Research ideation from literature, data and models
The ideation agent looks for scientific questions that follow from concrete gaps: places where the literature, the observations and the models do not agree, or where a feature is still unexplained.
How it works
Ideas are not generated in isolation. The agent works on the context built by the other agents in the same session: the literature synthesis with its citations, the retrieved observational data, and the behaviour of the fitted models. It points out tensions, inconsistencies and unexplained features that appear when theory is compared with data, and proposes follow-up studies that can be done with existing data and models.
Each proposed question comes from a specific gap between the papers, the data and the models.
Example: TXS 0506+056
Asked what remains open in the association of the neutrino IceCube-170922A with TXS 0506+056, the agent answered that the source is the strongest single blazar candidate but not yet a definitive identification. The IceCube-170922A track, about 290 TeV and seen during a gamma-ray flare, is at about 3 to 3.5σ. The 2014 to 2015 archival excess of 13 ± 5 events is at about 3.5σ, with PKS 0502+049, 1.2° away, as a possible contributor. One-zone hadronic models are strongly disfavoured for the 2014 to 2015 flare, because they overproduce X-rays or need 10⁵⁰ to 10⁵² erg s⁻¹. The answer cites nine papers.
Scope
Ideation draws on the same blazar literature, MMDC data and models as the other agents, so its suggestions are limited to what those cover today.
Worked example
Other topics
- Blazar research with AstroGenesisThe first source class covered: literature, SED data and jet emission models in one session.
- Literature analysis for astrophysics questionsPublished papers synthesised into one answer, with evidence, disagreements and ADS-linked citations.
- Multi-wavelength and multi-messenger data retrievalScience-ready radio to gamma-ray data from MMDC, with instrument and epoch metadata.
- SED modeling with neural networks trained on radiative simulationsEmission models fitted to SEDs with neural networks trained on radiative simulations.
Ask about your own source
Ask in plain language: literature, data, or a model fit. Free plan: 10 messages per day.