scholarly journals Sensitivity of multi-PMT optical modules in Antarctic ice to supernova neutrinos of MeV energy

2021 ◽  
Vol 81 (12) ◽  
Author(s):  
C. J. Lozano Mariscal ◽  
L. Classen ◽  
M. A. Unland Elorrieta ◽  
A. Kappes
Keyword(s):  
Optical Sensors ◽  
Detection Rate ◽  
High Energy ◽  
Core Collapse ◽  
South Pole ◽  
False Detection Rate ◽  
The South ◽  
False Detection ◽  
Neutrino Telescopes ◽  
Astrophysical Neutrinos

AbstractNew optical sensors with a segmented photosensitive area are being developed for the next generation of neutrino telescopes at the South Pole. In addition to increasing sensitivity to high-energy astrophysical neutrinos, we show that this will also lead to a significant improvement in sensitivity to MeV neutrinos, such as those produced in core-collapse supernovae (CCSN). These low-energy neutrinos can provide a detailed picture of the events after stellar core collapse, testing our understanding of these violent explosions. We present studies on the event-based detection of MeV neutrinos with a segmented sensor and, for the first time, the potential of a corresponding detector in the deep ice at the South Pole for the detection of extra-galactic CCSN. We find that exploiting temporal coincidences between signals in different photocathode segments, a $$27\ \mathrm {M}_{\odot }$$ 27 M ⊙ progenitor mass CCSN can be detected up to a distance of 341 kpc with a false detection rate of $${0.01}\,\hbox {year}^{-1}$$ 0.01 year - 1 with a detector consisting of 10,000 sensors. Increasing the number of sensors to 20,000 and reducing the optical background by a factor of 70 expands the range such that a CCSN detection rate of 0.1 per year is achieved, while keeping the false detection rate at $${0.01}\,{\hbox {year}^{-1}}$$ 0.01 year - 1 .

ULTRA HIGH ENERGY NEUTRINO TELESCOPES

2006 ◽  
Vol 21 (08n09) ◽  
pp. 1914-1924
Author(s):  
PER OLOF HULTH
Keyword(s):  
Lake Baikal ◽  
High Energy ◽  
Atmospheric Neutrinos ◽  
South Pole ◽  
Ultra High Energy ◽  
The South ◽  
High Energy Neutrino ◽  
Neutrino Telescopes ◽  
Energy Neutrino ◽  
Neutrino Astronomy

The Neutrino Telescopes NT-200 in Lake Baikal, Russia and AMANDA at the South Pole, Antarctica have now opened the field of High Energy Neutrino Astronomy. Several other Neutrino telescopes are in the process of being constructed or very near realization. Several thousands of atmospheric neutrinos have been observed with energies up to several 100 TeV but so far no evidence for extraterrestrial neutrinos has been found.

scholarly journals IceCube: A Kilometer-Scale Neutrino Observatory at the South Pole

2005 ◽  
Vol 13 ◽  
pp. 949-950
Author(s):  
Francis Halzen
Keyword(s):  
Cosmic Rays ◽  
Particle Physics ◽  
High Energy ◽  
Neutrino Telescope ◽  
South Pole ◽  
Neutrino Detector ◽  
The South ◽  
High Energy Neutrino ◽  
Neutrino Telescopes ◽  
Diversity Of Science

AbstractSolving the century-old puzzle of how and where cosmic rays are accelerated mostly drives the design of high-energy neutrino telescopes. It calls, along with a diversity of science goals reaching particle physics, astrophysics and cosmology, for the construction of a kilometer-scale neutrino detector. This led to the IceCube concept to transform a kilometer cube of transparent Antarctic Ice, one mile below the South Pole, into a neutrino telescope.

scholarly journals IceCube neutrino observatory at the South Pole: recent results

