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Multi-wavelength imaging polarimetry at far-infrared wavelengths has proven to be an excellent tool for studying the physical properties of dust, molecular clouds, and magnetic fields in the interstellar medium. Although these wavelengths…

Efficient multi-hop reasoning requires Large Language Models (LLMs) based agents to acquire high-value external knowledge iteratively. Previous work has explored reinforcement learning (RL) to train LLMs to perform search-based document…

Retrieval-Augmented Generation (RAG) has emerged as a powerful framework to improve factuality in large language models (LLMs) by grounding their outputs in retrieved documents. However, ensuring perfect retrieval of relevant information…

Retrieval-Augmented Generation (RAG) couples a retriever with a large language model (LLM) to ground generated responses in external evidence. While this framework enhances factuality and domain adaptability, it faces a key bottleneck:…

Photometric redshifts are necessary for enabling large-scale multicolour galaxy surveys to interpret their data and constrain cosmological parameters. While the increased depth of future surveys such as the Large Synoptic Survey Telescope…

This article presents Holistically-Attracted Wireframe Parsing (HAWP), a method for geometric analysis of 2D images containing wireframes formed by line segments and junctions. HAWP utilizes a parsimonious Holistic Attraction (HAT) field…

There are two common ways in which developers are incorporating proprietary and domain-specific data when building applications of Large Language Models (LLMs): Retrieval-Augmented Generation (RAG) and Fine-Tuning. RAG augments the prompt…

Resource-intensive computations are a major factor that limits the effectiveness of automated machine learning solutions. In the paper, we propose a modular approach that can be used to increase the quality of evolutionary optimization for…

Retrieval-Augmented Generation (RAG) has recently emerged as a method to extend beyond the pre-trained knowledge of Large Language Models by augmenting the original prompt with relevant passages or documents retrieved by an Information…

Worldwide image geolocalization, which aims to predict the GPS coordinates of any image on Earth, remains challenging due to global visual diversity. Recent generative approaches based on Retrieval-Augmented Generation (RAG) and Large…

Flux estimates for faint sources or transients are systematically biased high because there are far more truly faint sources than bright. Corrections which account for this effect are presented as a function of signal-to-noise ratio and the…

Aims. To describe two potential options for the Source Environment Analysis pipeline, SEAPipe, for the Gaia mission. This pipeline will enable the discovery of sources which are new to Gaia, in the sense that they were not found by the…

Large Language Models (LLMs) excel in various language tasks but they often generate incorrect information, a phenomenon known as "hallucinations". Retrieval-Augmented Generation (RAG) aims to mitigate this by using document retrieval for…

The Fermi Large Area Telescope (LAT) Collaboration has recently released the Third Catalog of Hard Fermi-LAT Sources (3FHL), which contains 1556 sources detected above 10 GeV with seven years of Pass 8 data. We investigate the source count…

In recent years the importance of finding a meaningful pattern from huge datasets has become more challenging. Data miners try to adopt innovative methods to face this problem by applying feature selection methods. In this paper we propose…

Large Language Models (LLMs) demonstrate potential in the field of scientific idea generation. However, the generated results often lack controllable academic context and traceable inspiration pathways. To bridge this gap, this paper…

The limited angular resolution of gamma-ray telescopes prevents the straight identification of the majority of the sources detected so far. While the only certified galactic gamma-ray emitters are associated with pulsars, more than 90% of…

One of many ways for the James-Webb Space Telescope (JWST) to capture astronomical signals is the Mid-Infrared Instrument (MIRI) Imaging mode. To make this data ready for analysis, the JWST standard reduction pipeline has three stages and…

We report an extension of the source imaging method for imaging full three-dimensional sources from three-dimensional like-pair correlations. Our technique consists of expanding the correlation data and the underlying source function in…

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…