Search Results for author:
Found 9 papers, 3 papers with code
Unsupervised Star Galaxy Classification with Cascade Variational Auto-Encoder
The increasing amount of data in astronomy provides great challenges for machine learning research.
Extended Isolation Forest
This extension, named Extended Isolation Forest (EIF), resolves issues with assignment of anomaly score to given data points.
Star-galaxy Classification Using Deep Convolutional Neural Networks
Most existing star-galaxy classifiers use the reduced summary information from catalogs, requiring careful feature extraction and selection.
Machine Learning and Cosmological Simulations II: Hydrodynamical Simulations
In this work, we show that ML is a promising technique to study galaxy formation in the backdrop of a hydrodynamical simulation.
Astrophysics of Galaxies Cosmology and Nongalactic Astrophysics
Machine Learning and Cosmological Simulations I: Semi-Analytical Models
We present a new exploratory framework to model galaxy formation and evolution in a hierarchical universe by using machine learning (ML).
Astrophysics of Galaxies Cosmology and Nongalactic Astrophysics
A Hybrid Ensemble Learning Approach to Star-Galaxy Classification
There exist a variety of star-galaxy classification techniques, each with their own strengths and weaknesses.
Instrumentation and Methods for Astrophysics
Data Mining and Machine Learning in Astronomy
We review the current state of data mining and machine learning in astronomy.
Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics
Robust Machine Learning Applied to Astronomical Datasets III: Probabilistic Photometric Redshifts for Galaxies and Quasars in the SDSS and GALEX
We apply machine learning in the form of a nearest neighbor instance-based algorithm (NN) to generate full photometric redshift probability density functions (PDFs) for objects in the Fifth Data Release of the Sloan Digital Sky Survey (SDSS DR5).
Robust Machine Learning Applied to Astronomical Datasets II: Quantifying Photometric Redshifts for Quasars Using Instance-Based Learning
The instance-based algorithm is trained on a representative sample of the data and pseudo-blind-tested on the remaining unseen data.
astro-ph
