Machine Learning on data with sPlot background subtraction

Data analysis in high energy physics often deals with data samples consisting of a mixture of signal and background events. The sPlot technique is a common method to subtract the contribution of the background by assigning weights to events. Part of the weights are by design negative. Negative weights lead to the divergence of some machine learning algorithms training due to absence of the lower bound in the loss function. In this paper we propose a mathematically rigorous way to train machine learning algorithms on data samples with background described by sPlot to obtain signal probabilities conditioned on observables, without encountering negative event weight at all. This allows usage of any out-of-the-box machine learning methods on such data

Numerical optimization for Artificial Retina Algorithm

High-energy physics experiments rely on reconstruction of the trajectories of particles produced at the interaction point. This is a challenging task, especially in the high track multiplicity environment generated by p-p collisions at the LHC energies. A typical event includes hundreds of signal examples (interesting decays) and a significant amount of noise (uninteresting examples). This work describes a modification of the Artificial Retina algorithm for fast track finding: numerical optimization methods were adopted for fast local track search. This approach allows for considerable reduction of the total computational time per event. Test results on simplified simulated model of LHCb VELO (VErtex LOcator) detector are presented. Also this approach is well-suited for implementation of paralleled computations as GPGPU which look very attractive in the context of upcoming detector upgrades

Filaments in Carbonaceous Meteorites: Mineral Crystals, Modern Bio-Contaminants or Indigenous Microfossils of Trichomic Prokaryotes?

Environmental (ESEM) and Field Emission Scanning Electron Microscopy (FESEM) investigations have resulted in the detection of a large number of complex filaments in a variety of carbonaceous meteorites. Many of the filaments were observed to be clearly embedded the rock matrix of freshly fractured interior surfaces of the meteorites. The high resolution images obtained combined with tilt and rotation of the stage provide 3-dimensional morphological and morphometric data for the filaments. Calibrated Energy Dispersive X-ray Spectroscopy (EDS) and 2-D elemental X-ray maps have provided information on the chemical compositions of the filaments and the minerals of the associated meteorite rock matrix. These observations are used to evaluate diverse hypotheses regarding the possible abiotic or biogenic nature of the filaments found embedded in these meteorites

Deep learning for inferring cause of data anomalies

Daily operation of a large-scale experiment is a resource consuming task, particularly from perspectives of routine data quality monitoring. Typically, data comes from different sub-detectors and the global quality of data depends on the combinatorial performance of each of them. In this paper, the problem of identifying channels in which anomalies occurred is considered. We introduce a generic deep learning model and prove that, under reasonable assumptions, the model learns to identify 'channels' which are affected by an anomaly. Such model could be used for data quality manager cross-check and assistance and identifying good channels in anomalous data samples. The main novelty of the method is that the model does not require ground truth labels for each channel, only global flag is used. This effectively distinguishes the model from classical classification methods. Being applied to CMS data collected in the year 2010, this approach proves its ability to decompose anomaly by separate channels.Comment: Presented at ACAT 2017 conference, Seattle, US