Related papers: Deep Learning for Multi-Messenger Astrophysics: A …
The past decade has seen a remarkable series of advances in machine learning, and in particular deep learning approaches based on artificial neural networks, to improve our abilities to build more accurate systems across a broad range of…
Machine learning plays a crucial role in enhancing and accelerating the search for new fundamental physics. We review the state of machine learning methods and applications for new physics searches in the context of terrestrial high energy…
Massive multiple-input multiple-output (MIMO) systems are a main enabler of the excessive throughput requirements in 5G and future generation wireless networks as they can serve many users simultaneously with high spectral and energy…
The Time-Domain And MultiMessenger (TDAMM) Communications Science Analysis Group (TDAMMCommSAG) was formulated to describe the unique technical challenges of communicating rapidly to and from NASA astrophysics missions studying the most…
The success of deep learning algorithms generally depends on large-scale data, while humans appear to have inherent ability of knowledge transfer, by recognizing and applying relevant knowledge from previous learning experiences when…
In contemporary astronomy and astrophysics (A&A), the integration of high-performance computing (HPC), big data analytics, and artificial intelligence/machine learning (AI/ML) has become essential for advancing research across a wide range…
The Multimessenger Diversity Network (MDN), formed in 2018, extends the basic principle of multimessenger astronomy -- that working collaboratively with different approaches enhances understanding and enables previously impossible…
The application of machine learning (ML) methods to the analysis of astrophysical datasets is on the rise, particularly as the computing power and complex algorithms become more powerful and accessible. As the field of ML enjoys a…
Interference Management is a vast topic present in many disciplines. The majority of wireless standards suffer the drawback of interference intrusion and the network efficiency drop due to that. Traditionally, interference management has…
Machine-learning is playing an increasing role in helping the astronomical community to face data analysis challenges, in particular in the field of Galactic Archaeology and large scale spectroscopic surveys. We present recent developments…
The groundbreaking discoveries of gravitational waves from binary black-hole mergers and, most recently, coalescing neutron stars started a new era of Multi-Messenger Astrophysics and revolutionized our understanding of the Cosmos. Machine…
In recent years, machine learning (ML) methods have remarkably improved how cosmologists can interpret data. The next decade will bring new opportunities for data-driven cosmological discovery, but will also present new challenges for…
This contribution summarizes the main activities and objectives of the outreach project Astroaccesible, whose main aim is to carry the teaching and diffusion of astronomy among all kinds of collectives, focusing on blind and visually…
Deep learning has revolutionized biomedical research by providing sophisticated methods to handle complex, high-dimensional data. Multimodal deep learning (MDL) further enhances this capability by integrating diverse data types such as…
Effective routing in satellite mega-constellations has become crucial to facilitate the handling of increasing traffic loads, more complex network architectures, as well as the integration into 6G networks. To enhance adaptability as well…
Machine learning has rose to become an important research tool in the past decade, its application has been expanded to almost if not all disciplines known to mankind. Particularly, the use of machine learning in astrophysics research had a…
Astronomy is entering a new era as multiple, large area, digital sky surveys are in production. The resulting datasets are truly remarkable in their own right; however, a revolutionary step arises in the aggregation of complimentary…
Data analysis in space sciences has been performed exclusively visually for years, despite the fact that the largest amount of data belongs to non-visible portions of the electromagnetic spectrum. This, on the one hand, limits the study of…
These notes were originally prepared as additional material for the lessons I have given at the summer school Gamma-ray Astrophysics and Multifrequency: Data analysis and astroparticle problems, organized by the Department of Physics of the…
This article is based on the tutorial we gave at the hands-on workshop of the ICRANet-ISFAHAN Astronomy Meeting. We first introduce the basic theory of machine learning and sort out the whole process of training a neural network. We then…