- Machine learning in APOGEE🔍
- Identification of stellar populations through chemical abundances🔍
- Machine learning in APOGEE. Unsupervised spectral classification ...🔍
- The regression of effective temperatures in APOGEE and LAMOST🔍
- An application of deep learning in the analysis of stellar spectra🔍
- Data Science & Analysis🔍
Machine learning in APOGEE
Machine learning in APOGEE - Unsupervised spectral classification ...
Our research applies an unsupervised classification scheme based on K-means to the massive APOGEE data set. We explore whether the data are amenable to ...
Machine learning in APOGEE - Identification of stellar populations ...
We explore the possibility of using unsupervised clustering algorithms to separate stellar populations with distinct chemical patterns.
Machine learning in APOGEE: Unsupervised spectral classification ...
Title:Machine learning in APOGEE: Unsupervised spectral classification with K-means ... Abstract:The data volume generated by astronomical surveys ...
Identification of stellar populations through chemical abundances
Astrophysics > Instrumentation and Methods for Astrophysics · Title:Machine learning in APOGEE: Identification of stellar populations through ...
Identification of stellar populations through chemical abundances
Machine learning in APOGEE: Identification of stellar populations through chemical abundances · R. Garcia-Dias, C. Prieto, +1 author. P. Palicio · Published in ...
APOGEE 2: multi-layer machine-learning model for the interpretable ...
APOGEE 2 is a machine-learning tool for assessing the fragility of the mitochondrial genome, evaluating genetic variant pathogenicity and ...
Machine learning in APOGEE. Unsupervised spectral classification ...
Context. The volume of data generated by astronomical surveys is growing rapidly. Traditional analysis techniques in spectroscopy either demand intensive ...
Machine learning in APOGEE: Unsupervised spectral classification ...
To give a few examples, Garcia-Dias et al. (2018) used K-Means unsupervised clustering to classify over 150,000 spectra. Reis et al. (2019) developed a data ...
Machine learning in APOGEE: Unsupervised spectral - ProQuest
Machine learning in APOGEE: Unsupervised spectral classification with K K -means. Garcia-Dias, Rafael; Carlos Allende Prieto; Jorge Sánchez Almeida; Ordovás ...
APOGEE 2: multi-layer machine-learning model for the interpretable ...
APOGEE 2 is a mitochondrially-centered ensemble method designed to improve the accuracy of pathogenicity predictions for interpreting missense ...
Identification of stellar populations through chemical abundances
Machine learning in APOGEE. Identification of stellar populations through chemical abundances ... Context. The vast volume of data generated by ...
Machine learning in APOGEE - OUCI
In this scenario, machine learning, and unsupervised clustering algorithms in particular, offer interesting alternatives. The Apache Point Observatory Galactic ...
Machine learning in APOGEE - OUCI
Context. The vast volume of data generated by modern astronomical surveys offers test beds for the application of machine-learning.
Machine learning in APOGEE - DIGITAL.CSIC
In this scenario, machine learning, and unsupervised clustering algorithms in particular, offer interesting alternatives. The Apache Point Observatory Galactic ...
APOGEE Net: Improving the Derived Spectral Parameters for Young ...
APOGEE Net: Improving the Derived Spectral Parameters for Young Stars through Deep Learning, Richard Olney, Marina Kounkel, Chad Schillinger, ...
APOGEE Net: Improving the Derived Spectral Parameters for You..
The viability of machine-learning approaches has been demonstrated for spectra covering a variety of wavelengths and spectral resolutions, but most often for ...
The regression of effective temperatures in APOGEE and LAMOST
When the NUV-u, u-g, g-r, r-i, i-J, J-H, H-K, K-WISE_4_5 magnitudes are used as machine learning features, the coefficient of determination of regression are ...
APOGEE 2: multi-layer machine-learning model for the interpretable ...
Mitochondrial dysfunction has pleiotropic effects and is frequently caused by mitochondrial DNA mutations. However, factors such as significant variability ...
An application of deep learning in the analysis of stellar spectra
StarNet can also predict stellar parameters when trained on synthetic data, with excellent precision and accuracy for both APOGEE data and synthetic data, over ...
Data Science & Analysis | Apogee Integration
... APOGEE teams to focus on discovering the knowledge opportunities hidden ... machine learning, and data visualizations. *( p > 0.05 ) To Insights & Beyond!