Comparing weak- and unsupervised methods for resonant anomaly detection

Anomaly detection techniques are growing in importance at the Large Hadron Collider (LHC), motivated by the increasing need to search for new physics in a model-agnostic way. In this work, we provide a detailed comparative study between a well-studied unsupervised method called the autoencoder (AE) and a weakly-supervised approach based on the Classification Without Labels (CWoLa) technique. We examine the ability of the two methods to identify a new physics signal at different cross sections in a fully hadronic resonance search. By construction, the AE classification performance is independent of the amount of injected signal. In contrast, the CWoLa performance improves with increasing signal abundance. When integrating these approaches with a complete background estimate, we find that the two methods have complementary sensitivity. In particular, CWoLa is effective at finding diverse and moderately rare signals while the AE can provide sensitivity to very rare signals, but only with certain topologies. We therefore demonstrate that both techniques are complementary and can be used together for anomaly detection at the LHC.

Mass Unspecific Supervised Tagging (MUST) for boosted jets

Jet identification tools are crucial for new physics searches at the LHC and at future colliders. We introduce the concept of Mass Unspecific Supervised Tagging (MUST) which relies on considering both jet mass and transverse momentum varying over wide ranges as input variables — together with jet substructure observables — of a multivariate tool. This approach not only provides a single efficient tagger for arbitrary ranges of jet mass and transverse momentum, but also an optimal solution for the mass correlation problem inherent to current taggers. By training neural networks, we build MUST-inspired generic and multi-pronged jet taggers which, when tested with various new physics signals, clearly outperform the variables commonly used by experiments to discriminate signal from background. These taggers are also efficient to spot signals for which they have not been trained. Taggers can also be built to determine, with a high degree of confidence, the prongness of a jet, which would be of utmost importance in case a new physics signal is discovered.

Research on the Teaching System Development of Maintenance Training for General-Purpose Electronic Equipment

Development maintenance training for electronic equipment is the important way to solve the difficult of electronic equipment maintenance and problem of iridescent method of maintenance training. The technical characteristics of the maintenance system for electronic equipment are analyzed in this paper. A maintenance training teaching system for electronic equipment with unity hardware and common software platform is researched. The hardware structure and the development method of its software are discussed in this paper. The teaching system possesses characters such as real physics signal，multi method of human-computer interaction ，well results of learning best，easy to development，etc. The teaching system is mainly composed of the following: motherboard, equipment operation panel, equipment component, lights and buttons etc. Its software is based on a common database. When we need development a teaching system for new equipment, we just add some data to the database using the development platform and need not modify its software. Practice shows that the teaching system uses a common hardware design and software platform, greatly reducing the development workload of a new teaching system using the architecture.

CHARMED MESON RECONSTRUCTION WITH THE PANDA DETECTOR

The PANDA detector will allow to investigate the charmonium region in [Formula: see text] annihilation processes at a energy range for which production cross sections of charmonium states are unknown, in particular for those above the [Formula: see text] threshold. Cross sections for these states are expected to be rather small, so that it has to be verified that PANDA can cope with small rates. Therefore, several reaction channels have been identified as "benchmark channels" and the PANDA performance has been investigated in Monte Carlo studies. The focus of the charmed benchmark channels is to show the physics performance of the planned apparatus and to investigate the feasibility of separating the physics signal from a huge hadronic background.