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Exploring the spectroscopic diversity of type Ia supernovae with DRACULA: a machine learning approach
Michele Sasdelli, E. E. O. Ishida, R. Vilalta +8
The existence of multiple subclasses of type Ia supernovae (SNeIa) has been the subject of great debate in the last decade. One major challenge inevitably met when trying to infer…
Using gamma regression for photometric redshifts of survey galaxies
J. Elliott, R. S. de Souza, A. Krone-Martins +3
Machine learning techniques offer a plethora of opportunities in tackling big data within the astronomical community. We present the set of Generalized Linear Models as a fast alte…
The Overlooked Potential of Generalized Linear Models in Astronomy-III: Bayesian Negative Binomial Regression and Globular Cluster Populations
R. S. de Souza, J. M. Hilbe, B. Buelens +5
In this paper, the third in a series illustrating the power of generalized linear models (GLMs) for the astronomical community, we elucidate the potential of the class of GLMs whic…
The Cosmic Dawn and Epoch of Reionization with the Square Kilometre Array
L. V. E. Koopmans, J. Pritchard, G. Mellema +42
Concerted effort is currently ongoing to open up the Epoch of Reionization (EoR) (15-6) for studies with IR and radio telescopes. Whereas IR detections have been made of sou…
cosmoabc: Likelihood-free inference via Population Monte Carlo Approximate Bayesian Computation
E. E. O. Ishida, S. D. P. Vitenti, M. Penna-Lima +5
Approximate Bayesian Computation (ABC) enables parameter inference for complex physical systems in cases where the true likelihood function is unknown, unavailable, or computationa…
AMADA-Analysis of Multidimensional Astronomical Datasets
Rafael S. de Souza, Benedetta Ciardi
We present AMADA, an interactive web application to analyse multidimensional datasets. The user uploads a simple ASCII file and AMADA performs a number of exploratory analysis toge…