Research

Our approach consists in splitting the workflow leading to measurements and NP searches in tasks, organized in 4 work packages (WPs), each of which we aim at optimizing with ML techniques; a fifth WP is devoted to general ML applications and insight on ML algorithms:

WP1: THEORY INPUTS

• Extraction of theory inputs: SM parameters and Parton Distribution Functions;
• Simulation of MC events beyond LO;
• Inclusion of theory and detector effects: showering, hadronization and detector response;

WP2: DATA ACQUISITION

• Implementation of algorithms for online triggers, taggers, anomaly detection and data quality monitoring;
• Implementation of algorithms for offline analysis through object reconstruction, jet clustering, boosted object tagging, particle flow, etc.

WP3: DATA ANALYSIS AND STATISTICAL INFERENCE

• Perform statistical inference within and beyond the SM through several techniques with varying level of model dependence: anomaly detection, statistical learning, parametrized neural networks, model  dependent searches, etc.;

WP4: RESULTS PRESENTATION, DISTRIBUTION AND PRESERVATION

• Present results delivering full likelihood (or even full statistical model) information and by publishing enough information to allow for preservation and reinterpretation;

WP5: GENERAL ML TOOLS AND OTHER STUDIES

• Develop general ML tools such as physics inspired evaluation metrics and new sampling and integration techniques suitable for different tasks;
• Implement physics informed algorithms that exploit symmetries or properties of the data;
• Study general properties of density estimation and generative models performances;
• Emulation of self-consistent mean field as a function of particle/nucleon positions to accelerate nuclear interaction models of interest for medical applications;