Oct 3 – 7, 2022
Science Culture Center, IBS
Asia/Seoul timezone

Particle identification of VAMOS++ spectrometer data using several machine learning techniques

Oct 4, 2022, 9:52 PM
S236 (Science Culture Center, IBS)


Science Culture Center, IBS

55 EXPO-ro, Yuseong-gu, Daejeon
Poster Session Poster Session


Youngju Cho (SNU)


The studies of low-lying excited states of the neutron-rich nuclei near the shell closures are one of the foremost topics of nuclear physics. The information of unstable, neutron-rich nuclei near N=126 magicity below 208Pb is crucial for understanding not only the nuclear structure of heavy nuclei but also the astrophysical r-process. However, the study of the south of 208Pb in the nuclear chart has been limited due to the difficulty of producing those nuclei.
We approached this region of interest using multi-nucleon transfer (MNT) reactions between 136Xe beam (7MeV/u) and 198Pt target. The experiment was performed at GANIL G1 hall. The VAMOS++ magnetic spectrometer [1] was set to grazing angle (40˚) with respect to the beam axis and used to identify projectile-like fragments (PLFs). The complementary target-like fragments’ (TLFs) velocity vector was measured by the newly installed second arm set to the complementary angle (55˚). AGATA HPGe tracking array [2] with nominal configuration [3] measures the prompt gamma rays from the excited states of the produced nuclei. Additionally, the delayed gamma rays from TLFs were measured by EXOGAM HPGe clover array [4] located at the end of the second arm.
Unambiguous particle identification (PID) of PLFs from VAMOS++ data is the prerequisite for figuring out the origin of detected gamma rays in TLFs. Conventionally, multi-parameter analysis was carried out for PID due to the complex setup and reconstruction method. This method needs a lot of effort especially when ion energy has a broad range near a few MeV/u.
Therefore, we developed the new method using several machine learning techniques for PID. The supervised learning with the deep neural network (DNN) and boosted decision tree (BDT) was used to calculate the ion energy which is critical for mass and ion charge states calculation. The mass and ion charge state resolution show improved value compared to reported in the literature using the conventional analysis technique [5].
The atomic number (Z) identification was treated as multi-class classification problem with soft labels. We used semi-supervised learning to identify the nuclear charge state of a particle and to calculate its confidence. Compared to the conventional ΔE-E method, additional physical measurements such as velocity and mass can be used to deduce the nuclear charge state more accurately.

Primary authors

Youngju Cho (SNU) Yunghee Kim (Center for Exotic Nuclear Studies, IBS) Prof. Seonho Choi (SNU) Mr Yonghyun Son (SNU) Dr Andrey Andreev (Univ. of York) Dr Philipp John (TU Darmstadt) Mr Ablaihan Utepov (GANIL) Dr Dieter Ackermann (GANIL) Dr Navin Alahari (GANIL) Dr Sunghan Bae (IBS CENS) Dr Ranabir Banik (VECC) Dr Soumik Bhattacharya (VECC) Dr Sarmishtha Bhattacharyya (VECC) Dr Kyungyuk Chae (SKKU) Dr Gilles De France (GANIL) Dr Francois Didierjean (IPHC) Dr Jeremie Dudouet (IP2I Lyon) Dr Chloe Fougeres (GANIL) Dr George Fremont (GANIL) Dr Joan Goupil (GANIL) Dr Gyoungmo Gu (SKKU) Dr Jeongsu Ha (KU Leuven) Dr Yoshikazu Hirayama (KEK) Dr Sunchan Jeong (KEK) Dr Chanhee Kim (SKKU) Dr Minju Kim (SKKU) Dr Sohyun Kim (SKKU) Dr Wolfram Korten (CEA) Dr Antoine Lemasson (GANIL) Dr Paola Marini (CENBG) Dr Hiroari Miyatake (KEK) Dr Momo Mukai (RIKEN) Dr Gopal Mukherjee (VECC) Dr Toshitaka Niwase (KEK) Dr Joochun Park (IBS CENS) Dr Rosa Maria Perez Vidal (INFN-LNL) Dr Diego Ramos (GANIL) Dr Francesco Recchia (Univ. of Padova) Dr Maurycy Rejmund (GANIL) Dr Kseniia Rezynkina (INFN-Padova) Dr Marco Rosenbusch (RIKEN) Dr Peter Schury (RIKEN) Dr Jean-Charle Thomas (GANIL) Dr Deby Treasa (CENBG) Dr Igor Tsekhanovich (CENBG) Dr Yutaka Watanabe (KEK) Dr Giacomo de Angelis (INFN-LNL)

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