{"paper":{"title":"Simulation-based inference of profile evolution for high-precision pulsar timing","arxiv_id":"2610.31450","primary_cat":"astro-ph.IM","submitted_at":"2026-10-22T15:46:04+00:00","version_compiled":"v1"},"notice":"Every quoted sentence is the authors' text from the arXiv listing abstract. Labels and probabilities are the outputs of classifier-1.13.0 on the question bank abstract-p1-20260923, exactly as returned. 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decades.","hedge":{"p":1.0,"label":"asserted"},"own_p":0.05,"main_claim_p":0.03,"quantitative":false,"quantitative_p":0.33,"role_probabilities":{"gap":0.0,"method":0.0,"result":0.0,"background":1.0,"limitation":0.0,"future_work":0.0,"contribution":0.0,"interpretation":0.0}},{"i":2,"p":0.98,"id":"s2","own":false,"role":"background","text":"These profiles change shape systematically with observing frequency, reflecting the physics of the emitting pulsar beam, but if left unmodelled, it can bias the arrival-time measurements, undermining precision timing.","hedge":{"p":0.93,"label":"asserted"},"own_p":0.07,"main_claim_p":0.05,"quantitative":false,"quantitative_p":0.06,"role_probabilities":{"gap":0.02,"method":0.0,"result":0.0,"background":0.98,"limitation":0.0,"future_work":0.0,"contribution":0.0,"interpretation":0.0}},{"i":3,"p":0.84,"id":"s3","own":false,"role":"gap","text":"Profile-domain timing has been proposed to address this effect, but current implementations use stochastic sampling, which is computationally prohibitive and has limited the application in large-scale analyses.","hedge":{"p":1.0,"label":"asserted"},"own_p":0.05,"main_claim_p":0.04,"quantitative":false,"quantitative_p":0.18,"role_probabilities":{"gap":0.84,"method":0.0,"result":0.0,"background":0.15,"limitation":0.01,"future_work":0.0,"contribution":0.0,"interpretation":0.0}},{"i":4,"p":0.55,"id":"s4","own":true,"role":"contribution","text":"Here, we present a simulation-based inference approach to profile-domain timing, using a normalising flow conditioned on a convolutional embedding of the frequency-phase portrait.","hedge":{"p":1.0,"label":"asserted"},"own_p":0.94,"main_claim_p":0.23,"quantitative":false,"quantitative_p":0.05,"role_probabilities":{"gap":0.0,"method":0.45,"result":0.0,"background":0.0,"limitation":0.0,"future_work":0.0,"contribution":0.55,"interpretation":0.0}},{"i":5,"p":0.43,"id":"s5","own":true,"role":"method","text":"From a single portrait, it returns joint posteriors on dispersion measures, arrival times, and the parameters governing profile evolution instantaneously, with uncertainties verified against known inputs following an initial training run.","hedge":{"p":1.0,"label":"asserted"},"own_p":0.93,"main_claim_p":0.33,"quantitative":false,"quantitative_p":0.33,"role_probabilities":{"gap":0.0,"method":0.43,"result":0.31,"background":0.0,"limitation":0.0,"future_work":0.0,"contribution":0.26,"interpretation":0.0}},{"i":6,"p":0.97,"id":"s6","own":false,"role":"interpretation","text":"The value of a likelihood-free approach is that the simulator becomes the interface to pulsar properties.","hedge":{"p":1.0,"label":"asserted"},"own_p":0.25,"main_claim_p":0.14,"quantitative":false,"quantitative_p":0.04,"role_probabilities":{"gap":0.0,"method":0.02,"result":0.0,"background":0.0,"limitation":0.0,"future_work":0.0,"contribution":0.01,"interpretation":0.97}},{"i":7,"p":0.95,"id":"s7","own":false,"role":"interpretation","text":"Fundamental properties like non-Gaussian noise, pulsar self-noise, jitter, scattering, and profile change can be inferred by simulating them rather than by deriving a new likelihood for each, opening a promising pathway to include machine learning methods into pulsar timing workflows.","hedge":{"p":0.63,"label":"hedged_possibility"},"own_p":0.23,"main_claim_p":0.36,"quantitative":false,"quantitative_p":0.06,"role_probabilities":{"gap":0.0,"method":0.01,"result":0.0,"background":0.0,"limitation":0.0,"future_work":0.03,"contribution":0.01,"interpretation":0.95}}]},"compiled":{"main_claim":{"p":0.9,"text":"Fundamental properties like non-Gaussian noise, pulsar self-noise, jitter, scattering, and profile change can be inferred by simulating them rather than by deriving a new likelihood for each, opening a promising pathway to include machine learning methods into pulsar timing workflows.","location":"abstract s7","hedge_strength":{"p":0.63,"label":"hedged possibility"}},"provenance":{"route":{"via":"TypeSafe 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