Dictionary based encoding of cosmological images

Gauci, Adam; Abela, John; Cachia, Ernest; Adami, Kristian Zarb

doi:10.1109/iceaa.2014.6903864

Cited by 4 publications

(6 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This method has a marginal but encouraging higher accuracy among all four methods that we have tested. Gauci et al (2010) performed a comparison of different classification tree algorithms to a data set of 75000 objects from the SDSS previously classified by the Galaxy Zoo project. The algorithms of CART, C4.5 and Random Forest (RF) are tested with a ten-fold cross validation technique where, in each run, nine subsets of the data are used for training and one for testing.…”

Section: Discussionmentioning

confidence: 99%

“…Huertas-Company et al (2007) offered a generalisation of the CAS method using SVM. Other examples from literature where a statistical learning technique was used to classify galaxies include Banerji et al (2010) (artificial neural networks), Owens et al (1996) (oblique decision trees) and Gauci et al (2010) (three decision tree algorithms including a random forest approach). All these methods use measured parameters as inputs to the classifying algorithms.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Galaxy And Mass Assembly: automatic morphological classification of galaxies using statistical learning

Sreejith

Pereverzyev

Kelvin

et al. 2017

Monthly Notices of the Royal Astronomical Society

View full text Add to dashboard Cite

We apply four statistical learning methods to a sample of 7941 galaxies (z < 0.06) from the Galaxy and Mass Assembly (GAMA) survey to test the feasibility of using automated algorithms to classify galaxies. Using 10 features measured for each galaxy (sizes, colours, shape parameters & stellar mass) we apply the techniques of Support Vector Machines (SVM), Classification Trees (CT), Classification Trees with Random Forest (CTRF) and Neural Networks (NN), returning True Prediction Ratios (TPRs) of 75.8%, 69.0%, 76.2% and 76.0% respectively. Those occasions whereby all four algorithms agree with each other yet disagree with the visual classification ('unanimous disagreement') serves as a potential indicator of human error in classification, occurring in ∼ 9% of ellipticals, ∼ 9% of Little Blue Spheroids, ∼ 14% of early-type spirals, ∼ 21% of intermediate-type spirals and ∼ 4% of late-type spirals & irregulars. We observe that the choice of parameters rather than that of algorithms is more crucial in determining classification accuracy. Due to its simplicity in formulation and implementation, we recommend the CTRF algorithm for classifying future galaxy datasets. Adopting the CTRF algorithm, the TPRs of the 5 galaxy types are : E, 70.1%; LBS, 75.6%; S0-Sa, 63.6%; Sab-Scd, 56.4% and Sd-Irr, 88.9%. Further, we train a binary classifier using this CTRF algorithm that divides galaxies into spheroid-dominated (E, LBS & S0-Sa) and disk-dominated (Sab-Scd & Sd-Irr), achieving an overall accuracy of 89.8%. This translates into an accuracy of 84.9% for spheroid-dominated systems and 92.5% for disk-dominated systems. c 0000 The Authors arXiv:1711.06125v2 [astro-ph.GA] 17 Nov 2017 2 S.Sreejith et al.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Galaxy And Mass Assembly: automatic morphological classification of galaxies using statistical learning

Sreejith

Pereverzyev

Kelvin

et al. 2017

Monthly Notices of the Royal Astronomical Society

View full text Add to dashboard Cite

show abstract

“…With the advent of large high quality survey data like from the Sloan Digital Sky Survey (York & SDSS Collaboration 2000) and CANDELS (Grogin et al 2011), we are beginning to see more machine learning guillecabrera@udec.cl a https://github.com/guille-c/labeling bias morphologically classified galaxy data sets using a variety of methods (e.g. Ball et al 2004;Scarlata et al 2007;Tasca et al 2009;Gauci et al 2010;Huertas-Company et al 2011;Dieleman et al 2015;. Many of the current machine learning classification techniques fall into the category of supervised learning and thus require training data sets, usually based on visually classified morphologies.…”

Section: Introductionmentioning

confidence: 99%

Systematic Labeling Bias in Galaxy Morphologies

Cabrera-Vives

Miller

Schneider

2018

View full text Add to dashboard Cite

We present a metric to quantify systematic labeling bias in galaxy morphology data sets stemming from the quality of the labeled data. This labeling bias is independent from labeling errors and requires knowledge about the intrinsic properties of the data with respect to the observed properties. We conduct a relative comparison of label bias for different low redshift galaxy morphology data sets. We show our metric is able to recover previous de-biasing procedures based on redshift as biasing parameter. By using the image resolution instead, we find biases that have not been addressed. We find that the morphologies based on supervised machine-learning trained over features such as colors, shape, and concentration show significantly less bias than morphologies based on expert or citizenscience classifiers. This result holds even when there is underlying bias present in the training sets used in the supervised machine learning process. We use catalog simulations to validate our bias metric, and show how to bin the multidimensional intrinsic and observed galaxy properties used in the bias quantification. Our approach is designed to work on any other labeled multidimensional data sets and the code is publicly available a .

show abstract

“…Machine learning algorithms have been widely used to classify galaxy morphology in the past years, for instance, Artificial Neural Network (Naim et al 1995), NN + local weighted regression (De la Calleja & Fuentes 2004), Random Forest (Gauci et al 2010), linear discriminant analysis (LDA, (Ferrari et al 2015)).…”

Section: First Authormentioning

confidence: 99%

Classifying Galaxy Morphologies with Few-Shot Learning

Zhang,

Zou,

et al. 2022

Preprint

View full text Add to dashboard Cite

The taxonomy of galaxy morphology is critical in astrophysics as the morphological properties are powerful tracers of galaxy evolution. With the upcoming Large-scale Imaging Surveys, billions of galaxy images challenge astronomers to accomplish the classification task by applying traditional methods or human inspection. Consequently, machine learning, in particular supervised deep learning, has been widely employed to classify galaxy morphologies recently due to its exceptional automation, efficiency, and accuracy. However, supervised deep learning requires extensive training sets, which causes considerable workloads; also, the results are strongly dependent on the characteristics of training sets, which leads to biased outcomes potentially. In this study, we attempt Few-shot Learning to bypass the two issues. Our research adopts the dataset from Galaxy Zoo Challenge Project on Kaggle, and we divide it into five categories according to the corresponding truth table. By classifying the above dataset utilizing few-shot learning based on Siamese Networks and supervised deep learning based on AlexNet, VGG 16, and ResNet 50 trained with different volumes of training sets separately, we find that few-shot learning achieves the highest accuracy in most cases, and the most significant improvement is 21% compared to AlexNet when the training sets contain 1000 images. In addition, to guarantee the accuracy is no less than 90%, few-shot learning needs ∼6300 images for training, while ResNet 50 requires ∼13000 images. Considering the advantages stated above, foreseeably, few-shot learning is suitable for the taxonomy of galaxy morphology and even for

show abstract

Dictionary based encoding of cosmological images

Cited by 4 publications

References 4 publications

Galaxy And Mass Assembly: automatic morphological classification of galaxies using statistical learning

Galaxy And Mass Assembly: automatic morphological classification of galaxies using statistical learning

Systematic Labeling Bias in Galaxy Morphologies

Classifying Galaxy Morphologies with Few-Shot Learning

Contact Info

Product

Resources

About