of both computational and observational astrophysics over the past decade necessitates efficient and intuitive methods for examining and visualizing large datasets. Here, I present {\it AstroBlend},…
Spectroscopic surveys are undergoing a rapid expansion in their data collecting capabilities, reaching the level of hundreds of spectra per pointing. An efficient use of such huge amounts of information requires a high degree of…
The field of astronomy is experiencing a data explosion driven by significant advances in observational instrumentation, and classical methods often fall short of addressing the complexity of modern astronomical datasets. Probabilistic…
With the launch and application of next-generation ground- and space-based telescopes, astronomy has entered the era of big data, necessitating more efficient and robust data analysis methods. Most traditional parameter estimation methods…
Synthetically augmenting training datasets with diffusion models has become an effective strategy for improving the generalization of image classifiers. However, existing approaches typically increase dataset size by 10-30x and struggle to…
Photometry of galaxies has typically focused on small, faint systems due to their interest for cosmological studies. Large angular size galaxies, on the other hand, offer a more detailed view into the properties of galaxies, but bring a…
We pose the question of how much information on the atmospheric parameters of late-type stars can be retrieved purely from colors using standard photometric systems. We carried out numerical experiments using stellar fluxes from model…
We describe a new software package capable of predicting the spectra of solar-system planets, exoplanets, brown dwarfs and cool stars. The Versatile Software for Transfer of Atmospheric Radiation (VSTAR) code combines a line-by-line…
Current and future continuum surveys being undertaken by the new generation of radio telescopes are now poised to address many important science questions, ranging from the earliest galaxies, to the physics of nearby AGN, as well as…
There is a great need to accurately predict short-term precipitation, which has socioeconomic effects such as agriculture and disaster prevention. Recently, the forecasting models have employed multi-source data as the multi-modality input,…
Tabular anomaly detection (TAD) aims to identify samples that deviate from the majority in tabular data and is critical in many real-world applications. However, existing methods follow a ``one model for one dataset (OFO)'' paradigm, which…
We are in the era of the Big Data. In Astronomy and Astrophysics, the massive amounts of data generated are, as of today, in the Peta-scale if not already in the Exa-scale. In the near future, we will see the data collected size and…
The Delta-variance analysis, has proven to be an efficient and accurate method of characterising the power spectrum of interstellar turbulence. The implementation presently in use, however, has several shortcomings. We propose and test an…
This paper introduces new methodology based on the field of Topological Data Analysis for detecting anomalies in multivariate time series, that aims to detect global changes in the dependency structure between channels. The proposed…
The aim of this paper is to describe a novel non-parametric noise reduction technique from the point of view of Bayesian inference that may automatically improve the signal-to-noise ratio of one- and two-dimensional data, such as e.g.…
The rapidly increasing volume of asteroseismic observations on solar-type stars has revealed a need for automated analysis tools. The reason for this is not only that individual analyses of single stars are rather time consuming, but more…
Symbolic data analysis (SDA) is an emerging area of statistics concerned with understanding and modelling data that takes distributional form (i.e. symbols), such as random lists, intervals and histograms. It was developed under the premise…
The Chandra Interactive Analysis of Observations (CIAO) software, developed by the Chandra X-ray Center, has been the data analysis package for the Chandra X-ray Observatory since its launch in 1999. Over nearly three decades, CIAO has…
Multivariate time-series forecasting, as a typical problem in the field of time series prediction, has a wide range of applications in weather forecasting, traffic flow prediction, and other scenarios. However, existing works do not…