High precision is the next frontier for the Large Hadron Collider (LHC). It requires accurate and precise theoretical predictions. The goal of this project is twofold: establish a statistically solid framework for estimating the uncertainty on theoretical predictions, and producing more precise higher-order parton distribution functions (PDFs). Theoretical predictions are not exact, and carry therefore an uncertainty due to the approximate method (perturbation theory) used to compute physical observables. Assessing this uncertainty is not obvious, and standard techniques are not always reliable and lack of statistical interpretation, limiting their usability. HiPPiE@LHC will overcome these problems by proposing a Bayesian approach to assess theory uncertainties, which will make use of all the information available on the perturbative expansion and will provide a probability distribution for the unknown exact result. The approach will be similar to the Cacciari-Houdeau method, but will contain several conceptual and technical improvements, which will effectively make it more reliable. Then, new PDFs will be fitted from data using higher-order theoretical predictions, mostly based on all-order resummations, and including for the first time theory uncertainties. The computation of all the new ingredients needed to achieve this task will be part of the project. This also include the development of novel techniques (e.g., the extension of threshold resummation to fully differential level). These additions will increase the reliability and quality of the fit. The new PDFs will represent a substantial improvement over previous generations, and will be crucial for achieving higher precision in theoretical predictions. The outcome of this project will be a milestone for the High-Energy Physics community, and will open the door to high precision phenomenology in the next phase of the LHC.

Large international scientific collaborations will face in the near future unprecedented computing and data challenges. The analysis of multi-PetaByte datasets at CMS, ATLAS, LHCb and Alice, the four experiments at the Large Hadron Collider (LHC), requires a global federated infrastructure of distributed computing resources. The HL-LHC, the High Luminosity upgrade of the LHC, is expected to deliver 100 times more data than the LHC, with corresponding increase of event sizes, volumes and complexity. Modern techniques for big data analytics and machine learning (ML) are needed to cope with such unprecedented data stream. Critical areas that will strongly benefit from ML are data analysis, detector operation including calibration and monitoring, and computing operations. Aim of this project is to provide the LHC community with the necessary tools to deploy ML solutions through the use of open cloud technologies such as the INDIGO-DataCloud services. Heterogeneous technologies (systems based on multi-cores, GPUs, ...) and opportunistic resources will be integrated. The developed tools will be experiment-independent to promote the exchange of common solutions among the various LHC experiments. The benefits of such an approach will be demonstrated in a real world use case, the optimization of the computing operations for the CMS experiment. In addition, once available, the tools to deploy ML as a service can be easily transferred to other scientific domains that have the need to treat large data streams.