Related papers: The Automated Photometry Of Transients (AutoPhOT) …
I developed a new pure-python pipeline to reduce photometric and polarimetric data: ASTROPOP. It has been designed and optimized to work fully automated with the IAGPOL polarimeter of Pico dos Dias observatory (OPD, Brazil) and can reduce…
We have developed an end-to-end photometric data processing pipeline to compare current photometric algorithms commonly used on ground-based imaging data. This testbed is exceedingly adaptable, and enables us to perform many research and…
The ``P32'' Astronomical Observation Template (AOT) provided a means to map large areas of sky (up to 45x45 arcmin) in the FIR at high redundancy and with sampling close to the Nyquist limit using the ISOPHOT C100 (3x3) and C200 (2x2)…
lightcurver is a photometric pipeline for time series astronomical imaging data, designed for the semi-automatic extraction of precise light curves from small, blended targets. Such targets include, but are not limited to, lensed quasars,…
The direct detection and characterization of planetary and substellar companions at small angular separations is a rapidly advancing field. Dedicated high-contrast imaging instruments deliver unprecedented sensitivity, enabling detailed…
An open-source and modular Python package, Catalight, is developed and demonstrated to automate (photo)catalysis measurements. (Photo)catalysis experiments require studying several parameters to evaluate performance, including temperature,…
Autoplot is software developed for the Virtual Observatories in Heliophysics to provide intelligent and automated plotting capabilities for many typical data products that are stored in a variety of file formats or databases. Autoplot has…
A new era of space exploration and exploitation is fast approaching. A multitude of spacecraft will flow in the future decades under the propulsive momentum of the new space economy. Yet, the flourishing proliferation of deep-space assets…
The ``P32'' Astronomical Observation Template (AOT) provided a means to map large areas of sky (up to 45 x 45 arcmin) in the far-infrared (FIR) at high redundancy and with sampling close to the Nyquist limit using the ISOPHOT C100 (3 x 3)…
We introduce GeoDiT, a diffusion transformer designed for text-to-satellite image generation with point-based control. Existing controlled satellite image generative models often require pixel-level maps that are time-consuming to acquire,…
In this paper, we present an automated pipeline for generating domain-specific synthetic datasets with diffusion models, addressing the distribution shift between pre-trained models and real-world deployment environments. Our three-stage…
We describe 2DPHOT, a general purpose analysis environment for source detection and analysis in deep wide-field images. 2DPHOT is an automated tool to obtain both integrated and surface photometry of galaxies in an image, to perform…
Medical image segmentation is vital for clinical diagnosis, yet current deep learning methods often demand extensive expert effort, i.e., either through annotating large training datasets or providing prompts at inference time for each new…
Modern telescopes generate increasingly large and diverse datasets, often consisting of complex and morphologically rich structures. To efficiently explore such data requires automated methods that can extract and organize physically…
AOSAT is a python package for the analysis of single-conjugate adaptive optics (SCAO) simulation results. Python is widely used in the astronomical community these days, and AOSAT may be used stand-alone, integrated into a simulation…
To reduce and analyze astronomical images, astronomers can rely on a wide range of libraries providing low-level implementations of legacy algorithms. However, combining these routines into robust and functional pipelines requires a major…
Much of the progress made in time-domain astronomy is accomplished by relating observational multi-wavelength time series data to models derived from our understanding of physical laws. This goal is typically accomplished by dividing the…
Machine learning-based interatomic potentials and force fields depend critically on accurate atomic structures, yet such data are scarce due to the limited availability of experimentally resolved crystals. Although atomic-resolution…
We present a new software pipeline -- PyMorph -- for automated estimation of structural parameters of galaxies. Both parametric fits through a two dimensional bulge disk decomposition as well as structural parameter measurements like…
Scanning transmission electron microscopy is a common tool used to study the atomic structure of materials. It is an inherently multimodal tool allowing for the simultaneous acquisition of multiple information channels. Despite its…