In order to enable an iCal export link, your account needs to have an API key created. This key enables other applications to access data from within Indico even when you are neither using nor logged into the Indico system yourself with the link provided. Once created, you can manage your key at any time by going to 'My Profile' and looking under the tab entitled 'HTTP API'. Further information about HTTP API keys can be found in the Indico documentation.
Additionally to having an API key associated with your account, exporting private event information requires the usage of a persistent signature. This enables API URLs which do not expire after a few minutes so while the setting is active, anyone in possession of the link provided can access the information. Due to this, it is extremely important that you keep these links private and for your use only. If you think someone else may have acquired access to a link using this key in the future, you must immediately create a new key pair on the 'My Profile' page under the 'HTTP API' and update the iCalendar links afterwards.
Permanent link for public information only:
Permanent link for all public and protected information:
Data collected by the LHC experiments is valuable and finite. At the same time, the physics processes which are being searched for or measured, and the corresponding backgrounds predicted by the standard model have non-trivial and multi-dimensional underlying kinematics of the produced particles, and the space of observables is even further expanded by the interaction with and measurement by the detectors. This combined with the availability of large control samples from simulation or LHC data itself provides many opportunities for the use of machine learning to extend the sensitivity of searches and improve the precision of measurements at the LHC. I'll discuss the usage of machine learning techniques at the LHC experiments, covering both the reconstruction and identification of physics objects as well as usage in higher level physics analyses, with a particular focus on the discovery and measurements of the Higgs Boson in CMS. I'll also discuss possible uses of machine learning for numerical phase-space integration in Monte Carlo generators.