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Register a new userA density-based clustering algorithm for the CYGNO data analysis
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Time Projection Chambers (TPCs) working in combination with Gas Electron Multipliers (GEMs) produce a very sensitive detector capable of observing low energy events. This is achieved by capturing photons generated during the GEM electron multiplication process by means of a high-resolution camera. The CYGNO experiment has recently developed a TPC Triple GEM detector coupled to a low noise and high spatial resolution CMOS sensor. For the image analysis, an algorithm based on an adapted version of the well-known DBSCAN was implemented, called iDBSCAN. In this paper a description of the iDBSCAN algorithm is given, including test and validation of its parameters, and a comparison with DBSCAN itself and a widely used algorithm known as Nearest Neighbor Clustering (NNC). The results show that the adapted version of DBSCAN is capable of providing full signal detection efficiency and very good energy resolution while improving the detector background rejection.
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CYGNO is a project realising a cubic meter demonstrator to study the scalability of the performance of the optical approach for the readout of large-volume, GEM-equipped TPC. This is part of the CYGNUS proto-collaboration which aims at constructing a network of underground observatories for directional Dark Matter search. The combined use of high-granularity sCMOS and fast sensors for reading out the light produced in GEM channels during the multiplication processes was shown to allow on one hand to reconstruct 3D direction of the tracks, offering accurate energy measurements and sensitivity to the source directionality and, on the other hand, a high particle identification capability very useful to distinguish nuclear recoils. Results of the performed R&D and future steps toward a 30-100 cubic meter experiment will be presented.
The design of the project named CYGNO is presented. CYGNO is a new proposal supported by INFN, the Italian National Institute for Nuclear Physics, within CYGNUs proto-collaboration (CYGNUS-TPC) that aims to realize a distributed observatory in underground laboratories for directional Dark Matter (DM) search and the identification of the coherent neutrino scattering (CNS) from the Sun. CYGNO is one of the first prototypes in the road map to 100-1000 m^3 of CYGNUs and will be located at the National Laboratory of Gran Sasso (LNGS), in Italy, aiming to make significant advances in the technology of single phase gas-only time projection chambers (TPC) for the application to the detection of rare scattering events. In particular it will focus on a read-out technique based on Micro Pattern Gas Detector (MPGD) amplification of the ionization and on the visible light collection with a sub-mm position resolution sCMOS (scientific COMS) camera. This type of readout - in conjunction with a fast light detection - will allow on one hand to reconstruct 3D direction of the tracks, offering accurate sensitivity to the source directionality and, on the other hand, a high particle identification capability very useful to distinguish nuclear recoils.
The aim of the CYGNO project is the construction and operation of a 1~m$^3$ gas TPC for directional dark matter searches and coherent neutrino scattering measurements, as a prototype toward the 100-1000~m$^3$ (0.15-1.5 tons) CYGNUS network of underground experiments. In such a TPC, electrons produced by dark-matter- or neutrino-induced nuclear recoils will drift toward and will be multiplied by a three-layer GEM structure, and the light produced in the avalanche processes will be readout by a sCMOS camera, providing a 2D image of the event with a resolution of a few hundred micrometers. Photomultipliers will also provide a simultaneous fast readout of the time profile of the light production, giving information about the third coordinate and hence allowing a 3D reconstruction of the event, from which the direction of the nuclear recoil and consequently the direction of the incoming particle can be inferred. Such a detailed reconstruction of the event topology will also allow a pure and efficient signal to background discrimination. These two features are the key to reach and overcome the solar neutrino background that will ultimately limit non-directional dark matter searches.
Measuring graph clustering quality remains an open problem. To address it, we introduce quality measures based on comparisons of intra- and inter-cluster densities, an accompanying statistical test of the significance of their differences and a step-by-step routine for clustering quality assessment. Our null hypothesis does not rely on any generative model for the graph, unlike modularity which uses the configuration model as a null model. Our measures are shown to meet the axioms of a good clustering quality function, unlike the very commonly used modularity measure. They also have an intuitive graph-theoretic interpretation, a formal statistical interpretation and can be easily tested for significance. Our work is centered on the idea that well clustered graphs will display a significantly larger intra-cluster density than inter-cluster density. We develop tests to validate the existence of such a cluster structure. We empirically explore the behavior of our measures under a number of stress test scenarios and compare their behavior to the commonly used modularity and conductance measures. Empirical stress test results confirm that our measures compare very favorably to the established ones. In particular, they are shown to be more responsive to graph structure and less sensitive to sample size and breakdowns during numerical implementation and less sensitive to uncertainty in connectivity. These features are especially important in the context of larger data sets or when the data may contain errors in the connectivity patterns.
The high instantaneous luminosities expected following the upgrade of the Large Hadron Collider (LHC) to the High Luminosity LHC (HL-LHC) pose major experimental challenges for the CMS experiment. A central component to allow efficient operation under these conditions is the reconstruction of charged particle trajectories and their inclusion in the hardware-based trigger system. There are many challenges involved in achieving this: a large input data rate of about 20--40 Tb/s; processing a new batch of input data every 25 ns, each consisting of about 15,000 precise position measurements and rough transverse momentum measurements of particles (stubs); performing the pattern recognition on these stubs to find the trajectories; and producing the list of trajectory parameters within 4 $mu,$s. This paper describes a proposed solution to this problem, specifically, it presents a novel approach to pattern recognition and charged particle trajectory reconstruction using an all-FPGA solution. The results of an end-to-end demonstrator system, based on Xilinx Virtex-7 FPGAs, that meets timing and performance requirements are presented along with a further improved, optimized version of the algorithm together with its corresponding expected performance.
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