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3 results on '"Bartolot D."'

1. Towards an automated data cleaning with deep learning in CRESST

Author

Angloher, G., Banik, S., Bartolot, D., Benato, G., Bento, A., Bertolini, A., Breier, R., Bucci, C., Burkhart, J., Canonica, L., D'Addabbo, A., Di Lorenzo, S., Einfalt, L., Erb, A., Feilitzsch, F. v., Iachellini, N. Ferreiro, Fichtinger, S., Fuchs, D., Fuss, A., Garai, A., Ghete, V. M., Gerster, S., Gorla, P., Guillaumon, P. V., Gupta, S., Hauff, D., Ješkovský, M., Jochum, J., Kaznacheeva, M., Kinast, A., Kluck, H., Kraus, H., Lackner, M., Langenkämper, A., Mancuso, M., Marini, L., Meyer, L., Mokina, V., Nilima, A., Olmi, M., Ortmann, T., Pagliarone, C., Pattavina, L., Petricca, F., Potzel, W., Povinec, P., Pröbst, F., Pucci, F., Reindl, F., Rizvanovic, D., Rothe, J., Schäffner, K., Schieck, J., Schmiedmayer, D., Schönert, S., Schwertner, C., Stahlberg, M., Stodolsky, L., Strandhagen, C., Strauss, R., Usherov, I., Wagner, F., Willers, M., Zema, V., and Waltenberger, W.

Subjects
Physics - Instrumentation and Detectors, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

The CRESST experiment employs cryogenic calorimeters for the sensitive measurement of nuclear recoils induced by dark matter particles. The recorded signals need to undergo a careful cleaning process to avoid wrongly reconstructed recoil energies caused by pile-up and read-out artefacts. We frame this process as a time series classification task and propose to automate it with neural networks. With a data set of over one million labeled records from 68 detectors, recorded between 2013 and 2019 by CRESST, we test the capability of four commonly used neural network architectures to learn the data cleaning task. Our best performing model achieves a balanced accuracy of 0.932 on our test set. We show on an exemplary detector that about half of the wrongly predicted events are in fact wrongly labeled events, and a large share of the remaining ones have a context-dependent ground truth. We furthermore evaluate the recall and selectivity of our classifiers with simulated data. The results confirm that the trained classifiers are well suited for the data cleaning task., Comment: 12 pages, 8 figures, 6 tables

Published
2022
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2. Towards an automated data cleaning with deep learning in CRESST

Author

Angloher, G., Banik, S., Bartolot, D., Benato, G., Bento, A., Bertolini, A., Breier, R., Bucci, C., Burkhart, J., Canonica, L., D’Addabbo, A., Di Lorenzo, S., Einfalt, L., Erb, A., Feilitzsch, F. v., Iachellini, N. Ferreiro, Fichtinger, S., Fuchs, D., Fuss, A., Garai, A., Ghete, V. M., Gerster, S., Gorla, P., Guillaumon, P. V., Gupta, S., Hauff, D., Ješkovský, M., Jochum, J., Kaznacheeva, M., Kinast, A., Kluck, H., Kraus, H., Lackner, M., Langenkämper, A., Mancuso, M., Marini, L., Meyer, L., Mokina, V., Nilima, A., Olmi, M., Ortmann, T., Pagliarone, C., Pattavina, L., Petricca, F., Potzel, W., Povinec, P., Pröbst, F., Pucci, F., Reindl, F., Rizvanovic, D., Rothe, J., Schäffner, K., Schieck, J., Schmiedmayer, D., Schönert, S., Schwertner, C., Stahlberg, M., Stodolsky, L., Strandhagen, C., Strauss, R., Usherov, I., Wagner, F., Willers, M., Zema, V., and Waltenberger, W.

Published
2023
Full Text
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3. Towards an automated data cleaning with deep learning in CRESST

