scholarly journals GPU-based Clustering Algorithm for the CMS High Granularity Calorimeter

2020 ◽  
Vol 245 ◽  
pp. 05005
Author(s):  
Ziheng Chen ◽  
Antonio Di Pilato ◽  
Felice Pantaleo ◽  
Marco Rovere
Keyword(s):  
Phase Ii ◽  
Clustering Algorithm ◽  
Software Framework ◽  
High Luminosity ◽  
Silicon Sensors ◽  
Density Based Clustering ◽  
Pile Up ◽  
The Future

The future High Luminosity LHC (HL-LHC) is expected to deliver about 5 times higher instantaneous luminosity than the present LHC, resulting in pile-up up to 200 interactions per bunch crossing (PU200). As part of the phase-II upgrade program, the CMS collaboration is developing a new endcap calorimeter system, the High Granularity Calorimeter (HGCAL), featuring highly-segmented hexagonal silicon sensors and scintillators with more than 6 million channels. For each event, the HGCAL clustering algorithm needs to group more than 105 hits into clusters. As consequence of both high pile-up and the high granularity, the HGCAL clustering algorithm is confronted with an unprecedented computing load. CLUE (CLUsters of Energy) is a fast fullyparallelizable density-based clustering algorithm, optimized for high pile-up scenarios in high granularity calorimeters. In this paper, we present both CPU and GPU implementations of CLUE in the application of HGCAL clustering in the CMS Software framework (CMSSW). Comparing with the previous HGCAL clustering algorithm, CLUE on CPU (GPU) in CMSSW is 30x (180x) faster in processing PU200 events while outputting almost the same clustering results.

scholarly journals A novel reconstruction framework for an imaging calorimeter for HL-LHC

2021 ◽  
Vol 251 ◽  
pp. 03013
Author(s):  
Leonardo Cristella ◽  
Keyword(s):  
Clustering Algorithm ◽  
Particle Identification ◽  
Machine Learning Techniques ◽  
Radiation Tolerance ◽  
Reconstruction Algorithms ◽  
High Luminosity ◽  
Silicon Sensors ◽  
Learning Techniques ◽  
Recent Developments ◽  
Speed And Accuracy

To sustain the harsher conditions of the high-luminosity LHC, the CMS collaboration is designing a novel endcap calorimeter system. The new calorimeter will predominantly use silicon sensors to achieve sufficient radiation tolerance and will maintain highly-granular information in the readout to help mitigate the effects of pileup. In regions characterised by lower radiation levels, small scintillator tiles with individual on-tile SiPM readout are employed. A unique reconstruction framework (TICL: The Iterative CLustering) is being developed to fully exploit the granularity and other significant detector features, such as particle identification and precision timing, with a view to mitigate pileup in the very dense environment of HL-LHC. The inputs to the framework are clusters of energy deposited in individual calorimeter layers. Clusters are formed by a density-based algorithm. Recent developments and tunes of the clustering algorithm will be presented. To help reduce the expected pressure on the computing resources in the HL-LHC era, the algorithms and their data structures are designed to be executed on GPUs. Preliminary results will be presented on decreases in clustering time when using GPUs versus CPUs. Ideas for machine-learning techniques to further improve the speed and accuracy of reconstruction algorithms will be presented.

scholarly journals Triggering long-lived particles in HL-LHC and the challenges in the first stage of the trigger system

2020 ◽  
Vol 2020 (8) ◽  
Author(s):  
Biplob Bhattacherjee ◽  
Swagata Mukherjee ◽  
Rhitaja Sengupta ◽  
Prabhat Solanki
Keyword(s):  
Large Hadron Collider ◽  
Hadron Collider ◽  
High Luminosity ◽  
Trigger System ◽  
Pile Up ◽  
The Future ◽  
Level 1

Abstract Triggering long-lived particles (LLPs) at the first stage of the trigger system is very crucial in LLP searches to ensure that we do not miss them at the very beginning. The future High Luminosity runs of the Large Hadron Collider will have increased number of pile-up events per bunch crossing. There will be major upgrades in hardware, firmware and software sides, like tracking at level-1 (L1). The L1 trigger menu will also be modified to cope with pile-up and maintain the sensitivity to physics processes. In our study we found that the usual level-1 triggers, mostly meant for triggering prompt particles, will not be very efficient for LLP searches in the 140 pile-up environment of HL-LHC, thus pointing to the need to include dedicated L1 triggers in the menu for LLPs. We consider the decay of the LLP into jets and develop dedicated jet triggers using the track information at L1 to select LLP events. We show in our work that these triggers give promising results in identifying LLP events with moderate trigger rates.

scholarly journals IUE Studies of Luminous X-Ray Binaries

1983 ◽  
Vol 6 ◽  
pp. 648-648
Author(s):  
J.B. Hutchings
Keyword(s):  
Magellanic Clouds ◽  
Thick Shell ◽  
High Luminosity ◽  
Uv Spectrum ◽  
X Ray ◽  
Unique Object ◽  
The Future ◽  
Orbit Inclination ◽  
Supercritical Accretion ◽  
X Ray Binaries

IUE has been used to study 11 high luminosity X-ray binaries, of which 3 are in the Magellanic Clouds. In the supergiant systems, X-ray ionisation bubbles have been found in most cases, leading to a greater understanding of the winds and accretion processes. Further studies of precessing objects such as LMC X-4 with IUE and ST are clearly of considerable interest, relating to X-ray heating and blanketing. Detailed studies of the Cyg X-l ionisation bubble may resolve the long standing puzzle of its orbit inclination and masses. UV continua have furnished valuable information on extinction, temperatures and luminosities, and the presence of non-stellar (i.e. disk) luminosity. Here too, more detailed studies are clearly indicated for the future. A unique object of interest is the LMC transient 0538-66 whose UV spectrum has quasarlike lines and luminosity which varies oppositely to the visible. This may be a case of supercritical accretion generating an optically thick shell (“disk”) about the pulsar.

