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Introduction to astroML: Machine learning for astrophysics

Jacob VanderPlas, Andrew J. Connolly, Zeljko Ivezic, Alex Gray
2012 2012 Conference on Intelligent Data Understanding  
In this paper we describe astroML; an initiative, based on Python and scikit-learn, to develop a compendium of machine learning tools designed to address the statistical needs of the next generation of  ...  Astronomy and astrophysics are witnessing dramatic increases in data volume as detectors, telescopes and computers become ever more powerful.  ...  INTRODUCTION Data mining, machine learning and knowledge discovery are fields related to statistics, and to each other.  ... 
doi:10.1109/cidu.2012.6382200 dblp:conf/cidu/VanderPlasCIG12 fatcat:i2om7b2r2fc5xnldr43d7hsfr4

Machine learning technique for morphological classification of galaxies from the SDSS. I. Photometry-based approach

I. B. Vavilova, D. V. Dobrycheva, M. Yu Vasylenko, A. A. Elyiv, O. V. Melnyk, V. Khramtsov
2021 Astronomy and Astrophysics  
; (2) to test the influence of photometry data on morphology classification; (3) to discuss problem points of supervised machine learning and labeling bias; and (4) to apply the best fitting machine learning  ...  We use the photometry-based approach for the SDSS data (1) to exploit five supervised machine learning techniques and define the most effective among them for the automated galaxy morphological classification  ...  Valentina Karachentseva for the fruitful discussion and remarks. We are grateful to the referee for useful comments that allowed us to present the results of our study more fully.  ... 
doi:10.1051/0004-6361/202038981 fatcat:g4msubgonvbcpnrhmyeeuqcdye

Introduction to optimization with applications in astronomy and astrophysics

S. Canu, R. Flamary, D. Mary, D. Mary, R. Flamary, C. Theys, C. Aime
2016 EAS Publications Series  
This chapter aims at providing an introduction to numerical optimization with some applications in astronomy and astrophysics.  ...  For each family, we present in detail simple examples and more involved advanced examples. As a final illustration, we focus on two worked-out examples of optimization applied to astronomical data.  ...  inverse problems, data mining, or machine learning.  ... 
doi:10.1051/eas/1678007 fatcat:6lgmpphednbb5flwm4vgzauhlm

The Role of Machine Learning in Astronomy

Pankhudi Saraswat, Divya Jain
2021 International journal of advanced engineering research and applications  
Through this paper, authors try to bridge the difference between the two fields and try to show how various machine learning algorithms have been implemented in the field of space research and have made  ...  Astronomy is one such field that is reaching new levels with help of AI and machine learning.  ...  INTRODUCTION Astronomy is a field that has attracted and fascinated people for decades.  ... 
doi:10.46593/ijaera.2021.v07i03.001 fatcat:6jcwo2hk2jfgrf2f4xcozdbzfi

EmpiriciSN: Re-sampling Observed Supernova/Host Galaxy Populations Using an XD Gaussian Mixture Model

Thomas W.-S. Holoien, Philip J. Marshall, Risa H. Wechsler
2017 Astronomical Journal  
It allows the user to select between the AstroML (Vanderplas et al. 2012; Ivezic et al. 2015) and Bovy et al. (2011) fitting methods and is compatible with scikit-learn machine learning algorithms (Pedregosa  ...  XDGMM is new program for using Gaussian mixtures to do density estimation of noisy data using extreme deconvolution (XD) algorithms that has functionality not available in other XD tools.  ...  led to improvements to both the paper and the code.  ... 
doi:10.3847/1538-3881/aa68a1 fatcat:lcn6j3rrubbu7dnztmlofybq2y

ASTROMLSKIT: A New Statistical Machine Learning Toolkit: A Platform for Data Analytics in Astronomy [article]

Snehanshu Saha, Surbhi Agrawal, Manikandan. R, Kakoli Bora, Swati Routh, Anand Narasimhamurthy
2015 arXiv   pre-print
Machine learning algorithms and data analytic techniques provide the right platform for the challenges posed by these problems.  ...  The machine learning algorithms, integrated into ASTROMLSKIT, a toolkit developed in the course of the work, have been used to analyze HabCat data and supernovae data.  ...  We can conclude that data analytics and machine learning techniques learn characteristics from data. Data analytics uses machine learning methods to make decision for a system.  ... 
arXiv:1504.07865v1 fatcat:6wcwf3lu7rchzkedgawxesqs4i

Stellar Cluster Candidates Discovered in the Magellanic System

Andrés E. Piatti
2017 Astrophysical Journal Letters  
In addition, we used a functional relationship to account for the completeness of the SMASH field sample analyzed that takes into account not only the number of fields used but also their particular spatial  ...  We conducted a sound search for new stellar cluster candidates from suitable kernel density estimators running for appropriate ranges of radii and stellar densities.  ...  We thank David Nidever for providing us with an updated list of SMASH fields. We are also grateful to the anonymous referee for suggestions that improved the Letter.  ... 
doi:10.3847/2041-8213/834/2/l14 fatcat:khpdgooeejh4fiejadkwoeaaii

Solving Inverse Problems for Spectral Energy Distributions with Deep Generative Networks [article]

