Related papers: Assessing the Performance of a Machine Learning Al…
We investigate star-galaxy classification for astronomical surveys in the context of four methods enabling the interpretation of black-box machine learning systems. The first is outputting and exploring the decision boundaries as given by…
The bulk of the star-formation rate density peak at cosmic noon was obscured by dust. How accurately we can assess the role of dust obscured star-formation is affected by inherent biases in our empirical methods -- both those that rely on…
Currently available star cluster catalogues from HST imaging of nearby galaxies heavily rely on visual inspection and classification of candidate clusters. The time-consuming nature of this process has limited the production of reliable…
Pebble accretion is an emerging paradigm for the fast growth of planetary cores. Pebble flux and pebble sizes are the key parameters used in the pebble accretion models. We aim to derive the pebble sizes and fluxes from state-of-the-art…
Magnetic fields (B-fields) are ubiquitous in the interstellar medium (ISM), and they play an essential role in the formation of molecular clouds and subsequent star formation. However, B-fields in interstellar environments remain…
Stellar flybys are a common dynamical process in young stellar clusters and can significantly reshape protoplanetary discs. However, their impact on dust dynamics remains poorly understood, particularly in the weakly coupled regime…
Astronomy has entered the multi-messenger data era and Machine Learning has found widespread use in a large variety of applications. The exploitation of synoptic (multi-band and multi-epoch) surveys, like LSST (Legacy Survey of Space and…
Dust is a crucial component of the interstellar medium of galaxies. The presence of dust strongly affects the light produced by stars within a galaxy. As these photons are our main information vector to explore the stellar mass assembly and…
The characterization of the dust polarization foreground to the Cosmic Microwave Background (CMB) is a necessary step towards the detection of the B-mode signal associated with primordial gravitational waves. We present a method to simulate…
Numerical models of the wind-blown bubble of massive stars usually only account for the wind of a single star. However, since massive stars are usually formed in clusters, it would be more realistic to follow the evolution of a bubble…
(abridged) The dust content of the universe is primarily explored via its interaction with stellar photons, producing interstellar extinction. However, owing to the physical extension of the observing beam, observations may detect scattered…
The dust attenuation for a sample of $\sim$10000 local ($z\lesssim0.1$) star forming galaxies is constrained as a function of their physical properties. We utilize aperture-matched multi-wavelength data available from the Galaxy Evolution…
Wave breaking is an important process for energy dissipation in the open ocean and coastal seas. It drives beach morphodynamics, controls air-sea interactions, determines when ship and offshore structure operations can occur safely, and…
We present a novel approach for classifying stars as binary or exoplanet using deep learning techniques. Our method utilizes feature extraction, wavelet transformation, and a neural network on the light curves of stars to achieve…
We present a numerical study of the evolution of molecular clouds, from their formation by converging flows in the warm ISM, to their destruction by the ionizing feedback of the massive stars they form. We improve with respect to our…
Stars form within molecular clouds but our understanding of this fundamental process remains hampered by the complexity of the physics that drives their evolution. We review our observational and theoretical knowledge of molecular clouds…
One of the most remarkable outcomes from \textit{JWST} has been the exquisite UV-optical spectroscopic data for galaxies in the high-redshift Universe ($z \geq 5$), enabling the use of various nebular emission lines to infer conditions of…
Unsupervised machine learning is widely used to mine large, unlabeled datasets to make data-driven discoveries in critical domains such as climate science, biomedicine, astronomy, chemistry, and more. However, despite its widespread…
Large dust grains in thermal equilibrium dominate the far-infrared and contribute to the millimetre continuum of star-forming galaxies, but constraining their properties is difficult due to free-free and synchrotron contamination. We study…
We present an application of a particular machine-learning method (Boosted Decision Trees, BDTs using AdaBoost) to separate stars and galaxies in photometric images using their catalog characteristics. BDTs are a well established machine…