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Tingting Liu

4 papers hereh-index 331 citations12 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author4

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • astro-ph.GA2
  • astro-ph.HE2
same name
  • Tingting Liu — 14 papers, h 10
  • Tingting Liu — 12 papers, h 10
  • Tingting Liu — 3 papers, h 3
  • Tingting Liu — 2 papers, h 3
  • Tingting Liu — 2 papers, h 2
  • Tingting Liu — 1 paper, h 3

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

collaborators

4 papers

astro-ph.GA2026

BASS LV. Connecting X-ray variability with AGN physical properties and a new path to Cosmological distances

Matilde Signorini, Federica Ricci, Alessia Tortosa +18

X-ray variability is a well-established characteristic of active galactic nuclei (AGN), known to correlate inversely with both the supermassive black hole mass and luminosity, alth…

astro-ph.HE2026

An XMM-Newton Analysis of the Supermassive Black Hole Binary Candidate MCG+11--11--032

Yash A. Gursahani, Tingting Liu, Richard Mushotzky +2

We investigate the possibility of a binary supermassive black hole system at the center of MCG+11--11--032, a local (z = 0.036) Seyfert 2 galaxy. Prior work with stacked Swift/XRT…

astro-ph.HE2026

The Advanced X-ray Imaging Satellite (AXIS) Community Science Book

Michael Koss, Nafisa Aftab, Steven W. Allen +395

The AXIS Community Science Book represents the collective effort of 592 scientists worldwide to define the transformative science enabled by the Advanced X-ray Imaging Satellite (A…

astro-ph.GA2025

Continuum Reverberation Mapping of Accretion Disks Surrounding Supermassive Black Hole Binaries: Observational Signatures

Yi-Xin Fu, Yan-Rong Li, Jian-Min Wang +9

It has remained challenging to reliably identify sub-parsec supermassive black hole binaries (SMBHBs), despite them being expected to be ubiquitous. We propose a new method using m…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.