by Josh McFayden (Lawrence Berkeley National Lab), Clara Nellist (Radboud University)
Two recent ATLAS results, including a precise measurement of lepton flavour universality and the spectacular production of four top quarks, showcase the full range of the ATLAS physics potential
As CERN and its host-states gradually easen lockdown measures, activities in the accelerator complex and the experiments are now restarting to continue with major upgrades for Run 3 of the LHC.
In the 2020 edition of the LHCP conference, the ATLAS and CMS collaborations presented their latest results on new signatures for detecting the Higgs boson at CERN’s Large Hadron Collider.
As the LHC prepares to enter an era of long luminosity and high statistics, the use of heterogeneous computing resources could help to optimize resources and prepare for future eperiments.
The joint analysis preservation bootcamp offered to the participants a hands-on experience on making their analyses reproducible using state-of-the-art software.
Advanced deep neural networks can have multiple applications in the design of high-granularity calorimeters for future experiments while some of the offered solutions can find applications beyond particle physics.
The MSCA project AMVA4NewPhysics has made important contributions in developing advanced tools and training the next generation of scientists to work with vast amounts of data whether searching for new physics or developing industrial applications.
A collaborative effort between the ATLAS and CMS experiments with CERN's IT department enables rediscovering the Higgs boson within minutes using publicly available datasets and new computing tools.
Two recent ATLAS results, including a precise measurement of lepton flavour universality and the spectacular production of four top quarks, showcase the full range of the ATLAS physics potential 
As CERN and its host-states gradually easen lockdown measures, activities in the accelerator complex and the experiments are now restarting to continue with major upgrades for Run 3 of the LHC.
Discussing new tools for BSM search physics at colliders employing different machine-learning techniques.  (1).png)
In the 2020 edition of the LHCP conference, the 
ATLAS and CMS experiments employ deep-learning methods in searching for rare processes and long-lived particles.
As the LHC prepares to enter an era of long luminosity and high statistics, the use of heterogeneous computing resources could help to optimize resources and prepare for future eperiments.
The joint analysis preservation bootcamp offered to the participants a hands-on experience on making their analyses reproducible using state-of-the-art software.
Advanced deep neural networks can have multiple applications in the design of high-granularity calorimeters for future experiments while some of the offered solutions can find applications beyond particle physics. 
The MSCA project AMVA4NewPhysics has made important contributions in developing advanced tools and training the next generation of scientists to work with vast amounts of data whether searching for new physics or developing industrial applications.