Q3 2017:
One of the key aims of the project is to enable fully flexible data processing. What this means in reality is that for every collision that takes place in the detector, to be most efficient we want to only save the particles and sub-detector data that will be used for measurements.
A use case has also been implemented to select decay signatures of the form of the Bd->KS μμ Χ in real time, where X denotes more particles consistent with originating from the B decay vertex. This allows us to analyse one of the signature analyses of the TNT project, namely Bd->K*(->KS π0)μμ in real time.
Q4 2017:
For the creation of our machine learning classifiers, an analyst may have a preference that is not always supported for execution in real time. A toolkit was therefore created such that a drone artificial neural network could learn the properties of an analysts preferred neural network and then be used in the real-time framework.
In the paper, we created 2 datasets consisting of a beauty decay and a charm decay. We then trained a neural network with the SciKit-Learn package. A separate drone neural network was created and taught the properties of the SciKit-Learn neural network, demonstrating a machine learning from another machine.
Q1 2018:
An unexpected rerun of the offline step at the end of 2017 gave the opportunity to put in some improvements to the isolation of Bd->KS μμ Χ. While this rerun was taking place, time was available to perform a measurement of a so-called golden mode for LHCb: Bs->φφ.
Q2 2018:
In the HEPDrone project, together with Konstantin Gizdov, another example was prepared to go with the B physics example presented in Q4 2017 (work ongoing to make the tools more user friendly). In the new example, a simple jet classifier was made using Keras.
In preparation for 2018 data taking, we also put in place new neural network based classifiers to the real-time selection of LHCb making use of the HEPDrone tools to port the model to the LHCb production framework. The improved performance of the neural network allowed us to expand the mass range from just looking at the B meson window to a 15GeV wide window enabling sensitivity to axions.
Q3 2018:
With the use of a package known as snakemake, work began to automate the analysis pipeline for the analysis of the Bs→φφ decay (see Q1 2018). Q3 2018 also saw the Tau lepton workshop come to Amsterdam. My talk gave an update on the most recent results of lepton flavour non-universality, i. e. the amount at which decays to electrons (found in atoms around us) and the heavier versions (muons and tauons) differ from each other.
Q4 2018:
The snakemake package (see previous quarter), was used to expand the automated analysis chain from the 8 steps in the previous quarter, to 14 now. At the time of writing, the analysis steps from dataset preparation to final results and plots are automated, in addition to a portion of the systematic studies. Systematic studies are checks of how the result changes due to certain choices during the analysis. It is a broad term that encompasses many cross-checks.
The HEPDrone package has now been renamed to be NNDrone and has now been incorporated in the Scikit-HEP family of packages. The features of NNDrone have been used to select signatures of the X→γγ decay in real time, where X can be a Bs meson or a new undiscovered particle. A draft of a document known as a public note (freely available peer-reviewed CERN documentation of a feature or study) has been drafted and will be available shortly.
Q1 2019:
The automation has now been taken to a new level and has increased the number of steps to over 70.
Additionally, the public note envisioned in the previous quarter on the use of NNDrone to select signatures of the X→γγ decay in real time has been changed to be a publication, with the addition of sensitivity studies for various levels of LHCb data. For the discovery of a new undiscovered particle (known as an axion-like particle), a single year of LHCb data taking is enough to make a world-leading contribution.
Dissemination of codebase and packages.
NNDrone: The package is freely available on github and is part of the SciKit-HEP group of packages.
Dissemination to the wider public
The dissemination of information to the wider public consists of three separate acitvities:
- The fellow has participated in the European Researchers Night Marie Curie presentations in Rotterdam on 28th September 2018.
-
https://h2020-tnt.com(öffnet in neuem Fenster).
- An online video is also available describing the workings of the NNDrone package, that was provided in a talk at the Inter-Experimental Machine Learning Workshop at CERN during 2018. The video was broadcasted live online and a recording may be seen by any member of the public at
https://cds.cern.ch/record/2313025?ln=en(öffnet in neuem Fenster).
