The short-term fading of TLDs

Tsuda, Masayuki; Mori, Tomoyuki

doi:10.1016/0167-5087(84)90235-7

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…AGILE (Astrorivelatore Gamma ad Immagini LEggero -Light Imager for Gamma-Ray Astrophysics) is a space mission of the Italian Space Agency (ASI) designed for X-ray and gamma-ray observations and was launched on 23rd Apr 2007 (Tavani et al 2008(Tavani et al , 2009. The AGILE payload consists of the Silicon Tracker (ST), the SuperAGILE Xray detector, the CsI(Tl) Mini-Calorimeter (MCAL), and an AntiCoincidence System (ACS).…”

Section: Introductionmentioning

confidence: 99%

The AGILEScience App to execute gamma-ray scientific analyses from mobile devices

Bulgarelli¹,

Parmiggiani²,

Fioretti³

et al. 2023

Preprint

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AGILE is a space mission launched in 2007 devoted to high-energy astrophysics. The AGILE Team is involved in the multi-messenger campaigns to send and receive science alerts about transient events in the shortest time possible. For this reason, the AGILE Team developed several real-time analysis pipelines to analyse data and follow-up external science alerts. However, the results obtained by these pipelines are preliminary and must be validated with manual analyses that are the bottleneck of the workflow. To speed up the scientific analysis performed by scientists, the AGILE Team developed the AGILEScience mobile application (for iOS and Android devices) that offers to the AGILE Team a password-protected section used to visualise the results of automated pipelines. We present in this contribution an improved functionality of the AGILEScience application that aims to enable the AGILE Team to execute a full scientific analysis using their mobile devices. When the analysis is completed, the system sends an email to notify the user that can visualise the results (e.g. plots, tables, and HTML pages) through the application. The possibility to perform scientific analysis from a mobile device enables the AGILE researchers to perform fast scientific analyses remotely to validate the preliminary results obtained with the automated pipelines. This workflow reduces the overall reaction time of the AGILE Team for the follow-up of transient phenomena.

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Section: Introductionmentioning

confidence: 99%

The AGILEScience App to execute gamma-ray scientific analyses from mobile devices

Bulgarelli¹,

Parmiggiani²,

Fioretti³

et al. 2023

Preprint

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“…The earliest efforts were initiated by Bock et al (2004). Later on, for γ-hadron segregation, the effectiveness of tree based multivariate classifiers was demonstrated by two operational ground based observatories, MAGIC (Albert et al 2008) and HESS (Ohm et al 2009;Fiasson et al 2010;Dubois et al 2009). It should be noted that no machine learning method is sacrosanct as far as its superiority over other multivariate methods is concerned.…”

Section: Introductionmentioning

confidence: 99%

Gamma/hadron segregation for a ground based imaging atmospheric Cherenkov telescope using machine learning methods: Random Forest leads

Sharma¹,

Nayak²,

Koul³

et al. 2014

Res. Astron. Astrophys.

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A detailed case study of γ-hadron segregation for a ground based atmospheric Cherenkov telescope is presented. We have evaluated and compared various supervised machine learning methods such as the Random Forest method, Artificial Neural Network, Linear Discriminant method, Naive Bayes Classifiers, Support Vector Machines as well as the conventional dynamic supercut method by simulating triggering events with the Monte Carlo method and applied the results to a Cherenkov telescope. It is demonstrated that the Random Forest method is the most sensitive machine learning method for γ-hadron segregation.

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“…The fading effect has been thoroughly studied for many different types of thermoluminescence materials (Fowler et al, 1965;Suntharalingam and Cameron, 1966;Glenn et al, 1967;Gorbics et al, 1967;Webb, 1967a,b;Orton and Busemann, 1968;Suntharalingam et al, 1968;Nakajima and Hasizume, 1969;Nishita and Hamilton, 1971;Wintle et al, 1971;Sukis, 1971;Becker, 1973Becker, , 1974Gwiazdowski et al, 1974;Johnson, 1974;Mason et al, 1974;Burkhardt et al, 1976;Gross et al, 1976;Gennai, 1980, 1981;Nambi, 1982;Tsuda and Mori, 1984).…”

Section: Introductionmentioning

confidence: 99%

Theoretical considerations of the TL response after irradiation-storage periods at various temperatures

Furetta

1986

International Journal of Radiation Applications and Instrumenta

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Theoretical calculations for estimating both the temperature and fading effects on the TL response of dosemeters used in environmental radiation dosimetry are described.Making use of the first and second order kinetics a set of equations is derived when the TL response is affected both by fading only and by fading in conjunction with environmental background. Then the responses obtained are calculated as a function of the storage temperatures during monitoring time.

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The short-term fading of TLDs

Cited by 6 publications

References 11 publications

The AGILEScience App to execute gamma-ray scientific analyses from mobile devices

The AGILEScience App to execute gamma-ray scientific analyses from mobile devices

Gamma/hadron segregation for a ground based imaging atmospheric Cherenkov telescope using machine learning methods: Random Forest leads

Theoretical considerations of the TL response after irradiation-storage periods at various temperatures

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