Related papers: Starfinder: a code for crowded stellar fields anal…
Recent advancements in areas such as natural language processing and computer vision rely on intricate and massive models that have been trained using vast amounts of unlabelled or partly labeled data and training or deploying these…
Machine Learning is an efficient method for analyzing and interpreting the increasing amount of astronomical data that is available. In this study, we show, a pedagogical approach that should benefit anyone willing to experiment with Deep…
We present allesfitter, a public and open-source python software for flexible and robust inference of stars and exoplanets given photometric and radial velocity data. Allesfitter offers a rich selection of orbital and transit/eclipse…
The Difference Image Analysis (DIA) of the images obtained by the Optical Gravitational Lensing Experiment (OGLE-II) revealed a peculiar artifact in the sample of stars proposed as variable by Wozniak (2000) in one of the Galactic bulge…
Photometric Redshift is critical for analyzing astronomical objects, but existing ML methods often overlook the aleatoric uncertainties inherent in observed data. We introduce Starkindler, a novel training objective that explicitly…
Owing to the remarkable photometric precision of space observatories like Kepler, stellar and planetary systems beyond our own are now being characterized en masse for the first time. These characterizations are pivotal for endeavors such…
Sky-coverage in laser-assisted AO observations largely depends on the system's capability to guide on the faintest natural guide-stars possible. Here we give an up-to-date status of our natural guide-star processing tailored to the…
The rapid growth of imaging and spectroscopic surveys has intensified the need for efficient tools that support visual inspection, a practice that remains essential for tasks such as classification, catalog refinement, and validation of…
We have developed a method for fast and accurate stellar population parameters determination in order to apply it to high resolution galaxy spectra. The method is based on an optimization technique that combines active learning with an…
We briefly describe the current version of the PHOENIX code. We then present some illustrative results from the modeling of Type Ia and Type II supernovae, hot stars, and irradiated giant planets. Good fits to observations can be obtained,…
While microlensing is very rare, occurring on average once per million stars observed, current and near-future surveys are coming online with the capability of providing photometry of almost the entire visible sky to depths up to R ~ 22 mag…
Machine learning allows efficient extraction of physical properties from stellar spectra that have been obtained by large surveys. The viability of ML approaches has been demonstrated for spectra covering a variety of wavelengths and…
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},…
The fields of view of Extremely Large Telescopes will contain vast numbers of spatial sampling elements (spaxels) as their Adaptive Optics systems approach the diffraction limit over wide fields. Since this will exceed the detection…
High-contrast imaging data analysis depends on removing residual starlight from the host star to reveal planets and disks. Most observers do this with principal components analysis (i.e. KLIP) using modes computed from the science images…
The stellar population synthesis in unresolved composite objects is a very tricky problem. Indeed, it is a degenerate problem since many parameters affect the observables. The stellar population synthesis issue thus deserves a deep and…
Large, deep surveys must typically rely on multiband photometry rather than spectroscopy for determining the astrophysical properties (APs) of stars. Yet designing an optimal photometric system for a wide range of objects is complex,…
Affine linking numbers are the generalization of linking numbers to the case of nonzero homologous linked submanifolds. They were introduced by Rudyak and the first author who used them to study causality in globally hyperbolic spacetimes.…
The anisotropic 2-point correlation function (2PCF) of galaxies measures pairwise clustering as a function of the pair separation's angle to the line of sight. The latter is often defined as either the angle bisector of the…
I present a new FITS viewer designed to explore 3D spectral line data (in particular HI) and assist with visual source extraction and analysis. Using the artistic software Blender, FRELLED can visualise even large (~600^3 voxels) data sets…