524 research outputs found
Astroconformer: The Prospects of Analyzing Stellar Light Curves with Transformer-Based Deep Learning Models
Light curves of stars encapsulate a wealth of information about stellar
oscillations and granulation, thereby offering key insights into the internal
structure and evolutionary state of stars. Conventional asteroseismic
techniques have been largely confined to power spectral analysis, neglecting
the valuable phase information contained within light curves. While recent
machine learning applications in asteroseismology utilizing Convolutional
Neural Networks (CNNs) have successfully inferred stellar attributes from light
curves, they are often limited by the local feature extraction inherent in
convolutional operations. To circumvent these constraints, we present
, a Transformer-based deep learning framework designed
to capture long-range dependencies in stellar light curves. Our empirical
analysis, which focuses on estimating surface gravity (), is grounded
in a carefully curated dataset derived from light curves.
These light curves feature asteroseismic values spanning from 0.2 to
4.4. Our results underscore that, in the regime where the training data is
abundant, attains a root-mean-square-error (RMSE) of
0.017 dex around . Even in regions where training data are
sparse, the RMSE can reach 0.1 dex. It outperforms not only the K-nearest
neighbor-based model () but also state-of-the-art CNNs.
Ablation studies confirm that the efficacy of the models in this particular
task is strongly influenced by the size of their receptive fields, with larger
receptive fields correlating with enhanced performance. Moreover, we find that
the attention mechanisms within are well-aligned with
the inherent characteristics of stellar oscillations and granulation present in
the light curves.Comment: 13 pages, 9 figures, Submitted to MNRA
Complexity Heliophysics: A lived and living history of systems and complexity science in Heliophysics
In this piece we study complexity science in the context of Heliophysics,
describing it not as a discipline, but as a paradigm. In the context of
Heliophysics, complexity science is the study of a star, interplanetary
environment, magnetosphere, upper and terrestrial atmospheres, and planetary
surface as interacting subsystems. Complexity science studies entities in a
system (e.g., electrons in an atom, planets in a solar system, individuals in a
society) and their interactions, and is the nature of what emerges from these
interactions. It is a paradigm that employs systems approaches and is
inherently multi- and cross-scale. Heliophysics processes span at least 15
orders of magnitude in space and another 15 in time, and its reaches go well
beyond our own solar system and Earth's space environment to touch planetary,
exoplanetary, and astrophysical domains. It is an uncommon domain within which
to explore complexity science.
After first outlining the dimensions of complexity science, the review
proceeds in three epochal parts: 1) A pivotal year in the Complexity
Heliophysics paradigm: 1996; 2) The transitional years that established
foundations of the paradigm (1996-2010); and 3) The emergent literature largely
beyond 2010.
This review article excavates the lived and living history of complexity
science in Heliophysics. The intention is to provide inspiration, help
researchers think more coherently about ideas of complexity science in
Heliophysics, and guide future research. It will be instructive to Heliophysics
researchers, but also to any reader interested in or hoping to advance the
frontier of systems and complexity science
Portuguese SKA white book
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Medical Informatics and Data Analysis
During recent years, the use of advanced data analysis methods has increased in clinical and epidemiological research. This book emphasizes the practical aspects of new data analysis methods, and provides insight into new challenges in biostatistics, epidemiology, health sciences, dentistry, and clinical medicine. This book provides a readable text, giving advice on the reporting of new data analytical methods and data presentation. The book consists of 13 articles. Each article is self-contained and may be read independently according to the needs of the reader. The book is essential reading for postgraduate students as well as researchers from medicine and other sciences where statistical data analysis plays a central role
Machine learning techniques for galaxy imagery and photometry
Doctor of PhilosophyDepartment of Computer ScienceMajor Professor Not ListedIn the past two decades, autonomous digital sky surveys have enabled significant advances in astronomy by collecting massive databases of imagery and other information. The quantity of data, coupled with the variety of scientific questions that require its analysis, makes manual analysis of these data impractical. To address this challenge, machine learning algorithms have been widely adopted for data analysis and product generation in astronomy. In this dissertation I examine the efficacy of machine learning algorithms such as deep convolutional neural networks, support vector machines, and vision transformers for the purpose of astronomical data analysis, with emphasize on extra-galactic objects. These include algorithms that can annotate large datasets of galaxy images, and their application to premier digital sky surveys such as Pan-STARRS. Specifically, I address the following research question: How effective are machine learning algorithms for annotating astronomical data, and what are the downsides of using these algorithms for this purpose? Namely, biases that are typical to machine learning systems can influence the annotations, which may consequently lead to false conclusions when applying statistical analysis to data annotated using such systems. These biases are often difficult to identify. Overall, this research highlights the importance of careful consideration of machine learning algorithms and their potential biases when applying them to astronomical data analysis. Our findings have broad implications for the use of machine learning in astronomy and other scientific domains, as they demonstrate the importance of addressing potential biases in machine learning systems to avoid erroneous scientific conclusions
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