Formation Channels of Magnetars

TL;DR

This work investigates how magnetars form through a range of channels, including single-star evolution, binary disruption, tidal spin-up, and various mergers, by deploying rapid population synthesis with COMPAS. The authors implement a flow for magnetar field amplification that includes fossil fields, dynamo processes (convective, MRI, Tayler-Spruit), and fallback-driven spin-up, and they explore how different SN prescriptions, natal kicks, and CE efficiencies shape the magnetar population. The study finds that most magnetars are produced through binary-interaction channels but are observed as isolated objects, with the S2 (binary disruption) channel dominating the total fraction (~57%), while tidal spin-up in binaries accounts for the majority of surviving magnetar binaries. The results imply a strong link between binary evolution and magnetar observables, including delay-time distributions, companion types (predominantly MS stars), kick velocity distributions compatible with observations under low CCSN kicks, and distinct orbital properties, offering testable predictions for FRB, GRB, and GW observations in the near future.

Abstract

The formation channels of magnetars remain an open question. Although core collapse supernovae of isolated massive stars are important, binary interactions -- such as tidal interaction, common envelope evolution, and stellar mergers -- may also play a significant role in making magnetars. Understanding the relative contributions of these channels is crucial for linking magnetars to their observed properties and host environments. In this paper, we investigate potential magnetar formation channels using population synthesis simulations, considering both single-star and isolated binary system evolution. By conducting simulations with different parameters, we compare the effects of various evolution processes on magnetar formation. Additionally, we study the delay times and kick velocities across all formation channels, analyze the orbital properties and companion types of surviving magnetar binaries. We find that the majority of magnetars are observed as single objects ($\geq 90\%$), although a large fraction of them were originally in binary systems and experienced either kick disruption or merger. Surviving binaries are most likely to host main-sequence companions and exhibit different distributions of eccentricities due to different supernova mechanisms. These findings show the critical role of binary evolution in magnetar formation and provide predictions for the properties of magnetar populations that can be tested with future observations.

Figures (12)

• Figure 1: Flowchart illustrating the decision process for magnetar formation. A PNS becomes a magnetar if any of the following conditions are satisfied: (1) a sufficiently strong fossil magnetic field is inherited from the progenitor, (2) rapid core rotation provides enough angular momentum and differential rotation to drive a convective dynamo, or (3) fallback accretion during core collapse triggers the Tayler–Spruit dynamo. If none of these amplification mechanisms operate, the outcome is an ordinary NS.
• Figure 2: Flowchart of formation of magnetars through single and isolated binary evolution channels. Magnetars with a light-colored magnetic field background are in binary systems, while those with a black-colored background are single.
• Figure 3: Fractions of different magnetar formation channels based on different population synthesis models.
• Figure 4: Cumulative distribution functions of delay times for different evolutionary channels. The left panel shows results for the fiducial model, while the right panel presents a model with metallicity fixed as $Z_{\odot}$. Each curve represents a different formation scenario, including single and binary core-collapse supernovae (CCSN), tidal interactions, compact object mergers (BWD, BMS), and AIC. The x-axis denotes the logarithm of the total delay time in Myr, while the y-axis shows the cumulative probability.
• Figure 5: Same as Fig.\ref{['fig:delay_time']}, but showing the distribution from the total single and total binary channels.