The particle track reconstruction based on deep learning neural networks

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.Comment: 8 pages, 3 figures, CHEP 2018, the 23rd International Conference on Computing in High Energy and Nuclear Physics, Sofia, Bulgaria on July 9-13, 2018. arXiv admin note: text overlap with arXiv:1811.0600

PiggyBac transposon tools for recessive screening identify B-cell lymphoma drivers in mice.

B-cell lymphoma (BCL) is the most common hematologic malignancy. While sequencing studies gave insights into BCL genetics, identification of non-mutated cancer genes remains challenging. Here, we describe PiggyBac transposon tools and mouse models for recessive screening and show their application to study clonal B-cell lymphomagenesis. In a genome-wide screen, we discover BCL genes related to diverse molecular processes, including signaling, transcriptional regulation, chromatin regulation, or RNA metabolism. Cross-species analyses show the efficiency of the screen to pinpoint human cancer drivers altered by non-genetic mechanisms, including clinically relevant genes dysregulated epigenetically, transcriptionally, or post-transcriptionally in human BCL. We also describe a CRISPR/Cas9-based in vivo platform for BCL functional genomics, and validate discovered genes, such as Rfx7, a transcription factor, and Phip, a chromatin regulator, which suppress lymphomagenesis in mice. Our study gives comprehensive insights into the molecular landscapes of BCL and underlines the power of genome-scale screening to inform biology

Simulation of the GEM detector for BM@N experiment

The Gas Electron Multiplier (GEM) detector is one of the basic parts of the BM@N experiment included in the NICA project. The simulation model that takes into account features of signal generation process in an ionization GEM chamber is presented in this article. Proper parameters for the simulation were extracted from data retrieved with the help of Garfield++ (a toolkit for the detailed simulation of particle detectors). Due to this, we are able to generate clusters in layers of the micro-strip readout that correspond to clusters retrieved from a real physics experiment

Track Reconstruction in the BM@N Experiment

The BM@N experiment (Dubna, JINR) within the NICA complex is a running fixed target experiment. This paper mainly discusses the software support of the experimen and presents the status of the development and testing algorithms to be used for the reconstruction of heavy-ion collisions. Algorithms of reconstruction are described and tested with Monte Carlo input demonstrating a reasonable level of quality assurance. First results on particle identification being a good probe to estimate quality of reconstruction are also mentioned

Catch and Prolong: recurrent neural network for seeking track-candidates

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 and GEM detectors due to 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. We introduce a renewed method that is a significant improvement of our previous two-stage approach based on hit preprocessing using directed K-d tree search followed a deep neural classifier. We combine these two stages in one by applying recurrent neural network that simultaneously determines whether a set of points belongs to a true track or not and predicts where to look for the next point of track on the next coordinate plane of the detector. We show that proposed deep network is more accurate, faster and does not require any special preprocessing stage. Preliminary results of our approach for simulated events of the BM@N GEM detector are presented

Catch and Prolong: recurrent neural network for seeking track-candidates

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 and GEM detectors due to 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. We introduce a renewed method that is a significant improvement of our previous two-stage approach based on hit preprocessing using directed K-d tree search followed a deep neural classifier. We combine these two stages in one by applying recurrent neural network that simultaneously determines whether a set of points belongs to a true track or not and predicts where to look for the next point of track on the next coordinate plane of the detector. We show that proposed deep network is more accurate, faster and does not require any special preprocessing stage. Preliminary results of our approach for simulated events of the BM@N GEM detector are presented

Track Reconstruction in the BM@N Experiment

The BM@N experiment (Dubna, JINR) within the NICA complex is a running fixed target experiment. This paper mainly discusses the software support of the experimen and presents the status of the development and testing algorithms to be used for the reconstruction of heavy-ion collisions. Algorithms of reconstruction are described and tested with Monte Carlo input demonstrating a reasonable level of quality assurance. First results on particle identification being a good probe to estimate quality of reconstruction are also mentioned

BM@N Tracking with Novel Deep Learning Methods

Three deep tracking methods are presented for the BM@N experiment GEM track detector, which differ in their concepts. The first is a two-stage method with data preprocessing by a directional search in the k-d tree to find all possible candidates for tracks, and then use a deep recurrent neural network to classify them by true and ghost tracks. The second end-to-end method used a deep recurrent neural network to extrapolate the initial tracks, similar to the Kalman filter, which learns necessary parameters from the data. The third method implements our new attempt to adapt the neural graph network approach developed in the HEP.TrkX project at CERN to GEM-specific data. The results of applying these three methods to simulated events are presented

BM@N Tracking with Novel Deep Learning Methods

Three deep tracking methods are presented for the BM@N experiment GEM track detector, which differ in their concepts. The first is a two-stage method with data preprocessing by a directional search in the k-d tree to find all possible candidates for tracks, and then use a deep recurrent neural network to classify them by true and ghost tracks. The second end-to-end method used a deep recurrent neural network to extrapolate the initial tracks, similar to the Kalman filter, which learns necessary parameters from the data. The third method implements our new attempt to adapt the neural graph network approach developed in the HEP.TrkX project at CERN to GEM-specific data. The results of applying these three methods to simulated events are presented