The Tidal Tails of Globular Cluster Palomar 5 Based on Neural Networks Method
H. Zou, Z. -Y. Wu, J. Ma, X. Zhou

TL;DR
This paper employs neural networks on SDSS data to identify and analyze the tidal tails of globular cluster Palomar 5, revealing detailed tail structures and substructures, and confirming their origin from the cluster.
Contribution
It introduces a neural network-based method to detect and map tidal tails of Palomar 5 using SDSS data, providing detailed structural insights.
Findings
Tails extend 5.42° north and 3.77° south of Palomar 5.
Detected substructures within the tidal tails.
Tidal radius of Palomar 5 is 14.28 arcminutes.
Abstract
The Sixth Data Release (DR6) in the Sloan Digital Sky Survey (SDSS) provides more photometric regions, new features and more accurate data around globular cluster Palomar 5. A new method, Back Propagation Neural Network (BPNN), is used to estimate the probability of cluster member to detect its tidal tails. Cluster and field stars, used for training the networks, are extracted over a deg field by color-magnitude diagrams (CMDs). The best BPNNs with two hidden layers and Levenberg-Marquardt (LM) training algorithm are determined by the chosen cluster and field samples. The membership probabilities of stars in the whole field are obtained with the BPNNs, and contour maps of the probability distribution show that a tail extends to the north of the cluster and a tail extends to the south. The whole tails are similar to those detected by \citet{od03}, but…
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