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A machine-learning approach for classifying low-mass X-ray binaries based on their compact object nature

A machine-learning approach for classifying low-mass X-ray binaries based on their compact object nature
A machine-learning approach for classifying low-mass X-ray binaries based on their compact object nature
Low Mass X-ray binaries (LMXBs) are binary systems where one of the components is either a black hole or a neutron star and the other is a less massive star. It is challenging to unambiguously determine whether a LMXB hosts a black hole or a neutron star. In the last few decades, multiple observational works have tried, with different levels of success, to address this problem. In this paper, we explore the use of machine learning to tackle this observational challenge. We train a random forest classifier to identify the type of compact object using the energy spectrum in the energy range 5-25 keV obtained from the Rossi X-ray Timing Explorer archive. We report an average accuracy of 87+/-13 in classifying the spectra of LMXB sources. We further use the trained model for predicting the classes for LMXB systems with unknown or ambiguous classification. With the ever-increasing volume of astronomical data in the X-ray domain from present and upcoming missions (e.g., SWIFT, XMM-Newton, XARM, ATHENA, NICER), such methods can be extremely useful for faster and robust classification of X-ray sources and can also be deployed as part of the data reduction pipeline.
X-rays: Binaries, methods: Data analysis, methods: Statistical
3457–3471
Pattnaik, R.
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Sharma, K.
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Alabarta, K.
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Altamirano, D.
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Chakraborty, M.
1dd7dce0-f26b-4db0-b184-d58e5a8eb2bf
Kembhavi, A.
bd81f9d0-98e8-40a2-bc43-6eded7964d46
Mendez, M.
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Orwat-Kapola, J. K.
6c9db0de-ab29-414b-9e08-44c43e658f7d
Pattnaik, R.
5b203d98-5409-443c-b04f-412efbd5240d
Sharma, K.
c56d338f-ba42-459e-bed4-d0562410a6e9
Alabarta, K.
8c164ccd-de85-4b2b-870b-7471ff95423b
Altamirano, D.
d5ccdb09-0b71-4303-9538-05b467be075b
Chakraborty, M.
1dd7dce0-f26b-4db0-b184-d58e5a8eb2bf
Kembhavi, A.
bd81f9d0-98e8-40a2-bc43-6eded7964d46
Mendez, M.
d8268614-2e47-4007-881f-1b377e21bfe1
Orwat-Kapola, J. K.
6c9db0de-ab29-414b-9e08-44c43e658f7d

Pattnaik, R., Sharma, K., Alabarta, K., Altamirano, D., Chakraborty, M., Kembhavi, A., Mendez, M. and Orwat-Kapola, J. K. (2021) A machine-learning approach for classifying low-mass X-ray binaries based on their compact object nature. Monthly Notices of the Royal Astronomical Society, 501 (3), 3457–3471. (doi:10.1093/mnras/staa3899).

Record type: Article

Abstract

Low Mass X-ray binaries (LMXBs) are binary systems where one of the components is either a black hole or a neutron star and the other is a less massive star. It is challenging to unambiguously determine whether a LMXB hosts a black hole or a neutron star. In the last few decades, multiple observational works have tried, with different levels of success, to address this problem. In this paper, we explore the use of machine learning to tackle this observational challenge. We train a random forest classifier to identify the type of compact object using the energy spectrum in the energy range 5-25 keV obtained from the Rossi X-ray Timing Explorer archive. We report an average accuracy of 87+/-13 in classifying the spectra of LMXB sources. We further use the trained model for predicting the classes for LMXB systems with unknown or ambiguous classification. With the ever-increasing volume of astronomical data in the X-ray domain from present and upcoming missions (e.g., SWIFT, XMM-Newton, XARM, ATHENA, NICER), such methods can be extremely useful for faster and robust classification of X-ray sources and can also be deployed as part of the data reduction pipeline.

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2012.06934v1 - Accepted Manuscript
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More information

Accepted/In Press date: 11 December 2020
e-pub ahead of print date: 21 December 2020
Published date: 1 March 2021
Additional Information: 16 pages, 10 figures, 7 tables, Accepted for publication in MNRAS main journal
Keywords: X-rays: Binaries, methods: Data analysis, methods: Statistical

Identifiers

Local EPrints ID: 447641
URI: http://eprints.soton.ac.uk/id/eprint/447641
PURE UUID: cf6a0cb4-93a8-4bcb-a593-3fde24f63aa2
ORCID for D. Altamirano: ORCID iD orcid.org/0000-0002-3422-0074
ORCID for J. K. Orwat-Kapola: ORCID iD orcid.org/0000-0002-3984-1881

Catalogue record

Date deposited: 17 Mar 2021 17:35
Last modified: 26 Nov 2021 03:17

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Contributors

Author: R. Pattnaik
Author: K. Sharma
Author: K. Alabarta
Author: D. Altamirano ORCID iD
Author: M. Chakraborty
Author: A. Kembhavi
Author: M. Mendez
Author: J. K. Orwat-Kapola ORCID iD

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