Creat membership Creat membership
Sign in

Forgot password?

Confirm
  • Forgot password?
    Sign Up
  • Confirm
    Sign In
Creat membership Creat membership
Sign in

Forgot password?

Confirm
  • Forgot password?
    Sign Up
  • Confirm
    Sign In
Collection
For ¥0.57 per day, unlimited downloads CREATE MEMBERSHIP Download

toTop

If you have any feedback, Please follow the official account to submit feedback.

Turn on your phone and scan

home > search >

Deep neural networks for energy and position reconstruction in EXO-200

Author:
Delaquis, S.  Jewell, M. J.  Ostrovskiy, I.  Weber, M.  Ziegler, T.  Dalmasson, J.  Kaufman, L. J.  Richards, T.  Albert, J. B.  Anton, G.  Badhrees, I.  Barbeau, P. S.  Bayerlein, R.  Beck, D.  Belov, V.  Breidenbach, M.  Brunner, T.  Cao, G. F.  Cen, W. R.  Chambers, C.  Cleveland, B.  Coon, M.  Craycraft, A.  Cree, W.  Daniels, T.  Danilov, M.  Daugherty, S. J.  Daughhetee, J.  Davis, J.  Mesrobian-Kabakian, A. Der  DeVoe, R.  Dilling, J.  Dolgolenko, A.  Dolinski, M. J.  Fairbank, W., Jr.  Farine, J.  Feyzbakhsh, S.  Fierlinger, P.  Fudenberg, D.  Gornea, R.  Gratta, G.  Hall, C.  Hansen, E. V.  Harris, D.  Hoessl, J.  Hufschmidt, P.  Hughes, M.  Iverson, A.  Jamil, A.  Johnson, A.  Karelin, A.  Koffas, T.  Kravitz, S.  Krucken, R.  Kuchenkov, A.  Kumar, K. S.  Lan, Y.  Leonard, D. S.  Li, G. S.  Li, S.  Licciardi, C.  Lin, Y. H.  MacLellan, R.  Michel, T.  Mong, B.  Moore, D.  Murray, K.  Njoya, O.  Odian, A.  Piepke, A.  Pocar, A.  Retiere, F.  Robinson, A. L.  Rowson, P. C.  Schmidt, S.  Schubert, A.  Sinclair, D.  Soma, A. K.  Stekhanov, V.  Tarka, M.  Todd, J.  Tolba, T.  Veeraraghavan, V.  Vuilleumier, J. -L.  Wagenpfeil, M.  Waite, A.  Watkins, J.  Wen, L. J.  Wichoski, U.  Wrede, G.  Xia, Q.  Yang, L.  Yen, Y. -R.  Zeldovich, O. Ya.  


Journal:
JOURNAL OF INSTRUMENTATION


Issue Date:
2018


Abstract(summary):

We apply deep neural networks (DNN) to data from the EXO-200 experiment. In the studied cases, the DNN is able to reconstruct the relevant parameters - total energy and position - directly from raw digitized waveforms, with minimal exceptions. For the first time, the developed algorithms are evaluated on real detector calibration data. The accuracy of reconstruction either reaches or exceeds what was achieved by the conventional approaches developed by EXO-200 over the course of the experiment. Most existing DNN approaches to event reconstruction and classification in particle physics are trained on Monte Carlo simulated events. Such algorithms are inherently limited by the accuracy of the simulation. We describe a unique approach that, in an experiment such as EXO-200, allows to successfully perform certain reconstruction and analysis tasks by training the network on waveforms from experimental data, either reducing or eliminating the reliance on the Monte Carlo.


VIEW PDF

The preview is over

If you wish to continue, please create your membership or download this.

Create Membership

Similar Literature

Submit Feedback

This function is a member function, members do not limit the number of downloads