Agapi Rissaki, Orestis Pavlou, Dimitris Fotakis, Vicky Papadopoulou, Andreas Efstathiou
2020 arXiv   pre-print
We propose an end-to-end approach for solving inverse problems for a class of complex astronomical signals, namely Spectral Energy Distributions (SEDs).  ...  Our goal is to reconstruct such signals from scarce and/or unreliable measurements. We achieve that by leveraging a learned structural prior in the form of a Deep Generative Network.  ...  Introduction In astrophysics, distributions constructed by energy measurements in different wavelengths, namely Spectral Energy Distributions (SEDs), are important tools for studying the physical properties  ... 
arXiv:2012.06331v1 fatcat:insr5uhq5jfr7palbr5bvfi5aq

Computational Reproducibility Within Prognostics and Health Management [article]

Tim von Hahn, Chris K. Mechefske
2022 arXiv   pre-print
Although challenges remain, there are also clear opportunities, and benefits, for engaging in reproducible computational research.  ...  Highlighting an opportunity, we introduce an open-source software tool, called PyPHM, to assist PHM researchers in accessing and preprocessing common industrial datasets.  ...  et al., 2012) Astronomy and astrophysics Machine learning tools and data for astronomy and astrophysics Popular datasets, torchvision (Paszke et al., 2019) Computer vision model architectures, and image  ... 
arXiv:2205.15489v1 fatcat:oz4agkn74zd3zkibtfivkdfuzi

Discovery of a loose star cluster in the Large Magellanic Cloud

Andrés E. Piatti
2016 Monthly Notices of the Royal Astronomical Society: Letters  
We present results for an up-to-date uncatalogued star cluster projected towards the Eastern side of the Large Magellanic Cloud (LMC) outer disc.  ...  Nevertheless, radial velocity and chemical abundance measurements are needed to further understand its origin, as well as extensive search for loose star clusters in order to constrain the effectiveness  ...  ACKNOWLEDGEMENTS We thank the anonymous referee whose comments and suggestions allowed us to improve the manuscript.  ... 
doi:10.1093/mnrasl/slw053 fatcat:egounkve4jbgrbf4ugqzn47lim

Surveying the reach and maturity of machine learning and artificial intelligence in astronomy

Christopher J. Fluke, Colin Jacobs
2019 Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery  
This review surveys contemporary, published literature on machine learning and artificial intelligence in astronomy and astrophysics.  ...  Every week, new applications of machine learning and artificial intelligence are added to a growing corpus of work.  ...  Machine learning has also been used to identify the astrophysical features most significant for classification.  ... 
doi:10.1002/widm.1349 fatcat:k7swo7ozu5hhljtjlzofkxhb7u

API design for machine learning software: experiences from the scikit-learn project [article]

Lars Buitinck, Gilles Louppe, Mathieu Blondel, Fabian Pedregosa, Andreas Mueller, Olivier Grisel, Vlad Niculae, Peter Prettenhofer, Alexandre Gramfort (INRIA Saclay - Ile de France, LTCI), Jaques Grobler, Robert Layton, Jake Vanderplas (+3 others)
2013 arXiv   pre-print
Scikit-learn is an increasingly popular machine learning li- brary. Written in Python, it is designed to be simple and efficient, accessible to non-experts, and reusable in various contexts.  ...  In this paper, we present and discuss our design choices for the application programming interface (API) of the project.  ...  Past and present sponsors of the project also include Google for funding scholarships through its Summer of Code program, the Python Software Foundation and Tinyclues for funding coding sprints.  ... 
arXiv:1309.0238v1 fatcat:zsj2firttvb4zgbegpgagpt5py

EXPLORING THE VARIABLE SKY WITH LINEAR. III. CLASSIFICATION OF PERIODIC LIGHT CURVES

Lovro Palaversa, Željko Ivezić, Laurent Eyer, Domagoj Ruždjak, Davor Sudar, Mario Galin, Andrea Kroflin, Martina Mesarić, Petra Munk, Dijana Vrbanec, Hrvoje Božić, Sarah Loebman (+10 others)
2013 Astronomical Journal  
learning approach.  ...  The sample flux limit is several magnitudes fainter than for most other wide-angle surveys; the photometric errors range from 0.03 mag at r=15 to 0.20 mag at r=18.  ...  The Topcat tool (Taylor 2005 ) was used to find positional matches within a 3 arcsec  ... 
doi:10.1088/0004-6256/146/4/101 fatcat:4u3u7l72jbg3tgutvekb7gmnci

A supervised machine learning estimator for the non-linear matter power spectrum - SEMPS [article]

Irshad Mohammed, Janu Verma
2015 arXiv   pre-print
The estimator is trained on a set of cosmological models, and redshifts for which the P(k) is known, and it learns to predict P(k) for any other set.  ...  In this article, we argue that models based on machine learning (ML) can be very effective in estimating the non-linear matter power spectrum (P(k)).  ...  See (Hua et al. 2009 ) for a quick introduction to machine learning.  ... 
arXiv:1507.04622v1 fatcat:qsv3j2kawzcgljfigv3ug4vxzq

Large-scale clustering amongst Fermi blazars; evidence for axis alignments? [article]

M.J.M. Marcha, I.W.A. Browne
2021 arXiv   pre-print
To investigate if all blazars behave in the same way we used the machine learning classifications of Kovacevic, et al. (2020), which are based only on gamma-ray information, to separate BL Lac-like objects  ...  We find evidence for large-scale clustering amongst Fermi-selected BL Lac objects but not amongst Fermi-selected FSRQs.  ...  ACKNOWLEDGEMENTS We thank Lorne Whiteway for his extensive advice on di erent aspects of the two point correlation analysis and Neal Jackson, Scott Kay, and Richard Battye for early discussions.  ... 
arXiv:2105.06736v1 fatcat:rqurgi3nrrfqld3ujld4zx3may
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