NeuroStarMap: Neural Network encoding of Gaia’s distance ladder

IF 1.8 4区物理与天体物理 Q2 ASTRONOMY & ASTROPHYSICS

Astronomy and Computing Pub Date : 2026-04-01 Epub Date: 2025-12-30 DOI:10.1016/j.ascom.2025.101056

L. Brolli , C. Fruncillo , S. Zimotti , S. Tortora , L. Maina , A. Petrone , M. Gai , D. Busonero

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引用次数: 0

Abstract

NeuroStarMap aims at providing Neural Network (NN) tools for access to the Gaia catalogue source classes supporting the cosmic distance ladder materialization, namely Cepheids, RR Lyrae and eclipsing binaries. The tools are trained, tested and validated on Gaia DR3 objects, and are expected to be compatible (via update and upgrade) with the forthcoming DR4 and DR5 catalogue releases. The practical goal is the implementation of tools fed by suitable photometric and variability data, able to provide adequate estimate of the target distance, through its proxy, i.e. parallax, consistently with the direct Gaia determination. We discuss the available dataset characteristics, the filtering and pre-processing applied to ensure proper neural encoding, the NN model selection and the current status of dataset fitting. The proposed solution, labeled ParallaxPredictorMXL, is a heterogeneous combination of simpler regression models, providing the best match to the complex dataset information structure.

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神经星图：盖亚距离阶梯的神经网络编码

NeuroStarMap旨在提供神经网络（NN）工具，以访问支持宇宙距离阶梯物化的盖亚目录源类，即造父变星，RR天琴座和食双星。这些工具在Gaia DR3对象上进行了培训、测试和验证，预计将与即将发布的DR4和DR5目录版本兼容（通过更新和升级）。实际目标是实现由适当的光度和变异性数据提供的工具，能够通过其代理（即视差）提供与盖亚直接测定一致的目标距离的充分估计。我们讨论了可用的数据集特征、用于确保适当神经编码的滤波和预处理、神经网络模型的选择以及数据集拟合的现状。提出的解决方案，标记为ParallaxPredictorMXL，是简单回归模型的异构组合，为复杂的数据集信息结构提供了最佳匹配。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Astronomy and Computing ASTRONOMY & ASTROPHYSICSCOMPUTER SCIENCE,-COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

CiteScore

4.10

自引率

8.00%

发文量

期刊介绍： Astronomy and Computing is a peer-reviewed journal that focuses on the broad area between astronomy, computer science and information technology. The journal aims to publish the work of scientists and (software) engineers in all aspects of astronomical computing, including the collection, analysis, reduction, visualisation, preservation and dissemination of data, and the development of astronomical software and simulations. The journal covers applications for academic computer science techniques to astronomy, as well as novel applications of information technologies within astronomy.