Obtaining the distribution of a physical quantity is a frequent objective in experimental physics. In cases where the distribution of the relevant quantity cannot be accessed experimentally, it has to be reconstructed from distributions of correlated quantities that are measured, instead. This reconstruction is called deconvolution.
Formally, the task of deconvolution is to estimate the density function \(f:\mathcal{Y}\to\mathbb{R}\) of the relevant quantity \(Y\). Given are the measured density \(g\) and the detector response function \(R\), the last of which is estimated from training data.
We investigate and develop algorithms which solve the deconvolution problem. The present web site is a bulletin for our scientific results and readytouse software.
Cherenkov Astronomy
Cherenkov astronomy is a deconvolution use case which studies the energy distribution of cosmic gamma radiation to reason about the characteristics of celestial objects. Since the gamma radiation is not directly measured by the groundbased telescopes employed in Cherenkov astronomy, deconvolution reconstructs the gamma particle distribution from the related Cherenkov light recorded by these telescopes.
Deconvolution algorithms are also applied in other areas of particle physics, for example at the LHCb experiment [2].
Publications

M. Bunse, N. Piatkowski, T. Ruhe, W. Rhode, and K. Morik. Unification of deconvolution algorithms for Cherenkov astronomy. In Proc. of the 5th IEEE Int. Conf. on Data Science and Advanced Analytics (DSAA), pages 2130, 2018.

M. Bunse, N. Piatkowski, and K. Morik. Towards a unifying view on deconvolution in Cherenkov astronomy. In Lernen, Wissen, Daten, Analysen (LWDA) conference proceedings, pages 7377, 2018.

M. Bunse. DSEA rocksolid  regularization and comparison with other deconvolution algorithms. Master’s Thesis, TU Dortmund, 2018.
Supplementary Material
Software
The Julia package CherenkovDeconvolution.jl and examples are hosted on GitHub. The package implements the investigated algorithms:
 DSEA+, our enhanced version of the Dortmund Spectrum Estimation Algorithm
 RUN, the Regularized Unfolding [3]
 IBU, the Iterative Bayesian Unfolding [4]
A native port of this package to Python, called CherenkovDeconvolution.py, is under current development. Our experiments are implemented in a separate repository.
Providing Reference
We kindly ask you to provide reference to our DSAA paper.
@inproceedings{bunse2018unification,
author = {Bunse, Mirko and Piatkowski, Nico and Ruhe, Tim and Rhode, Wolfgang and Morik, Katharina},
title = {Unification of Deconvolution Algorithms for Cherenkov Astronomy},
booktitle = {5th International Conference on Data Science and Advanced Analytics (DSAA)},
year = {2018},
pages = {2130},
organization={IEEE},
note = {To appear}
}
Bibliography
 C. Bockermann, K. Brügge, J. Buss, A. Egorov, K. Morik, W. Rhode, and T. Ruhe. Online analysis of highvolume data streams in astroparticle physics. In Proc. of the ECMLPKDD 2015, pages 100115, 2015.
 T. Ruhe, M. Schellenberg, and B. Spaan. Application of the Dortmund spectrum estimation algorithm to LHCb monte carlo simulations. Tech Report, SFB 876, 2018.
 V. Blobel. An unfolding method for high energy physics experiments. In Adv. Stat. Techniques in Part. Phys., pages 258267, 2002.
 G. D’Agostini. Improved iterative Bayesian unfolding. arXiv:1010.0632, 2010.
 T. Ruhe, M. Börner, M. Wornowizki, et al. Mining for spectra  the Dortmund spectrum estimation algorithm. In Proc. of the ADASS XXVI, 2016.
 N. Milke, M. Doert, S. Klepser, D. Mazin, V. Blobel, and W. Rhode. Solving inverse problems with the unfolding program TRUEE: Examples in astroparticle physics. In Nucl. Instrum. Methods Phys. Res. A, Vol. 697, pages 133147, 2013.
 T. Ruhe, K. Morik, and the IceCube Collaboration. Development of a general analysis and unfolding scheme and its application to measure the energy spectrum of atmospheric neutrinos with IceCube. In Eur. Phys. J. C, Vol. 75, 2015.
Contact
Share your ideas on deconvolution with us!
Email: mirko.bunse [ät] cs.tudortmund.de
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