Muon Hunter: a Zooniverse project

Bird, R.; Daniel, M. K.; Dickinson, H. J.; Feng, Q.; Fortson, L.; Furniss, A.; Jarvis, J. F.; Mukherjee, R.; Sadeh, I.; Williams, D. A.

doi:10.1088/1742-6596/1342/1/012103

Cited by 7 publications

(7 citation statements)

References 6 publications

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“…Further improvements in the muon identification efficiency and muon purity could be made by the implementation of more sophisticated ring identification methods, such as by a Hough transform, machine learning or even citizen science approaches, helping to take the intrinsic systematic uncertainties arising from variation in hadronic air shower development into account [50,15,24]. With a single telescope trigger multiplicity the total number of muon events seen can be significantly increased, albeit without detection of the associated shower [18].…”

Section: Discussionmentioning

confidence: 99%

Potential for measuring the longitudinal and lateral profile of muons in TeV air showers with IACTs

Mitchell

Dembinski

Parsons

2019

Astroparticle Physics

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Muons are copiously produced within hadronic extensive air showers (EAS) occurring in the Earth's atmosphere, and are used by particle air shower detectors as a means of identifying the primary cosmic ray which initiated the EAS. Imaging Atmospheric Cherenkov Telescopes (IACTs), designed for the detection of γ-ray initiated EAS for the purposes of Very High Energy (VHE) γ-ray astronomy, are subject to a considerable background signal due to hadronic EAS. Although hadronic EAS are typically rejected for γ-ray analysis purposes, single muons produced within such showers generate clearly identifiable signals in IACTs and muon images are routinely retained and used for calibration purposes. For IACT arrays operating with a stereoscopic trigger, when a muon triggers one telescope, other telescopes in IACT arrays usually detect the associated hadronic EAS. We demonstrate for the first time the potential of IACT arrays for competitive measurements of the muon content of air showers, their lateral distribution and longitudinal profile of production slant heights in the TeV energy range. Such information can provide useful input to hadronic interaction models.

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Section: Discussionmentioning

confidence: 99%

Potential for measuring the longitudinal and lateral profile of muons in TeV air showers with IACTs

Mitchell

Dembinski

Parsons

2019

Astroparticle Physics

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“…CNN classification of VERITAS muon images has been done previously, first using a CNN trained on VEGAS-labelled data [3], and then using data from the original Muon Hunter [4] project to train a CNN [5]. However, the first of these used VEGAS-labelled data to both train and test the model, which didn't test the generalisability of the model.…”

Section: Pos(icrc2021)766mentioning

confidence: 99%

“…Muon Hunters 2 is a citizen science project for gathering muon labels for VERITAS images, hosted on Zooniverse.org. As a follow-on from the original Muon Hunter project [4], Muon Hunters 2 was launched in March of 2019 with the aim of making the identification of muon rings more efficient. In this project, a 6 × 6 grid of images is presented, and volunteers are asked to classify all of these images simultaneously by clicking on all images belonging to the…”

Section: Training and Validation Dataset: Muon Huntersmentioning

confidence: 99%

Identifying muon rings in VERITAS data using convolutional neural networks trained on images classified with Muon Hunters 2

Flanagan¹,

Wright²,

Dickinson³

et al. 2021

Proceedings of 37th International Cosmic Ray Conference — PoS(ICRC2021)

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Muons from extensive air showers appear as rings in images taken with imaging atmospheric Cherenkov telescopes, such as VERITAS. These muon-ring images are used for the calibration of the VERITAS telescopes, however the calibration accuracy can be improved with a more efficient muon-identification algorithm. Convolutional neural networks (CNNs) are used in many state-ofthe-art image-recognition systems and are ideal for muon image identification, once trained on a suitable dataset with labels for muon images. However, by training a CNN on a dataset labelled by existing algorithms, the performance of the CNN would be limited by the suboptimal muonidentification efficiency of the original algorithms. Muon Hunters 2 is a citizen science project that asks users to label grids of VERITAS telescope images, stating which images contain muon rings. Each image is labelled 10 times by independent volunteers, and the votes are aggregated and used to assign a 'muon' or 'non-muon' label to the corresponding image. An analysis was performed using an expert-labelled dataset in order to determine the optimal vote percentage cut-offs for assigning labels to each image for CNN training. This was optimised so as to identify as many muon images as possible while avoiding false positives. The performance of this model greatly improves on existing muon identification algorithms, identifying approximately 30 times the number of muon images identified by the current algorithm implemented in VEGAS (VERITAS Gamma-ray Analysis Suite), and roughly 2.5 times the number identified by the Hough transform method, along with significantly outperforming a CNN trained on VEGAS-labelled data.

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“…Only clean rings can then be fit in a robust manner (although promising attempts with wavelet filtering (Lessard et al 2002) and machine-learning (Bird et al 2018) methods have been made in the past that do not need to rely on a direct removal of night-sky background noise).…”

Section: Noise Removalmentioning

confidence: 99%

Using Muon Rings for the Calibration of the Cherenkov Telescope Array: A Systematic Review of the Method and Its Potential Accuracy

Gaug

Mitchell

Maccarone

et al. 2019

ApJS

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The analysis of ring images produced by muons in an Imaging Atmospheric Cherenkov Telescope (IACT) provides a powerful and precise method to calibrate the IACT optical throughput and monitor its optical point-spread function (PSF). First proposed by the Whipple collaboration in the early 90's, this method has been refined by the so-called second generation of IACT experiments: H.E.S.S., MAGIC and VERITAS. We review here the progress made with these instruments and investigate the applicability of the method as the primary throughput calibration method for the different telescope types forming the future Cherenkov Telescope Array (CTA). We find several additional systematic effects not yet taken into account by previous authors and propose several new analytical methods to include these in the analysis. Slight modifications in hardware and analysis need to be made to ensure that such a calibration works as accurately as required for the CTA. We derive analytic estimates for the expected muon data rates for optical throughput calibration, camera pixel flat-fielding and monitoring of the optical PSF. The achievable statistical and systematic uncertainties of the method are also assessed.

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Muon Hunter: a Zooniverse project

Cited by 7 publications

References 6 publications

Potential for measuring the longitudinal and lateral profile of muons in TeV air showers with IACTs

Potential for measuring the longitudinal and lateral profile of muons in TeV air showers with IACTs

Identifying muon rings in VERITAS data using convolutional neural networks trained on images classified with Muon Hunters 2

Using Muon Rings for the Calibration of the Cherenkov Telescope Array: A Systematic Review of the Method and Its Potential Accuracy

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