An overview of scientific program of the 2024 edition of the BND School is shown in this page. The aim is to have a total of 28 lectures/exercises (~3 per day), where the Saturday afternoon and Sunday are free. Additionally the students will work on a project in small teams throughout the school (1 slot per day).
The typical day structure is:
09:00 - 10:30: Lecture
Coffee Break
11:00 - 12:30: Lecture
Lunch Break
14:00 - 15:30: Lecture
Coffee Break
16:00 - 17:30: Lecture / Project
The topics which will be discussed during the school are listed below.
A condensed lecture plan can be found here.
Lecture content (TBC)
- QCD and Monte Carlo event generators (Dr. Marius Wiesemann)
- Fixed Order QCD calculations (Prof. Ben Page)
- Theory motivation for future particle colliders, including HL-LHC (Prof. Tiann-Tevong You)
- Accelerator Physics and Challenges for Future Colliders (Dr. Daniel Schulte)
- Neutrino Physics (Prof. Albert de Roeck / Prof. Richard Ruiz)
- AI for HEP (Dr. Ramon Winterhalder)
- Dark Matter Theory & Experiment (Dr. Gaétan Facchinetti / Dr. Maxime Pierre)
- Modelling and data analysis in GW science (Prof. Elena Cuoco / Prof. Massimiliano Razzano)
Students projects
- Scale Variations as Theoretical Uncertainties
- Gravitational-wave data analysis and matched filtering
- Luminosity measurement at the LHC
- Towards a future collider
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Scale Variations as Theoretical Uncertainties
AAny experimental analysis at the LHC requires the assessment of uncertainties in the theoretical modelling coming from neglected higher orders in perturbation theory. These are usually estimated by allowing some unphysical scale, such as the factorisation or renormalisation scale, to vary around a chosen central value.
In this project, the students will generate theoretical predictions for top-quark-pair production distributions at LHC by using the public tool MATRIX.
Several observables will be computed by using different values for the central scales and evaluating, in each case, the scale variation bands. The project aims to provide a deeper understanding of the meaning of scale variation bands as theoretical uncertainties and of the reasoning behind the choice of their central value. -
Gravitational-wave data analysis and matched filtering
BIn this project you will carry out data analysis with simulated and real gravitational-wave data. You will first develop an algorithm called “matched filtering” used for detecting modelled gravitational-wave signals from noisy data. You will demonstrate its use on simple signals – with a leading order “Newtonian chirp” waveform injected within simulated “Gaussian white” noise. Next you study data stretches from public data from the recent observing runs of LIGO and Virgo. You will estimate the statistical properties of the data, and will study interesting stretches of data which include the signal. You will finally extend your algorithm to work with real data as best as possible. Ability to code in Python is a prerequisite. You will use publicly available Python libraries such as gwpy.
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Luminosity measurement at the LHC
CLuminosity is a measure for the number of collisions that take place in a collider experiment. The higher the luminosity, the more collisions of a process of interest will take place. In this project, we will measure the instantaneous and integrated luminosity of the CMS experiment. To this end, we will analyze the measured rates with several dedicated luminosity detectors and apply the Van-der-Meer scan technique to derive the absolute luminosity scale. The data analysis code will be developed using public python packages.
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Towards a future collider
DThe future collider experiments will offer a unique opportunity to explore the fundamental interactions and search for new-physics phenomena at the high-energy frontier. Within the proposed future-collider project, the students will become familiar with the main concepts of the physics analysis at the Future Circular Collider (FCC). Through the development of the dedicated selection criteria, the students will search for new physics, skillfully hidden in the beforehand generated pseudo-data of collision events. The project will illustrate the characteristic features of the production of various new particles predicted in beyond the standard model theories and possible ways to identify them in data. To big FCC discoveries with minimal coding! The only prerequisite is to have Python (>=3.8) installed.