2009 ◽  
Vol 5 (H15) ◽  
pp. 620-621
Author(s):  
Kirill Filimonov
Keyword(s):  
Optical Sensors ◽  
Lower Energy ◽  
Cherenkov Radiation ◽  
Charged Particles ◽  
High Energy ◽  
South Pole ◽  
Particle Detector ◽  
The South ◽  
The Earth ◽  
The World

AbstractThe IceCube neutrino observatory, the largest particle detector in the world (1 km3), is currently being built at the South Pole. IceCube looks down through the Earth to filter out lower-energy particles and uses optical sensors embedded deep in the ultra-clean Antarctic ice to detect high energy neutrinos via Cherenkov radiation from charged particles produced in neutrino interactions. A summary of selected recent results is presented.

Faster multi-defect detection system in shield tunnel using combination of FCN and faster RCNN

2019 ◽  
Vol 22 (13) ◽  
pp. 2907-2921 ◽  
Author(s):  
Xinwen Gao ◽  
Ming Jian ◽  
Min Hu ◽  
Mohan Tanniru ◽  
Shuaiqing Li
Keyword(s):  
Boundary Layer ◽  
Defect Detection ◽  
Detection Rate ◽  
Layer Structure ◽  
Detection System ◽  
Identification Accuracy ◽  
Shield Tunnel ◽  
False Detection Rate ◽  
False Detection ◽  
Data Set

With the large-scale construction of urban subways, the detection of tunnel defects becomes particularly important. Due to the complexity of tunnel environment, it is difficult for traditional tunnel defect detection algorithms to detect such defects quickly and accurately. This article presents a deep learning FCN-RCNN model that can detect multiple tunnel defects quickly and accurately. The algorithm uses a Faster RCNN algorithm, Adaptive Border ROI boundary layer and a three-layer structure of the FCN algorithm. The Adaptive Border ROI boundary layer is used to reduce data set redundancy and difficulties in identifying interference during data set creation. The algorithm is compared with single FCN algorithm with no Adaptive Border ROI for different defect types. The results show that our defect detection algorithm not only addresses interference due to segment patching, pipeline smears and obstruction but also the false detection rate decreases from 0.371, 0.285, 0.307 to 0.0502, respectively. Finally, corrected by cylindrical projection model, the false detection rate is further reduced from 0.0502 to 0.0190 and the identification accuracy of water leakage defects is improved.

scholarly journals A Deep Learning Approach of Vehicle Multitarget Detection from Traffic Video

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Xun Li ◽  
Yao Liu ◽  
Zhengfan Zhao ◽  
Yue Zhang ◽  
Li He
Keyword(s):  
Detection Rate ◽  
Detection Method ◽  
Training Model ◽  
Learning Approach ◽  
Flow State ◽  
False Detection Rate ◽  
False Detection ◽  
Road Vehicles ◽  
Traffic Video ◽  
Flow States

Vehicle detection is expected to be robust and efficient in various scenes. We propose a multivehicle detection method, which consists of YOLO under the Darknet framework. We also improve the YOLO-voc structure according to the change of the target scene and traffic flow. The classification training model is obtained based on ImageNet and the parameters are fine-tuned according to the training results and the vehicle characteristics. Finally, we obtain an effective YOLO-vocRV network for road vehicles detection. In order to verify the performance of our method, the experiment is carried out on different vehicle flow states and compared with the classical YOLO-voc, YOLO 9000, and YOLO v3. The experimental results show that our method achieves the detection rate of 98.6% in free flow state, 97.8% in synchronous flow state, and 96.3% in blocking flow state, respectively. In addition, our proposed method has less false detection rate than previous works and shows good robustness.

