Object classification with Convolutional Neural Networks: from KiDS to Euclid

G. A. Verdoes Kleijn; C. A. Marocico; Y. Mzayek; M. Pöntinen; M. Granvik; O. Williams; J. T. A. de Jong; T. Saifollahi; L. Wang; B. Margalef-Bentabol; A. La Marca; B. Chowdhary Nagam; L. V. E. Koopmans; E. A. Valentijn

doi:10.48550/arXiv.2403.01613

Object classification with Convolutional Neural Networks: from KiDS to Euclid

G. A. Verdoes Kleijn
, C. A. Marocico
, Y. Mzayek
, M. Pöntinen
, M. Granvik
, O. Williams
, J. T. A. de Jong
, T. Saifollahi
, L. Wang
, B. Margalef-Bentabol
, A. La Marca
, B. Chowdhary Nagam
, L. V. E. Koopmans
, E. A. Valentijn

Research output: Working paper › Preprint › Academic

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Abstract

Large-scale imaging surveys have grown about 1000 times faster than the number of astronomers in the last 3 decades. Using Artificial Intelligence instead of astronomer's brains for interpretative tasks allows astronomers to keep up with the data. We give a progress report on using Convolutional Neural Networks (CNNs) to classify three classes of rare objects (galaxy mergers, strong gravitational lenses and asteroids) in the Kilo-Degree Survey (KiDS) and the Euclid Survey.

Original language	English
Publisher	arXiv
Number of pages	7
DOIs	https://doi.org/10.48550/arXiv.2403.01613
Publication status	Submitted - 3-Mar-2024

Keywords

Astrophysics - Instrumentation and Methods for Astrophysics

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10.48550/arXiv.2403.01613Licence: CC BY-SA

Object classification with Convolutional Neural Networks: from KiDS to EuclidSubmitted manuscript, 454 KBLicence: CC BY-SA

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Verdoes Kleijn, G. A., Marocico, C. A., Mzayek, Y., Pöntinen, M., Granvik, M., Williams, O., de Jong, J. T. A., Saifollahi, T., Wang, L., Margalef-Bentabol, B., La Marca, A., Chowdhary Nagam, B., Koopmans, L. V. E., & Valentijn, E. A. (2024). Object classification with Convolutional Neural Networks: from KiDS to Euclid. arXiv. https://doi.org/10.48550/arXiv.2403.01613

@techreport{5d9eeedfa91c403c97ee9872fc11a55e,

title = "Object classification with Convolutional Neural Networks: from KiDS to Euclid",

abstract = "Large-scale imaging surveys have grown about 1000 times faster than the number of astronomers in the last 3 decades. Using Artificial Intelligence instead of astronomer's brains for interpretative tasks allows astronomers to keep up with the data. We give a progress report on using Convolutional Neural Networks (CNNs) to classify three classes of rare objects (galaxy mergers, strong gravitational lenses and asteroids) in the Kilo-Degree Survey (KiDS) and the Euclid Survey.",

keywords = "Astrophysics - Instrumentation and Methods for Astrophysics",

author = "\{Verdoes Kleijn\}, \{G. A.\} and Marocico, \{C. A.\} and Y. Mzayek and M. P{\"o}ntinen and M. Granvik and O. Williams and \{de Jong\}, \{J. T. A.\} and T. Saifollahi and L. Wang and B. Margalef-Bentabol and \{La Marca\}, A. and \{Chowdhary Nagam\}, B. and Koopmans, \{L. V. E.\} and Valentijn, \{E. A.\}",

year = "2024",

month = mar,

day = "3",

doi = "10.48550/arXiv.2403.01613",

language = "English",

publisher = "arXiv",

type = "WorkingPaper",

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TY - UNPB

T1 - Object classification with Convolutional Neural Networks: from KiDS to Euclid

AU - Verdoes Kleijn, G. A.

AU - Marocico, C. A.

AU - Mzayek, Y.

AU - Pöntinen, M.

AU - Granvik, M.

AU - Williams, O.

AU - de Jong, J. T. A.

AU - Saifollahi, T.

AU - Wang, L.

AU - Margalef-Bentabol, B.

AU - La Marca, A.

AU - Chowdhary Nagam, B.

AU - Koopmans, L. V. E.

AU - Valentijn, E. A.

PY - 2024/3/3

Y1 - 2024/3/3

N2 - Large-scale imaging surveys have grown about 1000 times faster than the number of astronomers in the last 3 decades. Using Artificial Intelligence instead of astronomer's brains for interpretative tasks allows astronomers to keep up with the data. We give a progress report on using Convolutional Neural Networks (CNNs) to classify three classes of rare objects (galaxy mergers, strong gravitational lenses and asteroids) in the Kilo-Degree Survey (KiDS) and the Euclid Survey.

AB - Large-scale imaging surveys have grown about 1000 times faster than the number of astronomers in the last 3 decades. Using Artificial Intelligence instead of astronomer's brains for interpretative tasks allows astronomers to keep up with the data. We give a progress report on using Convolutional Neural Networks (CNNs) to classify three classes of rare objects (galaxy mergers, strong gravitational lenses and asteroids) in the Kilo-Degree Survey (KiDS) and the Euclid Survey.

KW - Astrophysics - Instrumentation and Methods for Astrophysics

UR - http://adsabs.harvard.edu/abs/2024arXiv240301613V

U2 - 10.48550/arXiv.2403.01613

DO - 10.48550/arXiv.2403.01613

M3 - Preprint

BT - Object classification with Convolutional Neural Networks: from KiDS to Euclid

PB - arXiv

ER -

Object classification with Convolutional Neural Networks: from KiDS to Euclid

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