The dipper is a novel class of young stellar object associated with large drops in flux on the order of 10-50 per cent lasting for hours to days. Too significant to arise from intrinsic stellar variability, these flux drops are currently attributed to disk warps, accretion streams, and/or transiting circumstellar dust. Dippers have been previously studied in young star forming regions including the Orion Complex. Using Next Generation Transit Survey (NGTS) data, we identified variable stars from their lightcurves. We then applied a machine learning random forest classifier for the identification of new dipper stars in Orion using previous variable classifications as a training set. We discover 120 new dippers, of which 83 are known members of the Complex. We also investigated the occurrence rate of disks in our targets, again using a machine learning approach. We find that all dippers have disks, and most of these are full disks. We use dipper periodicity and model-derived stellar masses to identify the orbital distance to the inner disk edge for dipper objects, confirming that dipper stars exhibit strongly extended sublimation radii, adding weight to arguments that the inner disk edge is further out than predicted by simple models. Finally, we determine a dipper fraction (the fraction of stars with disks which are dippers) for known members of 27.8+/-2.9 per cent. Our findings represent the largest population of dippers identified in a single cluster to date.

Cone search capability for table J/MNRAS/521/1700/ngtsvar (All variable NGTS sources analyzed in the paper)