Related papers: Machine Learning for Mini-EUSO Telescope Data Anal…
Machine learning (ML) tools such as encoder-decoder convolutional neural networks (CNN) can represent incredibly complex nonlinear functions which map between combinations of images and scalars. For example, CNNs can be used to map…
We present a machine learning (ML) pipeline to identify star clusters in the multi{color images of nearby galaxies, from observations obtained with the Hubble Space Telescope as part of the Treasury Project LEGUS (Legacy ExtraGalactic…
Machine learning (ML) refers to computer algorithms that predict a meaningful output or categorize complex systems based on a large amount of data. ML is applied in various areas including natural science, engineering, space exploration,…
Particle track reconstruction is the most computationally intensive process in nuclear physics experiments. Traditional algorithms use a combinatorial approach that exhaustively tests track measurements ("hits") to identify those that form…
We study a machine learning (ML) technique for refining images acquired during in situ observation using liquid-cell transmission electron microscopy (LC-TEM). Our model is constructed using a U-Net architecture and a ResNet encoder. For…
Deep neural networks represent the gold standard for image classification. However, they usually need large amounts of data to reach superior performance. In this work, we focus on image classification problems with a few labeled examples…
Machine learning techniques are utilised in several areas of astrophysical research today. This dissertation addresses the application of ML techniques to two classes of problems in astrophysics, namely, the analysis of individual…
Solar activity plays a quintessential role in influencing the interplanetary medium and space-weather around the Earth. Remote sensing instruments onboard heliophysics space missions provide a pool of information about the Sun's activity…
Photovoltaics (PV) are widely used to harvest solar energy, an important form of renewable energy. Photovoltaic arrays consist of multiple solar panels constructed from solar cells. Solar cells in the field are vulnerable to various…
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…
This paper pioneers the use of neural networks to provide a fast and automatic way to classify lightcurves in massive photometric datasets. As an example, we provide a working neural network that can distinguish microlensing lightcurves…
We investigate the extent to which supervised machine learning techniques can distinguish between neutron-star matter models using macroscopic and oscillation-related quantities derived from theoretical stellar configurations. Four…
The efficiency of using the YOLOV5 machine learning model for solving the problem of automatic de-tection and recognition of micro-objects in the marine environment is studied. Samples of microplankton and microplastics were prepared,…
Space Debris (SD) consist of non-operational artificial objects orbiting around the Earth, which could possibly damage space vehicles, such as the International Space Station (ISS) or other manned spacecrafts. The vast majority of such…
On-orbit proximity operations in space rendezvous, docking and debris removal require precise and robust 6D pose estimation under a wide range of lighting conditions and against highly textured background, i.e., the Earth. This paper…
As a part of NASA's Heliophysics System Observatory (HSO) fleet of satellites,the Solar Dynamics Observatory (SDO) has continuously monitored the Sun since2010. Ultraviolet (UV) and Extreme UV (EUV) instruments in orbit, such asSDO's…
Supervised classification of temporal sequences of astronomical images into meaningful transient astrophysical phenomena has been considered a hard problem because it requires the intervention of human experts. The classifier uses the…
Tiny object classification problem exists in many machine learning applications like medical imaging or remote sensing, where the object of interest usually occupies a small region of the whole image. It is challenging to design an…
Since their invention in the 1980s [1], optical tweezers have found a wide range of applications, from biophotonics and mechanobiology to microscopy and optomechanics [2, 3, 4, 5]. Simulations of the motion of microscopic particles held by…
Machine learning (ML) has emerged as a novel tool for generating large-scale land surface data in recent years. ML can learn the relationship between input and target, e.g. meteorological variables and in-situ soil moisture, and then…