Context . The past decade has seen a rise in the use of machine learning methods in the study of young stellar evolution. This trend has led to a growing need for a comprehensive database of young stellar objects (YSOs) that goes beyond survey-specific biases and can be employed for training, validating, and refining the physical interpretation of machine learning outcomes.
Aims . We aim to review the literature focussed on the Orion star formation complex (OSFC) to compile a thorough catalogue of previously identified YSO candidates in the region, including the curation of observables relevant to probing their youth.
Methods . Starting from the NASA/ADS database, we assembled YSO candidates from more than 200 peer-reviewed publications targeting the OSFC. We collated data products relevant to the study of young stars into a dedicated catalogue, which was complemented with data from large photometric and spectroscopic surveys as well as the Strasbourg Astronomical Data Center. We also added significant value to the catalogue by homogeneously deriving YSO infrared classification labels and through a comprehensive curation of labels concerning the sources’ multiplicity. Finally, we used a panchromatic approach to derive the probabilities of the candidate YSOs in our catalogue being contaminant extragalactic sources or giant stars.
Results . We present the NEMESIS catalogue of YSOs for the OSFC, which includes data collated for 27 879 sources covering the whole mass spectrum and the various stages of pre-main sequence evolution from protostars to disc-less young stars. The catalogue includes a large collection of panchromatic photometric data processed into spectral energy distributions, stellar parameters ( T eff , T bol , spectral types, log ɡ , υ sin i , and RV), infrared classes, equivalent widths of emission lines related to YSO accretion and star-disc interaction, and absorption lines such as lithium and lines related to the source’s gravity, X-ray emission observables, photometric variability observables (e.g. variability periods and amplitudes), and multiplicity labels.
