Pith. sign in

REVIEW 1 cited by

Deep eROSITA observations of the "magnificent seven" isolated neutron stars

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2202.06793 v1 pith:T2C7TUPV submitted 2022-02-14 astro-ph.HE

classification astro-ph.HE
keywords erositaneutronstarsabsorptiondeepenergyfeaturesinss
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

We report the initial results of deep eROSITA monitoring of the "magnificent seven" isolated neutron stars (INSs). Thanks to a combination of high count statistics and good energy resolution, the eROSITA datasets unveil the increasingly complex energy distribution of these presumably simple thermal emitters. For three targets, we report the detection of multiple (in some cases, phase-dependent) spectral absorption features and deviations from the dominant thermal continuum. Unexpected long-term changes of spectral state and timing behaviour have additionally been observed for two INSs. The results pose challenging theoretical questions on the nature of the variations and absorption features and ultimately impact the modeling of the atmosphere and cooling of highly magnetised neutron stars.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Counterfactual Shapley Credit Assignment

    cs.LG 2026-07 conditional novelty 6.0 of 10

    Counterfactual Shapley values, computed by simulated 'what-if' action replacements, redistribute RL rewards without changing the optimal policy and improve credit assignment in stochastic, sparse, delayed-reward tasks.

Pith tools