Selected Papers from “Dark Matter and Stars: Multi-Messenger Probes of Dark Matter and Modified Gravity”

A special issue of Particles (ISSN 2571-712X).

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 4054

Special Issue Editors


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Guest Editor
Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal
Interests: dark matter; stellar astrophysics; cosmology
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Guest Editor
Department of Physics, University of Coimbra, Coimbra, Portugal
Interests: compact stars; dark matter; nuclear physics

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Guest Editor
Institute for Theoretical Physics, Goethe University, Frankfurt, Germany
Interests: gravitational waves; dark matter; cosmology

Special Issue Information

Dear Colleagues,

With great pleasure, we would like to announce that, based on presentations at the International Conference "Dark Matter and Stars: Multi-Messenger Probes of Dark Matter and Modified Gravity" (https://indico.cern.ch/event/1194229), selected papers will be published in this Special Issue of Particles, an MDPI open-access journal. Manuscripts submitted to this Special Issue should contain original work or be a review of the field of expertise of the author(s). All submissions will be peer-reviewed by internationally recognized experts.

The International Conference "Dark Matter and Stars: Multi-Messenger Probes of Dark Matter and Modified Gravity" aims to bring together scientists working across the different research fields of astrophysics, cosmology, and modified gravity. We want to look at the dark matter problem from different perspectives, considering it to be of particle nature, as well as modification of gravity. This meeting is intended to initiate cross-field discussions of dark matter searches, their current status, and future prospects. We hope that the Special Issue becomes a useful reference both for beginners and experienced researchers in the dark matter topic.

As the Guest Editors, we invite you to submit your unpublished and original research relevant to this topic for publication in this Special Issue of Particles. The Article Processing Charge (APC) for submissions from the conference will be waived, and publication will be free of charge.

Prof. Dr. Ilídio Lopes
Dr. Violetta Sagun
Prof. Dr. Laura Sagunski
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Particles is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (3 papers)

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Research

13 pages, 3989 KiB  
Article
Exploring the Distribution and Impact of Bosonic Dark Matter in Neutron Stars
by Davood Rafiei Karkevandi, Mahboubeh Shahrbaf, Soroush Shakeri and Stefan Typel
Particles 2024, 7(1), 201-213; https://doi.org/10.3390/particles7010011 - 03 Mar 2024
Viewed by 854
Abstract
The presence of dark matter (DM) within neutron stars (NSs) can be introduced by different accumulation scenarios in which DM and baryonic matter (BM) may interact only through the gravitational force. In this work, we consider asymmetric self-interacting bosonic DM, which can reside [...] Read more.
The presence of dark matter (DM) within neutron stars (NSs) can be introduced by different accumulation scenarios in which DM and baryonic matter (BM) may interact only through the gravitational force. In this work, we consider asymmetric self-interacting bosonic DM, which can reside as a dense core inside the NS or form an extended halo around it. It is seen that depending on the boson mass (mχ), self-coupling constant (λ) and DM fraction (Fχ), the maximum mass, radius and tidal deformability of NSs with DM admixture will be altered significantly. The impact of DM causes some modifications in the observable features induced solely by the BM component. Here, we focus on the widely used nuclear matter equation of state (EoS) called DD2 for describing NS matter. We show that by involving DM in NSs, the corresponding observational parameters will be changed to be consistent with the latest multi-messenger observations of NSs. It is seen that for mχ200 MeV and λ2π, DM-admixed NSs with 4%Fχ20% are consistent with the maximum mass and tidal deformability constraints. Full article
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16 pages, 1378 KiB  
Article
Towards Uncovering Dark Matter Effects on Neutron Star Properties: A Machine Learning Approach
by Prashant Thakur, Tuhin Malik and Tarun Kumar Jha
Particles 2024, 7(1), 80-95; https://doi.org/10.3390/particles7010005 - 15 Jan 2024
Cited by 2 | Viewed by 1386
Abstract
Over the last few years, researchers have become increasingly interested in understanding how dark matter affects neutron stars, helping them to better understand complex astrophysical phenomena. In this paper, we delve deeper into this problem by using advanced machine learning techniques to find [...] Read more.
Over the last few years, researchers have become increasingly interested in understanding how dark matter affects neutron stars, helping them to better understand complex astrophysical phenomena. In this paper, we delve deeper into this problem by using advanced machine learning techniques to find potential connections between dark matter and various neutron star characteristics. We employ Random Forest classifiers to analyze neutron star (NS) properties and investigate whether these stars exhibit characteristics indicative of dark matter admixture. Our dataset includes 32,000 sequences of simulated NS properties, each described by mass, radius, and tidal deformability, inferred using recent observations and theoretical models. We explore a two-fluid model for the NS, incorporating separate equations of state for nucleonic and dark matter, with the latter considering a fermionic dark matter scenario. Our classifiers are trained and validated in a variety of feature sets, including the tidal deformability for various masses. The performance of these classifiers is rigorously assessed using confusion matrices, which reveal that NS with admixed dark matter can be identified with approximately 17% probability of misclassification as nuclear matter NS. In particular, we find that additional tidal deformability data do not significantly improve the precision of our predictions. This article also delves into the potential of specific NS properties as indicators of the presence of dark matter. Radius measurements, especially at extreme mass values, emerge as particularly promising features. The insights gained from our study are pivotal for guiding future observational strategies and enhancing the detection capabilities of dark matter in NS. This study is the first to show that the radii of neutron stars at 1.4 and 2.07 solar masses, measured using NICER data from pulsars PSR J0030+0451 and PSR J0740+6620, strongly suggest that the presence of dark matter in a neutron star is more likely than only hadronic composition. Full article
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28 pages, 5203 KiB  
Article
Fermion Proca Stars: Vector-Dark-Matter-Admixed Neutron Stars
by Cédric Jockel and Laura Sagunski
Particles 2024, 7(1), 52-79; https://doi.org/10.3390/particles7010004 - 09 Jan 2024
Cited by 4 | Viewed by 1150
Abstract
Dark matter could accumulate around neutron stars in sufficient amounts to affect their global properties. In this work, we study the effect of a specific model for dark matter—a massive and self-interacting vector (spin-1) field—on neutron stars. We describe the combined systems of [...] Read more.
Dark matter could accumulate around neutron stars in sufficient amounts to affect their global properties. In this work, we study the effect of a specific model for dark matter—a massive and self-interacting vector (spin-1) field—on neutron stars. We describe the combined systems of neutron stars and vector dark matter using Einstein–Proca theory coupled to a nuclear matter term and find scaling relations between the field and metric components in the equations of motion. We construct equilibrium solutions of the combined systems, compute their masses and radii, and also analyze their stability and higher modes. The combined systems admit dark matter (DM) core and cloud solutions. Core solutions compactify the neutron star component and tend to decrease the total mass of the combined system. Cloud solutions have the inverse effect. Electromagnetic observations of certain cloud-like configurations would appear to violate the Buchdahl limit. This could make Buchdahl-limit-violating objects smoking gun signals for dark matter in neutron stars. The self-interaction strength is found to significantly affect both mass and radius. We also compare fermion Proca stars to objects where the dark matter is modeled using a complex scalar field. We find that fermion Proca stars tend to be more massive and geometrically larger than their scalar field counterparts for equal boson masses and self-interaction strengths. Both systems can produce degenerate masses and radii for different amounts of DM and DM particle masses. Full article
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