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The accretion environment of supergiant fast X-ray transients probed with

2017· article· en· W6903166625 on OpenAlexfundno aff

Bibliographic record

VenueSpringer Link (Chiba Institute of Technology) · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysical Phenomena and Observations
Canadian institutionsnot available
FundersKazan Federal UniversityYork UniversityNew York University Abu Dhabi
KeywordsSupergiantHomogeneousAccretion (finance)Absorption (acoustics)Spectral lineFlare

Abstract

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Context. Supergiant fast X-ray (SFXT) transients are a peculiar class of supergiant X-ray binaries characterized by a remarkable variability in the X-ray domain, widely ascribed to accretion from a clumpy stellar wind. Aims. In this paper we performed a systematic and homogeneous analysis of the sufficiently bright X-ray flares observed with XMM-Newton from the supergiant fast X-ray transients to probe spectral variations on timescales as short as a few hundred seconds. Our ultimate goal is to investigate whether SFXT flares and outbursts are triggered by the presence of clumps, and to reveal whether strongly or mildly dense clumps are required. Methods. For all sources, we employ a technique developed by our group already exploited in a number of our previous papers, making use of an adaptive rebinned hardness ratio to optimally select the time intervals for the spectral extraction. A total of twelve observations performed in the direction of five SFXTs are reported, providing the largest sample of events available so far. Results. Using the original results reported here and those obtained with our technique from the analysis of two previously published XMM-Newton observations of IGR J17544-2619 and IGR J18410-0535, we show that both strongly and mildly dense clumps can trigger these events. In the former case, the local absorption column density may increase by a factor of ≫3, while in the latter case, the increase is only a factor of ~2–3 (or lower). An increase in the absorption column density is generally recorded during the rise of the flares/outbursts, while a drop follows when the source achieves peak flux. In a few cases, a re-increase of the absorption column density after the flare is also detected, and we discovered one absorption event related to the passage of an unaccreted clump in front of the compact object. Overall, there seems to be no obvious correlation between the dynamic ranges in the X-ray fluxes and absorption column densities in supergiant fast X-ray transients, with an indication that lower densities are recorded at the highest fluxes. Conclusions. The spectral variations measured in all sources are in agreement with the idea that the flares/outbursts are triggered by the presence of dense structures in the wind interacting with the X-rays from the compact object (leading to photoionization). The lack of correlation between the dynamic ranges in the X-ray fluxes and absorption column densities can be explained by the presence of accretion inhibition mechanism(s). Based on the knowledge acquired so far on the SFXTs, we propose a classification of the flares/outbursts from these sources in order to drive future observational investigations. We suggest that the difference between the classes of flares/outbursts is related to the fact that the mechanism(s) inhibiting accretion can be overcome more easily in some sources compared to others. We also investigate the possibility that different stellar wind structures, other than clumps, could provide the means to temporarily overcome the inhibition of accretion in supergiant fast X-ray transients.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.211
Teacher spread0.201 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2017
Admission routes1
Has abstractyes

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