One of the most important problems of data processing in high energy and nuclear physics is the event reconstruction. Its main part is the track reconstruction procedure which consists in looking for all tracks that elementary particles leave when they pass through a detector among a huge number of points, so-called hits, produced when flying particles fire detector coordinate planes. Unfortunately, the tracking is seriously impeded by the famous shortcoming of multiwired, strip in GEM detectors due to the appearance in them a lot of fake hits caused by extra spurious crossings of fired strips. Since the number of those fakes is several orders of magnitude greater than for true hits, one faces with the quite serious difficulty to unravel possible track-candidates via true hits ignoring fakes. On the basis of our previous two-stage approach based on hits preprocessing using directed K-d tree search followed by a deep neural classifier we introduce here two new tracking algorithms. Both algorithms combine those two stages in one while using different types of deep neural nets. We show that both proposed deep networks do not require any special preprocessing stage, are more accurate, faster and can be easier parallelized. Preliminary results of our new approaches for simulated events are presented.
A method for clustering of large amounts of data is presented which is a sequenced composition of two algorithms: the former builds a partition of input space into Voronoi regions and the latter partitions them. First, a model of clusters as high density regions in input space is presented, then it is shown how a Voronoi partition and it's topological map (a) can be build and (b) used as a low complexity approximation of the input space. During the (b) step, the usage of "watershed" algorithm is presented which has been pre viously used for image segmentation, but it is its first application to a data space partition that is proposed by the authors.
A tool for problem of inversion of geopotential fields has been developed for GIS INTEGRO under the name of “assembly method”, a family of which is currently gaining attention. In the article the capabilities of this specific implementation are being examined in the application to inversion of magnetic fields for regional 3D-modelling of large oil and gas perspective territories. The available methods of control and regularization of minimization of field residual are investigated using synthetic model data in the application to magnetic data and their differences to more well-understood gravity fields. In particular, a value of “depth priority” has the similar effect on model evolution as in the case of gravity problem, but the balanced value is larger. In the last section, an example of the method application is presented: the parameters of lithospheric source of satellite magnetic anomaly associated with Guli massif are investigated.
Various methods based on growing bodies are lately gaining attention in a context of inverse gravity problem that we call a family of “assembly methods”. A variant of method was adopted for GIS INTEGRO in original formulation that is fit for the problem of multiple bodies incorporated in an environment of varying density, in absolute densities (not density contrasts) that are however have to be a priori specified. Such formulation allowed the implementation of the method that is suitable for territory modeling in the regional scale. To workaround method’s instability a number of changes are proposed that consist of introduction of priority on atomic modifications, modification queue and assessment of model evolution instead of just the final result. The developed software allows processing of large grids (tens of millions of tiling elements) even on 5–8 year old desktops. Based on method approbation experience some insights and practice methods are presented. An application example is presented as part of work on modeling of Enisei-Khatanga regional depression territory.
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