Nettsider med emneord «LHC»
Når LHC på CERN blir oppgradert til høyere intensitet må mange av detektorene i ATLAS byttes innen 2027. I Norge deltar vi i utviklingen av pixel detektorer og vi får stadig nye sensorer som skal bygges til moduler og testes.
Model independent searches for new physics are proposed as a way to be sensitive to various scenarios of new physics theories in final states with e.g. leptons recorded with the ATLAS detector.
Gravity as we know it is negligible at the subatomic level. The addition of n space dimensions affects the behavior of the gravitational force, changing from 1/r2 to 1/r(2+n) , thus enhancing its strength at very short distances r. A way to search for signatures of gravity at the LHC, and thus reveal the existence of microscopic space dimensions, is to look for graviton excitations and/or microscopic black holes. Both would decay into SM particles, measurable in particle detectors such as ATLAS.
Although the first measurements of the properties of the Higgs boson discovered at CERN in 2012 are consistent with the Standard Model (SM), the uncertainties are large and there are many physically motivated models that would give small deviations from the SM predictions. The group in Oslo works on the decay channel of the Higgs boson to two photons.
The main objective is to enable the study of fundamental particles and interactions and the characterizaton of high-temperature strongly interacting matter at the extreme energies and collision rates of the upgraded Large Hadron Collider (HL-LHC) at CERN in the years 2017-2037.
We study the universe at the smallest distance scales (corresponding to the highest energy scales). After the Higgs boson was discovered at CERN in 2012, one of the hottest goals of our research is to reveal the nature of dark matter.
Do new fundamental forces show up at the LHC the way the Z and Higgs bosons did? According to superstring theories, which propose to unify all fundamental forces, including gravity, there is room for new forces to be mediated by new gauge bosons, known as Z’ and W’. The W’ boson is also predicted by theories aiming at restoring parity (left-right) symmetry at high energies. This work consists of: (i) a detailed study and implementation into MC generators of various theories beyond the SM, (ii) an analysis of ATLAS data, taken at the highest available energies, and a comparison to simulation data. You will make use of one of the following processes:
Thesis presented 2020
Thesis presented in 2020
Thesis presented in 2016
Thesis presented in 2019
Symmetries play a crucial role in physics. Supersymmetry (SUSY) relates integer spin particles (bosons) and half-integer spin particles (fermions). It allows unification of the electroweak and strong interactions, proposes dark matter candidates, and predicts five Higgs bosons (3 neutral and 2 charged ones). Processes of interest involve superpartners of the leptons (superpartners have a "~" above the particle), of the gauge and Higgs boson(s), as well as a dark matter particle, which is predicted to be the lightest supersymmetric particle (LSP).
Ultra-relativistic heavy-ion collisions offer a unique opportunity to study the nuclear phase diagram at high temperatures and densities. The matter under such extreme conditions probably has existed in the early Universe within the first few fm/c after the Big Bang. Therefore, it is very tempting to investigate the properties of the Little Big Bang in the laboratory, and to search for a new state of matter, predicted by the fundamental theory of strong interactions - Quantum Chromodynamics (QCD), namely, a plasma of deconfined quarks and gluons or quark-gluon plasma (QGP).
To describe such complex phenomenon one has to rely on phenomenological models, which can be subdivided into macroscopic, i.e. thermal and hydrodynamic, and microscopic Monte Carlo models, incorporating partonic and hadronic degrees of freedom in a consistent fashion. These models are indispensable for the comparison with the experimental data coming from current heavy-ion accelerators and for planning the new machines such as FAIR at GSI, NICA at JINR, and FCC at CERN, which is widely discussed nowadays.
In Oslo we use several MC models at our disposal, namely, Ultra-relativistic Quantum Molecular Dynamics (UrQMD) and Quark-Gluon String Model (QGSM) for description of various hadronic and nuclear collisions, and HYDrodynamics with JETs (HYDJET++) model for simulation of heavy-ion collisions.
Are you interested in sharing ATLAS data, research excitement and possibly discoveries with other students, and explaining to them modern physics concepts? Join the Path for education, research and discovery! The ambition to bring to the “classrooms” important LHC discoveries is already realized using the discovery of the Higgs boson in 2012. Approximately 10% of the ATLAS discovery data were made available for students to search themselves for the Higgs boson. Promises of new discoveries in the 13 TeV LHC era and opportunities offered by the CERN open data portal have triggered new educational materials.
Computing and software are crucial parts of the LHC physics experiments. The NorduGrid Advanced Resource Connector (ARC) middleware increases in popularity due its simplistic design and ease of deployment. This makes it the preferred choice of middleware for new and many existing sites particularly in Europe and Asia. ARC and its Control Tower allow seamless access to heterogeneous resources: Grid, High Performance Computers and Clouds. Moreover, ATLAS@home, based on BOINC and ARC, allow to access opportunistic resources made of personal computers.
The requirements imposed on software during the coming LHC runs will be as stringent as those on the computing resources. The data throughput that will have to be achieved exceeds anything that our community has managed to date. Such performance can only be attained by combining a number of techniques - multi-threading and parallel processing of events - as well as novel algorithms and optimization of existing software.
The importance of multi-variate analysis or "Machine Learning” in High Energy Physics continues to increase, for applications as diverse as reconstruction, physics analysis, data quality monitoring and distributed computing.
Sondre Vik Furuseth, Rosseland Centre for Solar Physics, UiO.