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:
(CERN and EPFL), David Rousseau
(LAL-Orsay, FR), Gian Michele Innocenti
(CERN), Lorenzo Moneta
(CERN), Loukas Gouskos
(CERN), DrPietro Vischia
(Universite Catholique de Louvain (UCL) (BE)), Riccardo Torre
(CERN), Simon Akar
(University of Cincinnati (US))
Anomaly detection at L1 Trigger with Autoencoders25m
We discuss how to adapt and deploy anomaly detection strategies based on Deep Autoenconders on atypical real-time event selection system at the Large Hadron Collider. Considering as a benchmark an inclusive data stream, pre-filtered requiring the presence of one lepton, we discuss different strategies to detect new physics events as anomalies. Using the hls4ml library, we show how resource consumption and latency match the constraints of a typical LHC real-time environment.
We show how to deal with uncertainties on the Reference Model predictions in an agnostic new physics search strategy based on artificial neural networks. Our approach builds directly on the profile likelihood treatment of uncertainties as nuisance parameters for hypothesis testing that is routinely employed in high-energy physics. After presenting the conceptual foundations of our method, we illustrate all aspects of its implementation and extensively study its performances on a toy one-dimensional problem. We show how to implement it in a multivariate setup by studying the impact of two typical sources of experimental uncertainties in two-body final states at the LHC.
(Universita e INFN, Padova (IT))
Boosting new physics sensitivity with Variational Autoencoders25m
We show how an anomaly detection algorithm could be integrated in a typical search for new physics in events with jets at the CERN Large Hadron Collider (LHC). We assume that an anomaly detection algorithm is given, trained to identify rare jet types, such as jets originating from the decay of a highly boosted massive particle. We show how this algorithm could be integrated in a search without disrupting the background-estimate strategy while enhancing the sensitivity to new physics. As an example, we consider convolutional variational autoencoders (VAEs) applied to dijet events. The proposed procedure can be generalized to any final state with jets. Once applied to real data, it could contribute to extend the sensitivity of the LHC experiments to previously uncovered new physics scenarios, e.g., broad-resonance and non-resonant jet production from new physics processes.
Kinga Anna Wozniak
(University of Vienna (AT))