From hard exclusive meson electroproduction to deeply virtual Compton scattering

We systematically evaluate observables for hard exclusive electroproduction of real photons and compare them to experiment using a set of Generalized Parton Distributions (GPDs) whose parameters are constrained by Deeply Virtual Meson Production data, nucleon form factors and parton distributions. The Deeply Virtual Compton Scattering amplitudes are calculated to leading-twist accuracy and leading order in QCD perturbation theory while the leptonic tensor is treated exactly, without any approximation. This study constitutes a check of the universality of the GPDs. We summarize all relevant details on the parametrizations of the GPDs and describe its use in the handbag approach of the aforementioned hard scattering processes. We observe a good agreement between predictions and measurements of deeply virtual Compton scattering on a wide kinematic range, including most data from H1, ZEUS, HERMES, Hall A and CLAS collaborations for unpolarized and polarized targets when available. We also give predictions relevant for future experiments at COMPASS and JLab after the 12 GeV upgrade.Comment: 37 pages, 12 figures v2 : fixed typos, updated to future published version (EPJC). v3 : fixed typo in Eq. (59

Consistent Feature Construction with Constrained Genetic Programming for Experimental Physics

A good feature representation is a determinant factor to achieve high performance for many machine learning algorithms in terms of classification. This is especially true for techniques that do not build complex internal representations of data (e.g. decision trees, in contrast to deep neural networks). To transform the feature space, feature construction techniques build new high-level features from the original ones. Among these techniques, Genetic Programming is a good candidate to provide interpretable features required for data analysis in high energy physics. Classically, original features or higher-level features based on physics first principles are used as inputs for training. However, physicists would benefit from an automatic and interpretable feature construction for the classification of particle collision events. Our main contribution consists in combining different aspects of Genetic Programming and applying them to feature construction for experimental physics. In particular, to be applicable to physics, dimensional consistency is enforced using grammars. Results of experiments on three physics datasets show that the constructed features can bring a significant gain to the classification accuracy. To the best of our knowledge, it is the first time a method is proposed for interpretable feature construction with units of measurement, and that experts in high-energy physics validate the overall approach as well as the interpretability of the built features.Comment: Accepted in this version to CEC 201

Embedded Constrained Feature Construction for High-Energy Physics Data Classification

International audienceBefore any publication, data analysis of high-energy physics experiments must be validated. This validation is granted only if a perfect understanding of the data and the analysis process is demonstrated. Therefore, physicists prefer using transparent machine learning algorithms whose performances highly rely on the suitability of the provided input features. To transform the feature space, feature construction aims at automatically generating new relevant features. Whereas most of previous works in this area perform the feature construction prior to the model training, we propose here a general framework to embed a feature construction technique adapted to the constraints of high-energy physics in the induction of tree-based models. Experiments on two high-energy physics datasets confirm that a significant gain is obtained on the classification scores, while limiting the number of built features. Since the features are built to be interpretable, the whole model is transparent and readable

PandaX-III: Searching for neutrinoless double beta decay with high pressure136^{136}Xe gas time projection chambers

International audienceSearching for the neutrinoless double beta decay (NLDBD) is now regarded as the topmost promising technique to explore the nature of neutrinos after the discovery of neutrino masses in oscillation experiments. PandaX-III (particle and astrophysical xenon experiment III) will search for the NLDBD of$^{136}$Xe at the China Jin Ping Underground Laboratory (CJPL). In the first phase of the experiment, a high pressure gas Time Projection Chamber (TPC) will contain 200 kg, 90%$^{136}$Xe enriched gas operated at 10 bar. Fine pitch micro-pattern gas detector (Microbulk Micromegas) will be used at both ends of the TPC for the charge readout with a cathode in the middle. Charge signals can be used to reconstruct the electron tracks of the NLDBD events and provide good energy and spatial resolution. The detector will be immersed in a large water tank to ensure ~5 m of water shielding in all directions. The second phase, a ton-scale experiment, will consist of five TPCs in the same water tank, with improved energy resolution and better control over backgrounds