Binary tomography - the process of identifying faulty network links through coordinated end-to-end probes - is a promising method for detecting failures that the network does not automatically mask (e.g., network "blackholes"). Because tomography is sensitive to the quality of the input, however, naïve end-to-end measurements can introduce inaccuracies. This paper develops two methods for generating inputs to binary tomography algorithms that improve their inference speed and accuracy. Failure confirmation is a per-path probing technique to distinguish packet losses caused by congestion from persistent link or node failures. Aggregation strategies combine path measurements from unsynchronized monitors into a set of consistent observations. When used in conjunction with existing binary tomography algorithms, our methods identify all failures that are longer than two measurement cycles, while inducing relatively few false alarms. In two wide-area networks, our techniques decrease the number of alarms by as much as two orders of magnitude. Compared to the state of the art in binary tomography, our techniques increase the identification rate and avoid hundreds of false alarms.