Related papers: SPIDAST: a new modular software to process spectro…
Intelligent spectrum management is crucial for improving spectrum efficiency and achieving secure utilization of spectrum resources. However, existing intelligent spectrum management methods, typically based on small-scale models, suffer…
Our knowledge about unresolved stellar systems comes from comparing integrated-light properties to SSP models. Therefore it is crucial to calibrate the latter as well as possible by integrated-light colors of clusters that have reliable…
In recent years, the detection of infrared small targets using deep learning methods has garnered substantial attention due to notable advancements. To improve the detection capability of small targets, these methods commonly maintain a…
We present an interactive IDL program for viewing and analyzing astronomical spectra in the context of modern imaging surveys. SpecPro's interactive design lets the user simultaneously view spectroscopic, photometric, and imaging data,…
We present a new version of the FIT3D and Pipe3D codes, two packages to derive properties of the stellar populations and the ionized emission lines from optical spectroscopy and integral field spectroscopy data respectively. The new codes…
We present a new processing technique aimed at significantly improving the angular differential imaging method (ADI) in the context of high-contrast imaging of faint objects nearby bright stars in observations obtained with extreme adaptive…
Stochastic simulation aims to compute output performance for complex models that lack analytical tractability. To ensure accurate prediction, the model needs to be calibrated and validated against real data. Conventional methods approach…
With the maturation of near-infrared high-resolution spectroscopy, especially when used for precision radial velocity, data reduction has faced unprecedented challenges in terms of how one goes from raw data to calibrated, extracted, and…
The Surface Brightness Fluctuation method is one of the most reliable and efficient ways of measuring distances to galaxies within 100 Mpc. While recent implementations have increasingly relied on space-based observations, SBF remains…
We present SNID-SAGE (SuperNova IDentification-Spectral Analysis and Guided Exploration), a framework for supernova spectral classification with both a fully interactive graphical interface and a scriptable command-line pipeline for…
FIRST (Fibered Imager foR a Single Telescope instrument) is a post-AO instrument that enables high contrast imaging and spectroscopy at spatial scales below the diffraction limit. FIRST achieves sensitivity and accuracy by a unique…
Advancing research in fields such as Simultaneous Localization and Mapping (SLAM) and autonomous navigation critically depends on the availability of reliable and reproducible multimodal datasets. While several influential datasets have…
Unlocking the full physical information encoded in low-resolution spectra poses a significant challenge for astronomical survey analysis. Such a task demands modeling spectra and optimizing astrophysical parameters in high-dimensional…
Presented here is decryst, a software suite for structure determination from powder diffraction, which uses the direct space method, and is able to apply anti-bump constraints automatically and efficiently during the procedure of global…
Three calibration campaigns of the spectrometer SPI have been performed before launch in order to determine the instrument characteristics, such as the effective detection area, the spectral resolution and the angular resolution. Absolute…
One of the main goals of the COROT mission is to get precise photometric observations of selected bright stars in order to allow the modelling of their interior through asteroseismology. However, in order to interpret the…
The SPI instrument has been launched on-board the INTEGRAL observatory on October 17, 2002. SPI is a spectrometer devoted to the sky observation in the 20 keV-8 MeV energy range using 19 germanium detectors. The performance of the cryogenic…
Training large AI models typically requires large-scale datasets in the machine learning process, making training and parameter-tuning process both time-consuming and costly. Some researchers address this problem by carefully synthesizing a…
Analysis of spatial multivariate data, i.e., measurements at irregularly-spaced locations, is a challenging topic in visualization and statistics alike. Such data are integral to many domains, e.g., indicators of valuable minerals are…
Nowadays, many of the images captured are `observed' by machines only and not by humans, e.g., in autonomous systems. High-level machine vision models, such as object recognition or semantic segmentation, assume images are transformed into…