Distance Density Based Clustering Algorithm in Wireless Sensor Network

2011 ◽  
Vol 291-294 ◽  
pp. 344-348
Author(s):  
Lin Lin ◽  
Shu Yan ◽  
Yi Nian
Keyword(s):  
Clustering Algorithm ◽  
Distribution Density ◽  
Simulation Experiment ◽  
Clustering Algorithms ◽  
Wireless Sensor ◽  
Energy Usage ◽  
Cluster Heads ◽  
Hierarchical Topology ◽  
Energy Factors ◽  
Density Based Clustering

The hierarchical topology of wireless sensor networks can effectively reduce the consumption in communication. Clustering algorithm is the foundation to realize herarchical structure, so it has been extensive researched. On the basis of Leach algorithm, a distance density based clustering algorithm (DDBC) is proposed, considering synthetically the distribution density of around nodes and the remaining energy factors of the node to dynamically banlance energy usage of nodes when selecting cluster heads. We analyzed the performance of DDBC through compared with the existing other clustering algorithms in simulation experiment. Results show that the proposed method can generare stable quantity cluster heads and banlance the energy load effectively.

An Efficient Density-based Clustering Algorithm for Face Groping

2021 ◽  
Author(s):  
Shenfei Pei ◽  
Feiping Nie ◽  
Rong Wang ◽  
Xuelong Li
Keyword(s):  
Clustering Algorithm ◽  
Density Based Clustering

An improved OPTICS clustering algorithm for discovering clusters with uneven densities

2021 ◽  
Vol 25 (6) ◽  
pp. 1453-1471
Author(s):  
Chunhua Tang ◽  
Han Wang ◽  
Zhiwen Wang ◽  
Xiangkun Zeng ◽  
Huaran Yan ◽  
...  
Keyword(s):  
Time Complexity ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Clustering Algorithms ◽  
Substantial Improvement ◽  
Experimental Results ◽  
High Time ◽  
Parameter Setting ◽  
K Nearest Neighbor ◽  
Density Based Clustering

Most density-based clustering algorithms have the problems of difficult parameter setting, high time complexity, poor noise recognition, and weak clustering for datasets with uneven density. To solve these problems, this paper proposes FOP-OPTICS algorithm (Finding of the Ordering Peaks Based on OPTICS), which is a substantial improvement of OPTICS (Ordering Points To Identify the Clustering Structure). The proposed algorithm finds the demarcation point (DP) from the Augmented Cluster-Ordering generated by OPTICS and uses the reachability-distance of DP as the radius of neighborhood eps of its corresponding cluster. It overcomes the weakness of most algorithms in clustering datasets with uneven densities. By computing the distance of the k-nearest neighbor of each point, it reduces the time complexity of OPTICS; by calculating density-mutation points within the clusters, it can efficiently recognize noise. The experimental results show that FOP-OPTICS has the lowest time complexity, and outperforms other algorithms in parameter setting and noise recognition.

scholarly journals A new method for unveiling open clusters in Gaia

2018 ◽  
Vol 618 ◽  
pp. A59 ◽  
Author(s):  
A. Castro-Ginard ◽  
C. Jordi ◽  
X. Luri ◽  
F. Julbe ◽  
M. Morvan ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Dimensional Space ◽  
Open Clusters ◽  
Machine Learning Algorithms ◽  
New Era ◽  
Use Of Data ◽  
Manual Intervention ◽  
Density Based Clustering ◽  
Artificial Neural Network Ann ◽  
Astrometric Data

Context. The publication of the Gaia Data Release 2 (Gaia DR2) opens a new era in astronomy. It includes precise astrometric data (positions, proper motions, and parallaxes) for more than 1.3 billion sources, mostly stars. To analyse such a vast amount of new data, the use of data-mining techniques and machine-learning algorithms is mandatory. Aims. A great example of the application of such techniques and algorithms is the search for open clusters (OCs), groups of stars that were born and move together, located in the disc. Our aim is to develop a method to automatically explore the data space, requiring minimal manual intervention. Methods. We explore the performance of a density-based clustering algorithm, DBSCAN, to find clusters in the data together with a supervised learning method such as an artificial neural network (ANN) to automatically distinguish between real OCs and statistical clusters. Results. The development and implementation of this method in a five-dimensional space (l, b, ϖ, μα*, μδ) with the Tycho-Gaia Astrometric Solution (TGAS) data, and a posterior validation using Gaia DR2 data, lead to the proposal of a set of new nearby OCs. Conclusions. We have developed a method to find OCs in astrometric data, designed to be applied to the full Gaia DR2 archive.

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