Assessing Galaxy Rotation Kinematics: Insights from Convolutional Neural Networks on Velocity Variations

Universe, vol. 11, pp. 92

Abstract

Distinguishing galaxies as either fast or slow rotators plays a vital role in understanding the processes behind galaxy formation and evolution. Standard techniques, which are based on the λR-spin parameter obtained from stellar kinematics, frequently face difficulties to classify fast and slow rotators accurately. These challenges arise particularly in cases where galaxies have complex interaction histories or exhibit significant morphological diversity. In this paper, we evaluate the performance of a Convolutional Neural Network (CNN) on classifying galaxy rotation kinematics based on stellar kinematic maps from the SAMI survey. Our results show that the optimal CNN architecture achieves an accuracy and precision of approximately 91% and 95% on the test dataset, respectively. Subsequently, we apply our trained model to classify previously unknown rotator galaxies for which traditional statistical tools have been unable to determine whether they exhibit fast or slow rotation, such as certain irregular galaxies or those in dense clusters. We also used Integrated Gradients (IG) to reveal the crucial kinematic features that influenced the CNN’s classifications. This research highlights the power of CNNs to improve our comprehension of galaxy dynamics and emphasizes their potential to contribute to upcoming large-scale Integral Field Spectrograph (IFS) surveys.

Authors 5

1. Amirmohammad Chegeni

   University of Padua · Istituto Nazionale di Fisica Nucleare, Sezione di Padova

   Affiliation as printed

   Dipartimento di Fisica e Astronomia “G. Galilei”, Università di Padova, Via Marzolo 8, 35131 Padova, Italy

   INFN-Padova, Via Marzolo 8, 35131 Padova, Italy

2. Fatemeh Fazel Hesar corresponding Aachen Leiden Institution of Advance Computer Science

   Leiden University · European Space Agency · European Space Research and Technology Centre

   Affiliation as printed

   ILEWG LUNEX-EuroMoonMars Earth-Space, ESTEC European Space Agency, Keplerlaan 1, 2201 AZ Noordwijk, The Netherlands

   Leiden Institution of Advance Computer Science, P.O. Box 9513, 2300 RA Leiden, The Netherlands

3. Mojtaba Raouf Aachen Leiden Observatory

   Leiden University · European Space Agency · Institute for Research in Fundamental Sciences · Leiden Observatory · European Space Research and Technology Centre

   Affiliation as printed

   ILEWG LUNEX-EuroMoonMars Earth-Space, ESTEC European Space Agency, Keplerlaan 1, 2201 AZ Noordwijk, The Netherlands

   Leiden Observatory, Leiden University, P.O. Box 9513, 2300 RA Leiden, The Netherlands

   School of Astronomy, Institute for Research in Fundamental Sciences (IPM), Tehran 19395-5746, Iran

4. Bernard H. Foing Aachen Leiden Observatory

   Leiden University · European Space Agency · Leiden Observatory · European Space Research and Technology Centre

   Affiliation as printed

   ILEWG LUNEX-EuroMoonMars Earth-Space, ESTEC European Space Agency, Keplerlaan 1, 2201 AZ Noordwijk, The Netherlands

   Leiden Observatory, Leiden University, P.O. Box 9513, 2300 RA Leiden, The Netherlands

5. Fons J. Verbeek Aachen Leiden Institution of Advance Computer Science

   Leiden University

   Affiliation as printed

   Leiden Institution of Advance Computer Science, P.O. Box 9513, 2300 RA Leiden, The Netherlands