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View article: AMBER -- Advanced SegFormer for Multi-Band Image Segmentation: an application to Hyperspectral Imaging
AMBER -- Advanced SegFormer for Multi-Band Image Segmentation: an application to Hyperspectral Imaging Open
Deep learning has revolutionized the field of hyperspectral image (HSI) analysis, enabling the extraction of complex spectral and spatial features. While convolutional neural networks (CNNs) have been the backbone of HSI classification, th…
View article: <i>Euclid</i> preparation
<i>Euclid</i> preparation Open
The Euclid mission is expected to image millions of galaxies at high resolution, providing an extensive dataset with which to study galaxy evolution. Because galaxy morphology is both a fundamental parameter and one that is hard to determi…
View article: ALMA Band 3 Source Counts: A Machine Learning Approach to Contamination Mitigation below 5 Sigma
ALMA Band 3 Source Counts: A Machine Learning Approach to Contamination Mitigation below 5 Sigma Open
We performed differential number counts down to 4.25 sigma using ALMA Band 3 calibrator images, which are known for their high dynamic range and susceptibility to various types of contamination. Estimating the fraction of contaminants is a…
View article: Euclid preparation. XLIII. Measuring detailed galaxy morphologies for\n Euclid with machine learning
Euclid preparation. XLIII. Measuring detailed galaxy morphologies for\n Euclid with machine learning Open
The Euclid mission is expected to image millions of galaxies with high\nresolution, providing an extensive dataset to study galaxy evolution. We\ninvestigate the application of deep learning to predict the detailed\nmorphologies of galaxie…
View article: A BRAIN Study to Tackle Image Analysis with Artificial Intelligence in the ALMA 2030 Era
A BRAIN Study to Tackle Image Analysis with Artificial Intelligence in the ALMA 2030 Era Open
An ESO internal ALMA development study, BRAIN, is addressing the ill-posed inverse problem of synthesis image analysis, employing astrostatistics and astroinformatics. These emerging fields of research offer interdisciplinary approaches at…
View article: A BRAIN study to tackle image analysis with artificial intelligence in the ALMA 2030 era
A BRAIN study to tackle image analysis with artificial intelligence in the ALMA 2030 era Open
An ESO internal ALMA development study, BRAIN, is addressing the ill-posed inverse problem of synthesis image analysis employing astrostatistics and astroinformatics. These emerging fields of research offer interdisciplinary approaches at …
View article: SKA Science Data Challenge 2: analysis and results
SKA Science Data Challenge 2: analysis and results Open
The Square Kilometre Array Observatory (SKAO) will explore the radio sky to new depths in order to conduct transformational science. SKAO data products made available to astronomers will be correspondingly large and complex, requiring the …
View article: Repeating Outbursts from the Young Stellar Object Gaia23bab (=SPICY 97589)
Repeating Outbursts from the Young Stellar Object Gaia23bab (=SPICY 97589) Open
The light curve of Gaia23bab (=SPICY 97589) shows two significant (Δ G > 2 mag) brightening events, one in 2017 and an ongoing event starting in 2022. The source’s quiescent spectral energy distribution indicates an embedded ( A V > 5 mag)…
View article: Repeating Outbursts from the Young Stellar Object Gaia23bab (= SPICY 97589)
Repeating Outbursts from the Young Stellar Object Gaia23bab (= SPICY 97589) Open
The light curve of Gaia23bab (= SPICY 97589) shows two significant ($ΔG>2$ mag) brightening events, one in 2017 and an ongoing event starting in 2022. The source's quiescent spectral energy distribution indicates an embedded ($A_V>5$ mag) …
View article: Bayesian and Machine Learning Methods in the Big Data Era for Astronomical Imaging
Bayesian and Machine Learning Methods in the Big Data Era for Astronomical Imaging Open
The Atacama large millimeter/submillimeter array with the planned electronic upgrades will deliver an unprecedented number of deep and high resolution observations. Wider fields of view are possible with the consequential cost of image rec…
View article: 3D detection and characterization of ALMA sources through deep learning
3D detection and characterization of ALMA sources through deep learning Open
We present a deep learning (DL) pipeline developed for the detection and characterization of astronomical sources within simulated Atacama Large Millimeter/submillimeter Array (ALMA) data cubes. The pipeline is composed of six DL models: a…
View article: Bayesian and Machine Learning Methods in the Big Data era for astronomical imaging
Bayesian and Machine Learning Methods in the Big Data era for astronomical imaging Open
The Atacama Large Millimeter/submillimeter Array with the planned electronic upgrades will deliver an unprecedented amount of deep and high resolution observations. Wider fields of view are possible with the consequential cost of image rec…
View article: How Have Astronomers Cited Other Fields in the Last Decade?
How Have Astronomers Cited Other Fields in the Last Decade? Open
We present a citation pattern analysis between astronomical papers and 13 other disciplines, based on the arXiv database over the past decade (2010–2020). We analyze 12,600 astronomical papers citing over 14,531 unique publications outside…
View article: Rejection Criteria Based on Outliers in the KiDS Photometric Redshifts and PDF Distributions Derived by Machine Learning
Rejection Criteria Based on Outliers in the KiDS Photometric Redshifts and PDF Distributions Derived by Machine Learning Open
View article: Star formation rates for photometric samples of galaxies using machine learning methods
Star formation rates for photometric samples of galaxies using machine learning methods Open
Star Formation Rates or SFRs are crucial to constrain theories of galaxy\nformation and evolution. SFRs are usually estimated via spectroscopic\nobservations requiring large amounts of telescope time. We explore an\nalternative approach ba…
View article: Stellar formation rates in galaxies using Machine Learning models
Stellar formation rates in galaxies using Machine Learning models Open
Global Stellar Formation Rates or SFRs are crucial to constrain theories of galaxy formation and evolution. SFR's are usually estimated via spectroscopic observations which require too much previous telescope time and therefore cannot matc…
View article: Stellar formation rates in galaxies using Machine Learning models
Stellar formation rates in galaxies using Machine Learning models Open
Global Stellar Formation Rates or SFRs are crucial to constrain theories of galaxy formation and evolution. SFR's are usually estimated via spectroscopic observations which require too much previous telescope time and therefore cannot matc…