Testing EMRI Models for Quasi-periodic Eruptions with 3.5 yr of Monitoring eRO-QPE1
Bibliographic record
Abstract
Abstract Quasi-periodic eruptions (QPEs) are luminous X-ray outbursts recurring on hour timescales, observed from the nuclei of a growing handful of nearby low-mass galaxies. Their physical origin is still debated, and usually modeled as (a) accretion disk instabilities or (b) interaction of a supermassive black hole (SMBH) with a lower mass companion in an extreme mass-ratio inspiral (EMRI). EMRI models can be tested with several predictions related to the short- and long-term behavior of QPEs. In this study, we report on the ongoing 3.5 yr NICER and XMM-Newton monitoring campaign of eRO-QPE1, which is known to exhibit erratic QPEs that have been challenging for the simplest EMRI models to explain. We report (1) complex, non-monotonic evolution in the long-term trends of QPE energy output and inferred emitting area; (2) the disappearance of the QPEs (within NICER detectability) in 2023 October, and then the reappearance by 2024 January at a luminosity of ∼100× fainter (and temperature of ∼3× cooler) than the initial discovery; (3) radio non-detections with MeerKAT and Very Large Array observations partly contemporaneous with our NICER campaign (though not during outbursts); and (4) the presence of a possible ∼6 day modulation of the QPE timing residuals, which aligns with the expected nodal precession timescale of the underlying accretion disk. Our results tentatively support EMRI–disk collision models powering the QPEs, and we demonstrate that the timing modulation of QPEs may be used to jointly constrain the SMBH spin and disk density profile.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".