Related papers: Danger Zone: Establishing Buffers for Enhanced Cla…

In this work, Machine Learning (ML) methods are used to efficiently identify the unassociated sources and the Blazar Candidate of Uncertain types (BCUs) in the Fermi-LAT Third Source Catalog (3FGL). The aims are twofold: 1) to distinguish…

The observed faintness of infrared fine-structure line emission along with the warm far-infrared (FIR) colors of ultraluminous infrared galaxies (ULIRGs) is a long-standing problem. In this work, we calculate the line and continuum…

The latest $\textit{Fermi}$-LAT gamma-ray catalog, 4FGL-DR3, presents a large fraction of sources without clear association to known counterparts, i.e., unidentified sources (unIDs). In this paper, we aim to classify them using machine…

This paper proposes a novel semi-supervised method on object recognition. First, based on Boost Picking, a universal algorithm, Boost Picking Teaching (BPT), is proposed to train an effective binary-classifier just using a few labeled data…

Medical image segmentation is usually regarded as one of the most important intermediate steps in clinical situations and medical imaging research. Thus, accurately assessing the segmentation quality of the automatically generated…

Convolutional neural networks (CNN) have become one of the most popular machine learning tools and are being applied in various tasks, however, CNN models are vulnerable to universal perturbations, which are usually human-imperceptible but…

Using longslit, optical spectra of Ultraluminous Infrared Galaxies (ULIRGs), we measure the evolution in the star-formation intensity during galactic mergers. In individual galaxies, we resolve kpc scales allowing comparison of the nucleus,…

The identification of cancer genes is a critical yet challenging problem in cancer genomics research. Existing computational methods, including deep graph neural networks, fail to exploit the multilayered gene-gene interactions or provide…

Line intensity mapping is emerging as a novel method that can measure the collective intensity fluctuations of atomic/molecular line emission from distant galaxies. Several observational programs with various wavelengths are ongoing and…

Unsupervised deep metric learning (UDML) focuses on learning a semantic representation space using only unlabeled data. This challenging problem requires accurately estimating the similarity between data points, which is used to supervise a…

In order to develop a pipeline for automated classification of stars to be observed by the TAUVEX ultraviolet space Telescope, we employ an artificial neural network (ANN) technique for classifying stars by using synthetic spectra in the UV…

We present an optical spectroscopic study of a 90% complete sample of 17 nearby ULIRGs with optical Seyfert nuclei, with the aim of investigating the nature of the nuclear warm gas outflows. A high proportion (94%) of our sample show…

Deep Generative Networks (DGNs) are extensively employed in Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and their variants to approximate the data manifold and distribution. However, training samples are often…

In the first two papers of this series (Rhea et al. 2020; Rhea et al. 2021), we demonstrated the dynamism of machine learning applied to optical spectral analysis by using neural networks to extract kinematic parameters and emission-line…

Future astrophysical surveys such as J-PAS will produce very large datasets, which will require the deployment of accurate and efficient Machine Learning (ML) methods. In this work, we analyze the miniJPAS survey, which observed about 1…

The Fermi gamma-ray space telescope has revolutionized our view of the gamma-ray sky and the high energy processes in the Universe. While the number of known gamma-ray emitters has increased by orders of magnitude since the launch of Fermi,…

A large fraction of Fermi-Large Area Telescope (LAT) sources in the fourth Fermi-LAT 14 yr catalog (4FGL) still remain unidentified (unIDed). We continued to improve our machine-learning pipeline and used it to classify 1206 X-ray sources…

Image classification is often prone to labelling uncertainty. To generate suitable training data, images are labelled according to evaluations of human experts. This can result in ambiguities, which will affect subsequent models. In this…

Unsupervised Domain Adaptation for Regression (UDAR) aims to adapt models from a labeled source domain to an unlabeled target domain for regression tasks. Traditional feature alignment methods, successful in classification, often prove…

We present an application of unsupervised Machine Learning Clustering to the PAU Survey of galaxy spectral energy distribution (SED) within the COSMOS field. The clustering algorithm is implemented and optimized to get the relevant groups…