JOURNAL ARTICLE
Numerical studies on holographic paramagnetic-ferromagnetic phase transition in Gauss–Bonnet gravity.
Published In: Modern Physics Letters A, 2023, v. 38, n. 3. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Binaei Ghotbabadi, B.; Sheykhi, A.; Bordbar, G. H.; Montakhab, A. 3 of 3
Abstract
Based on the shooting method, we numerically investigate the properties of holographic paramagnetism-ferromagnetism phase transition in the presence of higher-order Gauss–Bonnet (GB) correction terms on the gravity side. On the matter field side, however, we consider the effects of Power-Maxwell (PM) nonlinear electrodynamics on the phase transition of this system. For this purpose, we introduce a massive 2-form coupled to PM field, and neglect the effects of 2-form fields and gauge field on the background geometry. We observe that increasing the strength of both the power parameter q and GB coupling constant α decreases the critical temperature of holographic model, and leads to the harder formation of magnetic moment in the black hole background. Interestingly, we find out that at low temperatures, the spontaneous magnetization and ferromagnetic phase transition happen in the absence of external magnetic field. In this case, the critical exponent for magnetic moment has the mean field value, 1 / 2 , regardless of the values of q and α. In the presence of external magnetic field, however, the magnetic susceptibility satisfies the Curie–Weiss law. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Modern Physics Letters A. 2023/01, Vol. 38, Issue 3, p1
- Document Type:Article
- Subject Area:Science
- Publication Date:2023
- ISSN:0217-7323
- DOI:10.1142/S0217732323500190
- Accession Number:163748307
- Copyright Statement:Copyright of Modern Physics Letters A is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.