Classification of a black hole spin out of its shadow using support vector machines

González, José Antonio García-Cruces; Guzmán, F. S.

doi:10.1103/physrevd.99.103002

Cited by 4 publications

(6 citation statements)

References 22 publications

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“…Support Vector Machines (Cortes & Vapnik 1995) is one of the most well-established methods used in a wide range of topics. Some indicative examples include classification problems for variable stars (Pashchenko et al 2018), black hole spin (González & Guzmán 2019), molecular outflows (Zhang et al 2020), and supernova remnants (Kopsacheili et al 2020). The method searches for the line or the hyperplane (in two or multiple dimensions, respectively) that separates the input data (features) into distinct classes.…”

Section: Selected Algorithmsmentioning

confidence: 99%

A machine-learning photometric classifier for massive stars in nearby galaxies

Maravelias

Bonanos

Tramper

et al. 2022

A&A

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Context. Mass loss is a key parameter in the evolution of massive stars. Despite the recent progress in the theoretical understanding of how stars lose mass, discrepancies between theory and observations still hold. Moreover, episodic mass loss in evolved massive stars is not included in models, and the importance of its role in the evolution of massive stars is currently undetermined. Aims. A major hindrance to determining the role of episodic mass loss is the lack of large samples of classified stars. Given the recent availability of extensive photometric catalogs from various surveys spanning a range of metallicity environments, we aim to remedy the situation by applying machine-learning techniques to these catalogs. Methods. We compiled a large catalog of known massive stars in M31 and M33 using IR (Spitzer) and optical (Pan-STARRS) photometry, as well as Gaia astrometric information, which helps with foreground source detection. We grouped them into seven classes (Blue, Red, Yellow, B[e] supergiants, Luminous Blue Variables, Wolf-Rayet stars, and outliers, e.g., quasi-stellar objects and background galaxies). As this training set is highly imbalanced, we implemented synthetic data generation to populate the underrepresented classes and improve separation by undersampling the majority class. We built an ensemble classifier utilizing color indices as features. The probabilities from three machine-learning algorithms (Support Vector Classification, Random Forest, and Multilayer Perceptron) were combined to obtain the final classification.Results. The overall weighted balanced accuracy of the classifier is ∼ 83%. Red supergiants are always recovered at ∼ 94%. Blue and Yellow supergiants, B[e] supergiants, and background galaxies achieve ∼ 50 − 80%. Wolf-Rayet sources are detected at ∼ 45%, while Luminous Blue Variables are recovered at ∼ 30% from one method mainly. This is primarily due to the small sample sizes of these classes. In addition, the mixing of spectral types, as there are no strict boundaries in the features space (color indices) between those classes, complicates the classification. In an independent application of the classifier to other galaxies (IC 1613, WLM, and Sextans A), we obtained an overall accuracy of ∼ 70%. This discrepancy is attributed to the different metallicity and extinction effects of the host galaxies. Motivated by the presence of missing values, we investigated the impact of missing data imputation using a simple replacement with mean values and an iterative imputer, which proved to be more capable. We also investigated the feature importance to find that r − i and y − [3.6] are the most important, although different classes are sensitive to different features (with potential improvement with additional features). Conclusions. The prediction capability of the classifier is limited by the available number of sources per class (which corresponds to the sampling of their feature space), reflecting the rarity of these objects and the possible physical links between these massive st...

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Section: Selected Algorithmsmentioning

confidence: 99%

A machine-learning photometric classifier for massive stars in nearby galaxies

Maravelias

Bonanos

Tramper

et al. 2022

A&A

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“…The normal vector to Σ t at each point of the space-time is given by n µ = g µν n ν , where n ν = (−α, 0, 0, 0) is the 1-form measuring the density of hypersurfaces along the normal direction. We consider the matter to be a perfect fluid whose stressenergy tensor for a general space-time with metric g µν is T µν = ρ 0 hu µ u ν + pg µν , (2) where each volume element has rest mass density ρ 0 , specifi enthalpy h = 1 + e + p/ρ 0 , internal energy e, pressure p and 4-velocity u µ . With this metric one can construct expressions of the 4velocity of a flui element u µ = (u 0 , u i ) in terms of the velocities according to Eulerian observers:…”

Section: Hydrodynamics Equations On a Spherically Symmetric Space-timementioning

confidence: 99%

“…Formally the areal radius of the apparent horizon is R AH = γ θθ (t, r AH ), located where the outermost zero of Θ is found. Then we calculate the area of the AH as that of the corresponding 2-sphere A AH = 4πR 2 AH and the AH mass [22,23]. In our case, we are enforcing the condition γ θθ = r 2 , and therefore r AH = R AH .…”

Section: Apparent Horizonmentioning

confidence: 99%

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Spherical accretion of a perfect fluid onto a black hole

Murillo¹,

Ríos²,

Amezcua³

et al. 2021

Rev. Mex. Fis. E

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In this academic paper we present in detail the numerical solution of the accretion of a perfect fluid onto a black hole. The conditions are very simple, we consider a radial flux being accreted by a Schwarzschild black hole. We present two scenarios: 1) the test field case in which the fluid does not affect the geometry of the black hole space-time background, and 2) the full non-linear scenario, in which the geometry of the space-time evolves simultaneously with the fluid according to Einstein's equations. In the two scenarios we describe the black hole space-time in horizon penetrating coordinates, so that it is possible to visualize that accretion actually takes place within the numerical domain. For the evolution of matter we use the Valencia formulation of relativistic fluid dynamics. In the non-linear scenario we solve the equations of geometry using the ADM formulation of General Relativity, with very simple and intuitive gauge and boundary conditions, and include diagnostics related to the Apparent Horizon and Event Horizon growth. In view of the recent spectacular discoveries by the Event Horizon Telescope collaboration and further discoveries to come, the aim of this paper is to provide the necessary tools for interested graduate students in Black Hole Astrophysics, to enter into the accretion modeling starting from a considerable advanced starting point.

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“…Basically, the cross-validation separates the training set into a fixed number of subsets and, sequentially, each one of those subsets is used to test the accuracy of the decision function obtained using the rest of the subsets. For a detailed explanation of the methods the reader can consult [21, 22] and for a specific setup of our implementation in a similar analysis [23]. Instead of implementing our own version of the support vector machine, we use the library libSVM [22].…”

Section: Direct and Inverse Problemsmentioning

confidence: 99%

Location of sources in reaction-diffusion equations using support vector machines

2019

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The reaction-diffusion equation serves to model systems in the diffusion regime with sources. Specific applications include diffusion processes in chemical reactions, as well as the propagation of species, diseases, and populations in general. In some of these applications the location of an outbreak, for instance, the source point of a disease or the nest of a vector spreading a virus is important. Also important are the environmental parameters of the domain where the process diffuses, namely the space-dependent diffusion coefficient and the proliferation parameter of the process. Determining both, the location of a source and the environmental parameters, define an inverse problem that in turn, involves a partial differential equation. In this paper we classify the values of these parameters using Support Vector Machines (SVM) trained with numerical solutions of the reaction-diffusion problem. Our set up has accuracy of classifying the outbreak location above 90% and 77% of classifying both, the location and the environmental parameters. The approach presented in our analysis can be directly implemented by measuring the population under study at specific locations in the spatial domain as function of time.

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Classification of a black hole spin out of its shadow using support vector machines

Cited by 4 publications

References 22 publications

A machine-learning photometric classifier for massive stars in nearby galaxies

A machine-learning photometric classifier for massive stars in nearby galaxies

Spherical accretion of a perfect fluid onto a black hole

Location of sources in reaction-diffusion equations using support vector machines

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