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(PDF) Rare Object Search from Low-S/N Stellar Spectra in SDSS
Source: researchgate.net
Article summary:
1. This article proposes a novel method for searching for rare objects from low-S/N spectra using PCA (Principal Components Analysis) and CFSFDP (Clustering by Fast Search and Find of Density Peak).
2. The proposed method is applied to spectra in SDSS stellar classification template library with added white gaussian noise to search for rare objects such as carbon stars, carbon white dwarfs, carbon_lines, white dwarfs and white dwarfs magnetic.
3. Experimental results show that the proposed method has a higher efficiency compared to other methods such as Lick-index+K-means and Support Vector Machines (SVM).
Article analysis:
This article presents a novel method for searching for rare objects from low-S/N spectra using PCA (Principal Components Analysis) and CFSFDP (Clustering by Fast Search and Find of Density Peak). The authors provide evidence that their proposed method is more efficient than other methods such as Lick-index+K-means and Support Vector Machines (SVM). However, there are some potential biases in the article that should be noted.
First, the authors do not provide any evidence or discussion on the potential risks associated with their proposed method. It is unclear if there are any potential risks associated with using this method, such as false positives or false negatives. Additionally, the authors do not discuss any possible counterarguments or alternative approaches to their proposed method.
Second, the authors do not present both sides of the argument equally. They focus solely on presenting their own approach without exploring any alternatives or discussing any potential drawbacks of their approach. Furthermore, they do not provide any evidence to support their claims about the efficacy of their approach compared to other methods.
Finally, it is unclear if the authors have considered all relevant points of consideration when developing their approach. For example, they do not discuss how robust their approach is when dealing with different types of data or how well it performs in different scenarios. Additionally, they do not discuss how well it performs when dealing with noisy data or outliers in the data set.
In conclusion, while this article presents an interesting approach for searching for rare objects from low-S/N spectra using PCA and CFSFDP, there are some potential biases that should be noted before relying on its conclusions.