Machine learning classification of SDSS transient survey images

Buisson, L. du; Sivanandam, Navin; Bassett, Bruce A.; Smith, M.

doi:10.1093/mnras/stv2041

Cited by 55 publications

(35 citation statements)

References 32 publications

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“…Machine learning has been applied in classifying objects in different wavelengths (e.g. Farrell et al 2015;Du Buisson et al 2015;Miller et al 2015;Mirabal et al 2012;Saz Parkinson et al 2016) In this work, we will focus on the γ−ray regime.…”

Section: Introductionmentioning

confidence: 99%

An investigation on the factors affecting machine learning classifications in gamma-ray astronomy

Luo

Leung

Hui

et al. 2020

Monthly Notices of the Royal Astronomical Society

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We have investigated a number of factors that can have significant impacts on the classification performance of γ-ray sources detected by Fermi Large Area Telescope (LAT) with machine learning techniques. We show that a framework of automatic feature selection can construct a simple model with a small set of features which yields better performance over previous results. Secondly, because of the small sample size of the training/test sets of certain classes in γ-ray, nested re-sampling and cross-validations are suggested for quantifying the statistical fluctuations of the quoted accuracy. We have also constructed a test set by cross-matching the identified active galactic nuclei (AGNs) and the pulsars (PSRs) in the Fermi LAT eightyear point source catalog (4FGL) with those unidentified sources in the previous 3 rd Fermi LAT Source Catalog (3FGL). Using this cross-matched set, we show that some features used for building classification model with the identified source can suffer from the problem of covariate shift, which can be a result of various observational effects. This can possibly hamper the actual performance when one applies such model in classifying unidentified sources. Using our framework, both AGN/PSR and young pulsar (YNG)/millisecond pulsar (MSP) classifiers are automatically updated with the new features and the enlarged training samples in 4FGL catalog incorporated. Using a two-layer model with these updated classifiers, we have selected 20 promising MSP candidates with confidence scores > 98% from the unidentified sources in 4FGL catalog which can provide inputs for a multiwavelength identification campaign.

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Section: Introductionmentioning

confidence: 99%

An investigation on the factors affecting machine learning classifications in gamma-ray astronomy

Luo

Leung

Hui

et al. 2020

Monthly Notices of the Royal Astronomical Society

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“…Automatized classification using machine-learning algorithms has recently gained popularity in astronomy and has been applied to a number of problems, including star/galaxy/quasar classification (Bloom et al 2012;Solarz et al 2012;Małek et al 2013;Kurcz et al 2016) and the identification of different types of supernovae (du Buisson et al 2015;Lochner et al 2016).…”

Section: Introductionmentioning

confidence: 99%

A Machine-learning Method for Identifying Multiwavelength Counterparts of Submillimeter Galaxies: Training and Testing Using AS2UDS and ALESS

Stach

Smail

et al. 2018

ApJ

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Additional information: Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. AbstractWe describe the application of supervised machine-learning algorithms to identify the likely multiwavelength counterparts to submillimeter sources detected in panoramic, single-dish submillimeter surveys. As a training set, we employ a sample of 695 (S 870μm 1 mJy) submillimeter galaxies (SMGs) with precise identifications from the ALMA follow-up of the SCUBA-2 Cosmology Legacy Survey's UKIDSS-UDS field (AS2UDS). We show that radio emission, near-/mid-infrared colors, photometric redshift, and absolute H-band magnitude are effective predictors that can distinguish SMGs from submillimeter-faint field galaxies. Our combined radio + machinelearning method is able to successfully recover ∼85% of ALMA-identified SMGs that are detected in at least three bands from the ultraviolet to radio. We confirm the robustness of our method by dividing our training set into independent subsets and using these for training and testing, respectively, as well as applying our method to an independent sample of ∼100 ALMA-identified SMGs from the ALMA/LABOCA ECDF-South Survey (ALESS).To further test our methodology, we stack the 870 μm ALMA maps at the positions of those K-band galaxies that are classified as SMG counterparts by the machine learning but do not have a >4.3σ ALMA detection. The median peak flux density of these galaxies is S 870μm =(0.61±0.03) mJy, demonstrating that our method can recover faint and/or diffuse SMGs even when they are below the detection threshold of our ALMA observations. In future, we will apply this method to samples drawn from panoramic single-dish submillimeter surveys that currently lack interferometric follow-up observations to address science questions that can only be tackled with large statistical samples of SMGs.

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“…Neural networks are now commonly used in complex classification tasks such as image recognition (e.g., Krizhevsky et al 2012;Liu et al 2014;Wang et al 2016;Shen et al 2015), speech and music recognition (e.g., Hung et al 2005;Jaitly & Hinton 2011;Zhang & Wu 2013;Pradeep & Kumaraswamy 2014), biology (e.g., Head-Gordon & Stillinger 1993Plebe 2007;Wu & McLarty 2012;Spencer et al 2015), and are finding increased use in the classification of galaxies and cosmology (e.g., Collister & Lahav 2004;Agarwal et al 2012Agarwal et al , 2014Reis et al 2012;Karpenka et al 2013;Dieleman et al 2015;du Buisson et al 2015;Ellison et al 2015;Huertas-Company et al 2015).…”

Section: Robertmentioning

confidence: 99%

Dreaming of Atmospheres

Waldmann

2016

ApJ

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Here, we introduce the RobERt(Robotic Exoplanet Recognition) algorithm for the classification of exoplanetary emission spectra. Spectral retrieval of exoplanetary atmospheres frequently requires the preselection of molecular/ atomic opacities to be defined by the user. In the era of open-source, automated, and self-sufficient retrieval algorithms, manual input should be avoided. User dependent input could, in worst-case scenarios, lead to incomplete models and biases in the retrieval. The RobERtalgorithm is based on deep-belief neural (DBN) networks trained to accurately recognize molecular signatures for a wide range of planets, atmospheric thermal profiles, and compositions. Reconstructions of the learned features, also referred to as the "dreams" of the network, indicate good convergence and an accurate representation of molecular features in the DBN. Using these deep neural networks, we work toward retrieval algorithms that themselves understand the nature of the observed spectra, are able to learn from current and past data, and make sensible qualitative preselections of atmospheric opacities to be used for the quantitative stage of the retrieval process.

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Machine learning classification of SDSS transient survey images

Cited by 55 publications

References 32 publications

An investigation on the factors affecting machine learning classifications in gamma-ray astronomy

An investigation on the factors affecting machine learning classifications in gamma-ray astronomy

A Machine-learning Method for Identifying Multiwavelength Counterparts of Submillimeter Galaxies: Training and Testing Using AS2UDS and ALESS

Dreaming of Atmospheres

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