Related papers: Protostellar classification using supervised machi…
The KiDS Strongly lensed QUAsar Detection project (KiDS-SQuaD) aims at finding as many previously undiscovered gravitational lensed quasars as possible in the Kilo Degree Survey. This is the second paper of this series where we present a…
We analyze near-infrared UKIDSS observations of a sample of 8325 objects taken from a catalog of intrinsically red sources in the Galactic plane selected in the Spitzer-GLIMPSE survey. Given the differences in angular resolution (factor >2…
Young massive stars influence their surroundings from local to galactic scales, but the observational challenges associated with their distance and embedded nature has, until the recent decade, made high-resolution studies of these objects…
We present the first results of our dedicated programme of automatised classification of galaxies, stars and quasars in the mid-infrared all-sky data from the WISE survey. We employ the Support Vector Machines (SVM) algorithm, which defines…
Clustering is an unsupervised machine learning method grouping data samples into clusters of similar objects. In practice, clustering has been used in numerous applications such as banking customers profiling, document retrieval, image…
In this paper, we address the problem of detecting small, dense, and overlapping objects, a major challenge in computer vision. Our focus is on reviewing proposed methods based on deep learning supervised approaches. We provide a detailed…
We are in a golden era observing Young Stellar Objects (YSOs), protoplanetary disks, and substellar objects, crucial for understanding their formation and evolution. This Ph.D. thesis explores two binary systems. Firstly, we study an…
Concatenating data from the millimetre regime to the infrared, we have performed spectral energy distribution modelling for 227 of the 405 millimetre continuum sources of Hill et al. (2005) which are thought to contain young massive stars…
Organic semiconductors are promising materials for cheap, scalable and sustainable electronics, light-emitting diodes and photovoltaics. For organic photovoltaic cells, it is a challenge to find compounds with suitable properties in the…
We have developed a method for fast and accurate stellar population parameters determination in order to apply it to high resolution galaxy spectra. The method is based on an optimization technique that combines active learning with an…
The use of machine learning algorithms is an attractive way to produce very fast detector simulations for scattering reactions that can otherwise be computationally expensive. Here we develop a factorised approach where we deal with each…
Aims. We introduce a new deep learning tool that estimates stellar parameters (such as effective temperature, surface gravity, and extinction) of young low-mass stars by coupling the Phoenix stellar atmosphere model with a conditional…
Over the last two decades, machine learning models have been widely applied and have proven effective in classifying variable stars, particularly with the adoption of deep learning architectures such as convolutional neural networks,…
With the coming data deluge from synoptic surveys, there is a growing need for frameworks that can quickly and automatically produce calibrated classification probabilities for newly-observed variables based on a small number of time-series…
A significant degree of misclassification of variable stars through the application of machine learning methods to survey data motivates a search for more reliable and accurate machine learning procedures, especially in light of the very…
We present a study of the classification of z ~1 extremely red objects (EROs), using a combination of HST/ACS, Spitzer/IRAC, and ground-based images of the COSMOS field. Our sample includes about 5300 EROs with i-Ks>2.45 (AB, equivalently…
In this paper we investigate how implementing machine learning could improve the efficiency of the search for Trans-Neptunian Objects (TNOs) within Dark Energy Survey (DES) data when used alongside orbit fitting. The discovery of multiple…
X-ray emission is characteristic of young stellar objects (YSOs) and is known to be highly variable. We investigate, via an infrared and multi-epoch X-ray study of the L1630 dark cloud, whether and how X-ray variability in young stellar…
The observation of our home galaxy, the Milky Way (MW), is made difficult by our internal viewpoint. The Gaia survey that contains around 1.6 billion star distances is the new flagship of MW structure and can be combined with other…
Young Stellar Objects (YSO) are newly formed stars from molecular clouds. They stay close to where they were born and serve as good tracers to study gas and star formation. During cloud evolution, young massive stars can disrupt the…