Author

Angloher, G, Banik, S, Bartolot, D, Benato, G, Bento, A, Bertolini, A, Breier, R, Bucci, C, Burkhart, J, Canonica, L, D'Addabbo, A, Dilorenzo, S, Einfalt, L, Erb, A, Feilitzsch, F, Iachellini, N, Fichtinger, S, Fuchs, D, Fuss, A, Garai, A, Ghete, V, Gerster, S, Gorla, P, Guillaumon, P, Gupta, S, Hauff, D, Jeskovsky, M, Jochum, J, Kaznacheeva, M, Kinast, A, Kluck, H, Kraus, H, Lackner, M, Langenkamper, A, Mancuso, M, Marini, L, Meyer, L, Mokina, V, Nilima, A, Olmi, M, Ortmann, T, Pagliarone, C, Pattavina, L, Petricca, F, Potzel, W, Povinec, P, Probst, F, Pucci, F, Reindl, F, Rizvanovic, D, Rothe, J, Schaffner, K, Schieck, J, Schmiedmayer, D, Schonert, S, Schwertner, C, Stahlberg, M, Stodolsky, L, Strandhagen, C, Strauss, R, Usherov, I, Wagner, F, Willers, M, Zema, V, Waltenberger, W, Angloher G., Banik S., Bartolot D., Benato G., Bento A., Bertolini A., Breier R., Bucci C., Burkhart J., Canonica L., D'Addabbo A., DiLorenzo S., Einfalt L., Erb A., Feilitzsch F., Iachellini N. F., Fichtinger S., Fuchs D., Fuss A., Garai A., Ghete V. M., Gerster S., Gorla P., Guillaumon P. V., Gupta S., Hauff D., Jeskovsky M., Jochum J., Kaznacheeva M., Kinast A., Kluck H., Kraus H., Lackner M., Langenkamper A., Mancuso M., Marini L., Meyer L., Mokina V., Nilima A., Olmi M., Ortmann T., Pagliarone C., Pattavina L., Petricca F., Potzel W., Povinec P., Probst F., Pucci F., Reindl F., Rizvanovic D., Rothe J., Schaffner K., Schieck J., Schmiedmayer D., Schonert S., Schwertner C., Stahlberg M., Stodolsky L., Strandhagen C., Strauss R., Usherov I., Wagner F., Willers M., Zema V., Waltenberger W., Angloher, G, Banik, S, Bartolot, D, Benato, G, Bento, A, Bertolini, A, Breier, R, Bucci, C, Burkhart, J, Canonica, L, D'Addabbo, A, Dilorenzo, S, Einfalt, L, Erb, A, Feilitzsch, F, Iachellini, N, Fichtinger, S, Fuchs, D, Fuss, A, Garai, A, Ghete, V, Gerster, S, Gorla, P, Guillaumon, P, Gupta, S, Hauff, D, Jeskovsky, M, Jochum, J, Kaznacheeva, M, Kinast, A, Kluck, H, Kraus, H, Lackner, M, Langenkamper, A, Mancuso, M, Marini, L, Meyer, L, Mokina, V, Nilima, A, Olmi, M, Ortmann, T, Pagliarone, C, Pattavina, L, Petricca, F, Potzel, W, Povinec, P, Probst, F, Pucci, F, Reindl, F, Rizvanovic, D, Rothe, J, Schaffner, K, Schieck, J, Schmiedmayer, D, Schonert, S, Schwertner, C, Stahlberg, M, Stodolsky, L, Strandhagen, C, Strauss, R, Usherov, I, Wagner, F, Willers, M, Zema, V, Waltenberger, W, Angloher G., Banik S., Bartolot D., Benato G., Bento A., Bertolini A., Breier R., Bucci C., Burkhart J., Canonica L., D'Addabbo A., DiLorenzo S., Einfalt L., Erb A., Feilitzsch F., Iachellini N. F., Fichtinger S., Fuchs D., Fuss A., Garai A., Ghete V. M., Gerster S., Gorla P., Guillaumon P. V., Gupta S., Hauff D., Jeskovsky M., Jochum J., Kaznacheeva M., Kinast A., Kluck H., Kraus H., Lackner M., Langenkamper A., Mancuso M., Marini L., Meyer L., Mokina V., Nilima A., Olmi M., Ortmann T., Pagliarone C., Pattavina L., Petricca F., Potzel W., Povinec P., Probst F., Pucci F., Reindl F., Rizvanovic D., Rothe J., Schaffner K., Schieck J., Schmiedmayer D., Schonert S., Schwertner C., Stahlberg M., Stodolsky L., Strandhagen C., Strauss R., Usherov I., Wagner F., Willers M., Zema V., and Waltenberger W.

Abstract

The CRESST experiment employs cryogenic calorimeters for the sensitive measurement of nuclear recoils induced by dark matter particles. The recorded signals need to undergo a careful cleaning process to avoid wrongly reconstructed recoil energies caused by pile-up and read-out artefacts. We frame this process as a time series classification task and propose to automate it with neural networks. With a data set of over one million labeled records from 68 detectors, recorded between 2013 and 2019 by CRESST, we test the capability of four commonly used neural network architectures to learn the data cleaning task. Our best performing model achieves a balanced accuracy of 0.932 on our test set. We show on an exemplary detector that about half of the wrongly predicted events are in fact wrongly labeled events, and a large share of the remaining ones have a context-dependent ground truth. We furthermore evaluate the recall and selectivity of our classifiers with simulated data. The results confirm that the trained classifiers are well suited for the data cleaning task.

Published
2023

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