HE-YOLO: Aerial Target Detection Based On Improved YOLOv3

2021 ◽  
Author(s):  
Yuqing Zhao ◽  
Jinlu Jia ◽  
Di Liu ◽  
Yurong Qian
Keyword(s):  
Target Detection ◽  
Loss Function ◽  
Detection Rate ◽  
Aerial Image ◽  
Experimental Comparison ◽  
Small Scale ◽  
Detection Accuracy ◽  
False Detection Rate ◽  
False Detection ◽  
Bounding Box

Aerial image-based target detection has problems such as low accuracy in multiscale target detection situations, slow detection speed, missed targets and falsely detected targets. To solve this problem, this paper proposes a detection algorithm based on the improved You Only Look Once (YOLO)v3 network architecture from the perspective of model efficiency and applies it to multiscale image-based target detection. First, the K-means clustering algorithm is used to cluster an aerial dataset and optimize the anchor frame parameters of the network to improve the effectiveness of target detection. Second, the feature extraction method of the algorithm is improved, and a feature fusion method is used to establish a multiscale (large-, medium-, and small-scale) prediction layer, which mitigates the problem of small target information loss in deep networks and improves the detection accuracy of the algorithm. Finally, label regularization processing is performed on the predicted value, the generalized intersection over union (GIoU) is used as the bounding box regression loss function, and the focal loss function is integrated into the bounding box confidence loss function, which not only improves the target detection accuracy but also effectively reduces the false detection rate and missed target rate of the algorithm. An experimental comparison on the RSOD and NWPU VHR-10 aerial datasets shows that the detection effect of high-efficiency YOLO (HE-YOLO) is significantly improved compared with that of YOLOv3, and the average detection accuracies are increased by 8.92% and 7.79% on the two datasets, respectively. The algorithm not only shows better detection performance for multiscale targets but also reduces the missed target rate and false detection rate and has good robustness and generalizability.

A Approach to Change Detection for HR Image

2014 ◽  
Vol 971-973 ◽  
pp. 1449-1453
Author(s):  
Zuo Wei Huang ◽  
Shu Guang Wu ◽  
Tao Xin Zhang
Keyword(s):  
Change Detection ◽  
Detection Rate ◽  
False Detection Rate ◽  
False Detection ◽  
High Detection Rate ◽  
High Detection ◽  
Self Adaptation ◽  
Ica Algorithm ◽  
Different Characteristics ◽  
Better Than

Hyperspectral remote sensing is the multi-dimensional information obtaining technology,which combines target detection and spectral imaging technology together, In order to accord with the condition of hyperspectral imagery，the paper developed an optimized ICA algorithm for change detection to describe the statistical distribution of the data. By processing these abundance maps, change of different classes of objects can be obtained..A approach is capable of self-adaptation, and can be applied to hyperspectral images with different characteristics. Experiment results demonstrate that the ICA-based hyperspectral change detection performs better than other traditional methods with a high detection rate and a low false detection rate.

scholarly journals On the age vs depth and optical clarity of deep ice at the South Pole

1995 ◽  
Vol 41 (139) ◽  
pp. 445-454
Author(s):  
Keyword(s):  
Visible Light ◽  
High Energy ◽  
Dust Concentration ◽  
Cherenkov Light ◽  
South Pole ◽  
The South ◽  
High Energy Neutrino ◽  
Arrival Times ◽  
Energy Neutrino ◽  
Crystal Boundaries

AbstractThe first four strings of phototubes for the AMANDA high-energy neutrino observatory are now frozen in place at a depth of 800-1000 m in ice at the South Pole, During the 1995-96 season, as many as six more strings will be deployed at greater depths. Provided absorption, scattering and refraction of visible light are sufficiently small, the trajectory of a muon into which a neutrino converts can be determined by using the array of phototubes to measure the arrival times of Cherenkov light emitted by the muon. To help in deciding on the depth for implantation of the six new strings, we discuss models of age vs depth for South Pole ice, we estimate mean free paths for scattering from bubbles and dust as a function of depth and we assess distortion of light paths due to refraction at crystal boundaries and interfaces between air-hydrate inclusions and normal ice. We conclude that the interval 1600-2100 m will be suitably transparent for a future 1 km3 observatory except possibly in a region a few tens of meters thick at a depth corresponding to a peak in the dust concentration at 60 k year BP.

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