  Search for new physics events at the LHC mostly rely on the assumption that
the events are characterized in terms of standard-reconstructed objects such as
isolated photons, leptons, and jets initiated by QCD-partons. While such
strategy works for a vast majority of physics beyond the standard model
scenarios, there are examples aplenty where new physics give rise to anomalous
objects (such as collimated and equally energetic particles, decays due to long
lived particles etc.) in the detectors, which can not be classified as any of
the standard-objects. Varied methods and search strategies have been proposed,
each of which is trained and optimized for specific models, topologies, and
model parameters. Further, as LHC keeps excluding all expected candidates for
new physics, the need for a generic method/tool that is capable of finding the
unexpected can not be understated. In this paper, we propose one such method
that relies on the philosophy that all anomalous objects are $\it{not}$
standard-objects. The anomaly finder, we suggest, simply is a collection of
vetoes that eliminate all standard-objects up to a pre-determined acceptance
rate. Any event containing at least one anomalous object (that passes all these
vetoes), can be identified as a candidate for new physics. Subsequent offline
analyses can determine the nature of the anomalous object as well as of the
event, paving a robust way to search for these new physics scenarios in a
model-independent fashion. Further, since the method relies on learning only
the standard-objects, for which control samples are readily available from
data, one can build the analysis in an entirely data-driven way.
