High-dimensional Bayesian likelihood normalisation for CRESST's background model

Abstract Using CaWO 4 crystals as cryogenic calorimeters, the CRESST experiment searches for nuclear recoils caused by the scattering of potential Dark Matter particles. A reliable identification of a potential signal crucially depends on an accurate background model. In this work, we introduce an improved normalisation method for CRESST's model of electromagnetic backgrounds, which is an important technical step towards developing a more accurate background model. Spectral templates based on Geant4 simulations are normalised via a Bayesian likelihood fit to experimental background data. Contrary to our previous work, no explicit assumption of partial secular equilibrium is required a priori, which results in a more robust and versatile applicability. This new method also naturally considers the correlation between all background components. Due to these purely technical improvements, the presented method has the potential to explain up to 82.7 % of the experimental background within [1 keV,40 keV], an improvement of at most 18.6 % compared to our previous method. The actual value is subject to ongoing validations of the included physics.

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Publication Details

Journal
Journal of Instrumentation
Published
2024-11-01
DOI
https://doi.org/10.1088/1748-0221/19/11/p11013
Primary Topic
Medical Imaging Techniques and Applications
Type
article
Field-Weighted Citation Impact
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article

High-dimensional Bayesian likelihood normalisation for CRESST's background model

T. Ortmann, F. Petricca, P. Gorla, L. Pattavina et al.
Journal of Instrumentation
Medical Imaging Techniques and Applications
article

High-dimensional Bayesian likelihood normalisation for CRESST's background model

T. Ortmann, F. Petricca, P. Gorla, L. Pattavina, I. Usherov, V. Mokina, V. Zema, S. Banik, D. Schmiedmayer, A. D’Addabbo, C. Pagliarone, S. Fichtinger, A. Kinast, S. Schönert, Stefano De Nisi, F. Ferella, A. Langenkämper, C. Bucci, S. Di Lorenzo, F. Wagner, R. Breier, M. Stahlberg, J. Burkhart, Abhijit Garai, A. Erb, G. Benato, P. V. Guillaumon, C. Strandhagen, H. Kluck, L. Canonica, M. Ješkovský, A. Bertolini, A. Bento, M. Mancuso, L. Stodolsky, G. Angloher, D. Fuchs, V. M. Ghete, M. Laubenstein, F. Reindl, Du Toit Strauss, M. Kaznacheeva, L. Einfalt, J. Schieck, F. Pröbst, A. Nilima, W. Potzel, Pavel P. Povinec, J. Rothe, S. Kuckuk, L. Marini, D. Hauff, J. Jochum, M. Willers, H. Kraus, F. von Feilitzsch, S. Gupta, K. Schäffner, M. Olmi, F. Pucci, C. Schwertner, L. Meyer
article en

Abstract

Abstract Using CaWO 4 crystals as cryogenic calorimeters, the CRESST experiment searches for nuclear recoils caused by the scattering of potential Dark Matter particles. A reliable identification of a potential signal crucially depends on an accurate background model. In this work, we introduce an improved normalisation method for CRESST's model of electromagnetic backgrounds, which is an important technical step towards developing a more accurate background model. Spectral templates based on Geant4 simulations are normalised via a Bayesian likelihood fit to experimental background data. Contrary to our previous work, no explicit assumption of partial secular equilibrium is required a priori, which results in a more robust and versatile applicability. This new method also naturally considers the correlation between all background components. Due to these purely technical improvements, the presented method has the potential to explain up to 82.7 % of the experimental background within [1 keV,40 keV], an improvement of at most 18.6 % compared to our previous method. The actual value is subject to ongoing validations of the included physics.

Journal of InstrumentationVol. 19(11)
TU Wien (AT), Istituto Nazionale di Fisica Nucleare, Laboratori Nazionali del Gran Sasso (IT), University of Oxford (GB), Max Planck Institute for Physics (DE), Institute of High Energy Physics (AT), Gran Sasso Science Institute (IT), Technical University of Munich (DE), Comenius University Bratislava (SK), University of Coimbra (PT), University of Tübingen (DE)
Openalex Percentile: Top 66%
Medical Imaging Techniques and Applications
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