1,635 research outputs found
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
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