We present an initial analysis of Radio Frequency Interference (RFI) flagging statistics from archived Australian SKA Pathfinder (ASKAP) observations for the ‘Survey and Monitoring of ASKAP’s RFI environment and Trends’ (SMART) project. SMART is a two-part observatoryled project combining analysis of archived observations with a dedicated, comprehensive RFI survey. The survey component covers ASKAP’s full 700–1 800 MHz frequency range, including bands not typically used due to severe RFI. Observations are underway to capture a detailed snapshot of the ASKAP RFI environment over representative 24 h periods. In addition to this dedicated survey, we routinely archive and analyse flagging statistics for all scientific observations to monitor the observatory’s RFI environment in near real-time. We use the telescope itself as a very sensitive RFI monitor and directly assess the fraction of scientific observations impacted by RFI. To this end, flag tables are now automatically ingested and aggregated as part of routine ASKAP operations for all science observations, as a function of frequency and time. The data presented in this paper come from processing all archived data for several ASKAP Survey Science Projects (SSPs). We found that the average amount of flagging due to RFI across the routinely used ‘clean’ continuum science bands is 3%. The ‘clean’ mid band from 1 293 to 1 437 MHz (excluding the 144 MHz below 1293 MHz impacted by radionavigation-satellites which is discarded before processing) is the least affected by RFI, followed by the ‘clean’ low band from 742 to 1 085 MHz. ASKAP SSPs lose most of their data to the mobile service in the low band, aeronautical service in the mid band and satellite navigation service in the 1 510–1 797 MHz high band. We also show that for some of these services, the percentage of discarded data has been increasing year-on-year. SMART provides a unique opportunity to study ASKAP’s changing RFI environment, including understanding and updating the default flagging behaviour, inferring the suitability of and calibrating RFI monitoring equipment, monitoring spectrum management compliance in the Australian Radio Quiet Zone – Western Australia (ARQZWA), and informing the implementation of a suite of RFI mitigation techniques.

The VAST survey is a wide-field survey that observes with unprecedented instrument sensitivity (0.5 mJy or lower) and repeat cadence (a goal of 5 seconds) that will enable novel scientific discoveries related to known and unknown classes of radio transients and variables. Given the unprecedented observing characteristics of VAST, it is important to estimate source classification performance, and determine best practices prior to the launch of ASKAP's BETA in 2012. The goal of this study is to identify light-curve characterization and classification algorithms that are best suited for archival VAST light-curve classification. We perform our experiments on light-curve simulations of eight source types and achieve best-case performance of approximately 90% accuracy. We note that classification performance is most influenced by light-curve characterization rather than classifier algorithm.