Joint machine learning analysis of muon spectroscopy data from different materials

no code implementations • 17 Dec 2021 • T. Tula, G. Möller, J. Quintanilla, S. R. Giblin, A. D. Hillier, E. E. McCabe, S. Ramos, D. S. Barker, S. Gibson

A change in the asymmetry function might indicate a phase transition; however, these changes can be very subtle, and existing methods of analyzing the data require knowledge about the specific physics of the material.

BIG-bench Machine Learning

Correlated pion-proton pair emission off hot and dense QCD matter

no code implementations • 2 Dec 2020 • J. Adamczewski-Musch, O. Arnold, C. Behnke, A. Belounnas, A. Belyaev, J. C. Berger-Chen, A. Blanco, C. Blume, M. B"ohmer, P. Bordalo, S. Chernenko, L. Chlad, I. Ciepal, C. Deveaux, J. Dreyer, E. Epple, L. Fabbietti, O. Fateev, P. Filip, P. Fonte, C. Franco, J. Friese, I. Fr"ohlich, T. Galatyuk, J. A. Garzon, R. Gernh"auser, M. Golubeva, R. Greifenhagen, F. Guber, M. Gumberidze, S. Harabasz, T. Heinz, T. Hennino, S. Hlavac, C. H"ohne, R. Holzmann, A. Ierusalimov, A. Ivashkin, B. K"ampfer, T. Karavicheva, B. Kardan, I. Koenig, W. Koenig, M. Kohls, B. W. Kolb, G. Korcyl, G. Kornakov, F. Kornas, R. Kotte, A. Kugler, T. Kunz, A. Kurepin, A. Kurilkin, P. Kurilkin, V. Ladygin, R. Lalik, K. Lapidus, A. Lebedev, L. Lopes, M. Lorenz, T. Mahmoud, L. Maier, A. Malige, A. Mangiarotti, J. Markert, T. Matulewicz, S. Maurus, V. Metag, J. Michel, D. M. Mihaylov, S. Morozov, C. M"untz, R. M"unzer, L. Naumann, K. Nowakowski, Y. Parpottas, V. Pechenov, O. Pechenova, O. Petukhov, K. Piasecki, J. Pietraszko, W. Przygoda, K. Pysz, S. Ramos, B. Ramstein, N. Rathod, A. Reshetin, P. Rodriguez-Ramos, P. Rosier, A. Rost, A. Sadovsky, P. Salabura, T. Scheib, H. Schuldes, E. Schwab, F. Scozzi, F. Seck, P. Sellheim, I. Selyuzhenkov, J. Siebenson, L. Silva, U. Singh, J. Smyrski, Yu. G. Sobolev, S. Spataro, S. Spies, H. Str"obele, J. Stroth, C. Sturm, O. Svoboda, M. Szala, P. Tlusty, M. Traxler, H. Tsertos, E. Usenko, V. Wagner, C. Wendisch, M. G. Wiebusch, J. Wirth, D. Wojcik, Y. Zanevsky, P. Zumbruch

In this letter we report the first multi-differential measurement of correlated pion-proton pairs from 2 billion Au+Au collisions at \sqrt{s_{NN}} = 2. 42 GeV collected with HADES.

Nuclear Experiment

Machine Learning approach to muon spectroscopy analysis

no code implementations • 9 Oct 2020 • T. Tula, G. Möller, J. Quintanilla, S. R. Giblin, A. D. Hillier, E. E. McCabe, S. Ramos, D. S. Barker, S. Gibson

We discovered that the PCA method works well in detecting phase transitions in muon spectroscopy experiments and can serve as an alternative to current analysis, especially if the physics of the studied material are not entirely known.

BIG-bench Machine Learning