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Machine learning algorithms are highly useful for the classification of time series data in astronomy in this era of peta-scale public survey data releases. These methods can facilitate the discovery of new unknown events in most…
The goal of this paper is to perform object detection in satellite imagery with only a few examples, thus enabling users to specify any object class with minimal annotation. To this end, we explore recent methods and ideas from…
We present a novel approach for classifying stars as binary or exoplanet using deep learning techniques. Our method utilizes feature extraction, wavelet transformation, and a neural network on the light curves of stars to achieve…
One-shot object detection aims at detecting novel objects according to merely one given instance. With extreme data scarcity, current approaches explore various feature fusions to obtain directly transferable meta-knowledge. Yet, their…
Context. Determining properties of dust formed in and around supernovae from observations remains challenging. This may be due to either incomplete coverage of data in wavelength or time but also due to often inconspicuous signatures of…
We present an original phenomenological model to describe the evolution of galaxy number counts, morphologies, and spectral energy distributions across a wide range of redshifts (0.2<z<15) and stellar masses [Log10 M/Msun >6]. Our model…
Brown dwarfs can serve as both clocks and chemical tracers of the evolutionary history of the Milky Way due to their continuous cooling and high sensitivity of spectra to composition. We focus on brown dwarfs in globular clusters that host…
The launch of JWST has ushered in a new era of high precision infrared astronomy, allowing us to probe nearby white dwarfs for cold dust, exoplanets, and tidally heated exomoons. While previous searches for these exoplanets have…
The performance of neural network models is often limited by the availability of big data sets. To treat this problem, we survey and develop novel synthetic data generation and augmentation techniques for enhancing low/zero-sample learning…
As the James Webb Space Telescope (JWST) has become fully operational, early-release data are now available to begin building the tools and calibrations for precision point-source photometry and astrometry in crowded cluster environments.…
We show that the James Webb Space Telescope will be able to detect dwarf satellites of high-$z$ Lyman Break Galaxies (LBGs). To this aim, we use cosmological simulations following the evolution of a typical $M_\star\simeq10^{10}\rm M_\odot$…
Observed Young Stellar Objects (YSOs) are used to study star formation and characterize star forming regions. For this purpose, YSO candidate catalogs are compiled from various surveys, especially in the infrared (IR), and simple selection…
We present an investigation of evolved stars in the nearby star-forming galaxy WLM, using NIRCam imaging from the JWST resolved stellar populations early-release science (ERS) program. We find that various combinations of the F090W, F150W,…
We present an early analysis on the search for high redshift galaxies using the deepest public JWST imaging to date, the NGDEEP field. This data consists of 6-band NIRCam imaging on the Hubble Ultra Deep Field-Par2, covering a total area of…
Real-world object detection must operate in evolving environments where new classes emerge, domains shift, and unseen objects must be identified as "unknown": all without accessing prior data. We introduce Evolving World Object Detection…
We present a new non-parametric method to quantify morphologies of galaxies based on a particular family of learning machines called support vector machines. The method, that can be seen as a generalization of the classical CAS…
Young associations in star forming regions are stellar systems that allow us to understand the mechanisms that characterise the stars in their early life and what happens around them. In particular, the analysis of the disks and of the…
The information recoverable from galaxy spectra depends fundamentally on spectral resolution, yet assembling large samples at high resolution remains observationally expensive. We present a deep-learning framework for spectral…
We apply a combination of a Genetic Algorithms (GA) and Support Vector Machines (SVM) machine learning algorithm to solve two important problems faced by the astronomical community: star/galaxy separation, and photometric redshift…
High-dimensional data classification is a fundamental task in machine learning and imaging science. In this paper, we propose a two-stage multiphase semi-supervised classification method for classifying high-dimensional data and…