An Active Learning application in a dark matter search with ATLAS PanDA and iDDS

Many theories of Beyond Standard Model (BSM) physics feature multiple BSM particles. Generally, these theories live in higher dimensional phase spaces that are spanned by multiple independent BSM parameters such as BSM particle masses, widths, and coupling constants. Fully probing these phase spaces to extract comprehensive exclusion regions in the high dimensional space is challenging. Constraints on person-power and computational resources can limit analyses to focus only on one- or two-dimensional regions of the relevant parameter spaces. Nonetheless, fully comprehensive exclusion regions, even for complex theory phase spaces, are generally desirable to maximize the utility of such BSM searches. We are presenting an advanced analysis workflow composed of an integrated pipeline and active learning that enables such a comprehensive exclusion. The integrated pipeline automatically executes all steps of an analysis from event generation through to limit setting. Active learning is a technique to guide the sampling of the multi-dimensional phase space to find the exclusion contours in an iterative process: the sampled theory phase space points are selected such that the vicinity of the exclusion region is prioritized, reducing the sampling density in the less interesting areas. As a result, it allows searches over a larger space at the same precision, or reduces the resources required for the same search-space. We will present the implementation of the workflow with the Production and Distributed Analysis system (PanDA system) and intelligent Data Delivery Service (iDDS) in ATLAS, and showcase its abilities and utility in an extended search for a dark Z-boson using events with four-lepton final states.