Evidence for an abundant old population of Galactic ultra long period magnetars and implications for fast radio bursts
Authors: P. Beniamini, Z. Wadiasingh, J. Hare, K. Rajwade, G. Younes, A. J. van der Horst
Abstract: Two recent discoveries, namely PSR J0901-4046 and GLEAM-X J162759.5-523504.3 (hereafter GLEAM-X J1627), have corroborated an extant population of radio-loud periodic sources with long periods (76 s and 1091 s respectively) whose emission can hardly be explained by rotation losses. We argue that GLEAM-X J1627 is a highly-magnetized object consistent with a magnetar (an ultra long period magnetar - ULPM), and demonstrate it is unlikely to be either a magnetically or a rotationally-powered white dwarf. By studying these sources together with previously detected objects, we find there are at least a handful of promising candidates for Galactic ULPMs. The detections of these objects imply a substantial number, $N \gtrsim 13000$ and $N \gtrsim 500$ for PSR J0901--4046 like and GLEAM-X J1627 like objects, respectively, within our Galaxy. These source densities, as well as cooling age limits from non-detection of thermal X-rays, Galactic offsets, timing stability and dipole spindown limits, all imply the ULPM candidates are substantially older than confirmed Galactic magnetars and that their formation channel is a common one. Their existence implies widespread survival of magnetar-like fields for several Myr, distinct from the inferred behaviour in confirmed Galactic magnetars. ULPMs may also constitute a second class of FRB progenitors which could naturally exhibit very long periodic activity windows. Finally, we show that existing radio campaigns are biased against detecting objects like these and discuss strategies for future radio and X-ray surveys to identify more such objects. We estimate that ${\cal O}(100)$ more such objects should be detected with SKA-MID and DSA-2000.
Explore the paper tree
Click on the tree nodes to be redirected to a given paper and access their summaries and virtual assistant
Look for similar papers (in beta version)
By clicking on the button above, our algorithm will scan all papers in our database to find the closest based on the contents of the full papers and not just on metadata. Please note that it only works for papers that we have generated summaries for and you can rerun it from time to time to get a more accurate result while our database grows.