In the course of the last 40 years the standard model (SM) has received increasing verifications. There are, however, compelling reasons to believe the SM is not a complete theory but only an “effective” low-energy one, breaking down at energies higher than those probed so far. The Higgs boson may constitute the door through which a whole class of new phenomena and a deeper understanding of Nature can be accessed. Experiments are also looking for new particles predicted in many SM extensions, but it is equally important to pursue a model-independent approach, and search for any rare new processes that may be hiding in the high-energy collisions. It is therefore necessary to broaden the ways of conducting searches for new physics.
The advent of machine learning (ML) techniques has brought dramatic changes to the potential of data analysis. This ITN aimed to tackle the two big challenges mentioned above with cutting-edge ML tools, optimizing them as well as developing new ones. This program gives us a chance to train a new generation of data scientists; Physics has always been a breeding ground for skilled individuals. Also, HEP has a history of developing tools that later become of exceptional importance for society as a whole (e.g. the internet, proton therapy). Hence we claim that research in HEP, employing ML techniques now available, may produce new important advancements for tomorrow's society as a whole.
The overall objectives of the project have been:
O1: Develop and improve advanced ML tools for data analysis in particle physics.
O2: Bring together academic and non-academic partners to create innovative training opportunities for talented students in statistical learning, computational tools, and data science.
O3: Deepen our knowledge of Nature by providing answers to fundamental physics questions with the LHC.
After the conclusion of operation of the ITN, we observe that we fully succeeded in achieving objective O1. Indeed, we substantially improved the performance of existing ML tools in use, and we delivered entirely new tools that promise ground-breaking advances in the quality of the data analysis and the overall extraction of physics knowledge from the available data. These claims are supported by the produced deliverables of work packages 1, 2, 3, and 4.
For O2, we produced new high-level training opportunities to our students and to others who attended our open events. In particular, the involved ESRs have been able to include in their training plan a number of excellent workshops, schools, and lectures offered both from academic and non-academic instructors. Another notable action is the quite successful interaction with industrial partners, allowing a perfect synergy with the work plan of the ESRs (YANDEX secondments were appreciated for the insight offered by personnel involved in applying the new software technologies to fundamental research) and in others provided training opportunities in real-life applications of ML.
Concerning objective O3, the investigation of fundamental physics proceeds by both incremental and disruptive advancements. The latter are unpredictable and exceedingly rare. While the LHC has been producing a large number of exquisite scientific results, in many cases benefitting from the very work of our ESRs, we cannot in earnest claim that we could answer fundamental physics questions in radical new ways. What we certainly have achieved is a strengthening and an improvement of the potential of the ATLAS and CMS experiments to pursue that long-term goal.
In conclusion, we feel that the ITN has contributed significantly to physics advancements, has formed in an optimal way a cohort of bright young researchers who are now starting a career in research and outside academia, and has created new ML tools which promise to strengthen our capability of extracting more information from data both in science and in industry.
