# Periodicity Searches

Quasar variability is usually tested for periodic signals against a damped random walk (DRW) null, and a mis-specified noise model biases the significance estimates. Nullasar uses flow matching to learn the statistical structure of stochastic variability from an ensemble of synthetic light curves without assuming a noise model, then generates null realizations conditioned on a single observed curve. On synthetic AR(2) light curves its periodicity-detection AUC is 0.9648, averaged over three independently trained models, above 0.9180 for fitting AR(2) per curve; in the AR(1) case it matches fitting the correct DRW model.

Quasar variability is usually tested for periodic signals against a damped random walk (DRW) null, and a mis-specified noise model biases the significance estimates. Nullasar uses flow matching to learn the statistical structure of stochastic variability from an ensemble of synthetic light curves without assuming a noise model, then generates null realizations conditioned on a single observed curve. On synthetic AR(2) light curves its periodicity-detection AUC is 0.9648, averaged over three independently trained models, above 0.9180 for fitting AR(2) per curve; in the AR(1) case it matches fitting the correct DRW model.