5,620 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
Quantum Key Distribution (QKD) over Software-Defined Optical Networks
Optical network security is attracting increasing research interest. Currently, software-defined optical network (SDON) has been proposed to increase network intelligence (e.g., flexibility and programmability) which is gradually moving toward industrialization. However, a variety of new threats are emerging in SDONs. Data encryption is an effective way to secure communications in SDONs. However, classical key distribution methods based on the mathematical complexity will suffer from increasing computational power and attack algorithms in the near future. Noticeably, quantum key distribution (QKD) is now being considered as a secure mechanism to provision information-theoretically secure secret keys for data encryption, which is a potential technique to protect communications from security attacks in SDONs. This chapter introduces the basic principles and enabling technologies of QKD. Based on the QKD enabling technologies, an architecture of QKD over SDONs is presented. Resource allocation problem is elaborated in detail and is classified into wavelength allocation, time-slot allocation, and secret key allocation problems in QKD over SDONs. Some open issues and challenges such as survivability, cost optimization, and key on demand (KoD) for QKD over SDONs are discussed
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