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Revealing the ultra-fast outflow in IRAS 13224-3809 through spectral variability

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Michael L. Parker, William N. Alston, Douglas J. K. Buisson, Andrew C. Fabian, Jiachen Jiang, Erin Kara, Anne Lohfink, Ciro Pinto
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Christopher S. Reynolds

High Energy Astrophysical Phenomena · Submitted 18 Apr 2017

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim This result demonstrates that we can detect outflows from variability alone, and that variability studies of UFOs are an extremely promising avenue for future research.

how it is stated · asserted 1.00 read the record →

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arXiv ID1704.05545 (version 1)
Pith ID6HOGP5VV, Pith's permanent record of this paper and its review
CitationAndrew C. Fabian, Anne Lohfink, Christopher S. Reynolds, Ciro Pinto, Douglas J. K. Buisson, Erin Kara, Jiachen Jiang, Michael L. Parker, William N. Alston. "Revealing the ultra-fast outflow in IRAS 13224-3809 through spectral variability." arXiv:1704.05545 (2017). https://pith.science/paper/6HOGP5VV
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,411 input tokens.

What the abstract itself states, as probabilities: claims first 0.06 · states limitation 0.17 · human participants 0.01 · controlled comparison 0.05 · randomized assignment 0.01 · states quantitative result 0.10 · compares with prior results 0.85 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

We present an analysis of the long-term X-ray variability of the extreme narrow-line Seyfert 1 (NLS1) galaxy IRAS 13224-3809 using principal component analysis (PCA) and fractional excess variability (Fvar) spectra to identify model-independent spectral components. We identify a series of variability peaks in both the first PCA component and Fvar spectrum which correspond to the strongest predicted absorption lines from the ultra-fast outflow (UFO) discovered by Parker et al. (2017). We also find higher order PCA components, which correspond to variability of the soft excess and reflection features. The subtle differences between RMS and PCA results argue that the observed flux-dependence of the absorption is due to increased ionization of the gas, rather than changes in column density or covering fraction. This result demonstrates that we can detect outflows from variability alone, and that variability studies of UFOs are an extremely promising avenue for future research.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 We present an analysis of the long-term X-ray variability of the extreme narrow-line Seyfert 1 (NLS1) galaxy IRAS 13224-3809 using principal component analysis (PCA) and fractional excess variability (Fvar) spectra to identify model-independent spectral components. contribution 0.79 0.96 0.04 asserted 0.97
s1 We identify a series of variability peaks in both the first PCA component and Fvar spectrum which correspond to the strongest predicted absorption lines from the ultra-fast outflow (UFO) discovered by Parker et al. (2017). result 1.00 0.92 0.16 asserted 1.00
s2 We also find higher order PCA components, which correspond to variability of the soft excess and reflection features. result 1.00 0.96 0.04 asserted 1.00
s3 The subtle differences between RMS and PCA results argue that the observed flux-dependence of the absorption is due to increased ionization of the gas, rather than changes in column density or covering fraction. interpretation 0.98 0.91 0.17 asserted 0.95
s4 This result demonstrates that we can detect outflows from variability alone, and that variability studies of UFOs are an extremely promising avenue for future research. interpretation 0.70 0.66 0.83 asserted 1.00

What kind of paper the classifier reports this is: empirical study 0.98 · evidence from analysis of existing data 0.97.

What was examined, and what was not

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not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
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  • P abstract
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  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

We present an analysis of the long-term X-ray variability of the extreme narrow-line Seyfert 1 (NLS1) galaxy IRAS 13224-3809 using principal component analysis (PCA) and fractional excess variability (Fvar) spectra to identify model-independent spectral components. We identify a series of variability peaks in both the first PCA component and Fvar spectrum which correspond to the strongest predicted absorption lines from the ultra-fast outflow (UFO) discovered by Parker et al. (2017). We also find higher order PCA components, which correspond to variability of the soft excess and reflection features. The subtle differences between RMS and PCA results argue that the observed flux-dependence of the absorption is due to increased ionization of the gas, rather than changes in column density or covering fraction. This result demonstrates that we can detect outflows from variability alone, and that variability studies of UFOs are an extremely promising avenue for future research.

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References
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3 arXiv papers cite this in 3 sentences

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

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