On the Black Hole Mass---X-ray Excess Variance Scaling Relation for Active Galactic Nuclei in the Low-mass Regime
Hai-Wu Pan, Weimin Yuan, Xin-Lin Zhou, Xiao-Bo Dong, Bifang Liu

TL;DR
This study investigates whether the inverse linear scaling between X-ray variability and black hole mass in AGN extends to low-mass black holes below 10^6 solar masses, finding that the relation flattens at lower masses.
Contribution
It provides the first detailed analysis of X-ray excess variance in low-mass AGN, revealing a deviation from the linear scaling at masses below 10^6 solar masses.
Findings
The scaling relation flattens at around 10^6 solar masses.
The flattening aligns with the shape of the AGN power spectrum density.
The results support the inverse relation between break frequency and black hole mass.
Abstract
Recent studies of active galactic nuclei (AGN) found a statistical inverse linear scaling between the X-ray normalized excess variance (variability amplitude) and the black hole mass spanning over . Being suggested to have a small scatter, this scaling relation may provide a novel method to estimate the black hole mass of AGN. However, a question arises as to whether this relation can be extended to the low-mass regime below . If confirmed, it would provide an efficient tool to search for AGN with low-mass black holes using X-ray variability. This paper presents a study of the X-ray excess variances for a sample of AGN with black hole masses in the range of observed with {\it XMM-Newton} and {\it ROSAT}, including data both from the archives and from newly preformed observations. It is…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAstrophysical Phenomena and Observations · Statistical and numerical algorithms · Scientific Measurement and Uncertainty Evaluation
