Mon·Nov 17, 2025Monday·November 17, 2025

1. 01*

   New Ground State in ${}^{149}$La Removes Two-Neutron-Separation-Energy Anomaly in Lanthanum Isotopes

   S. Kimura · M. Wada · H. Haba · Y. Hirayama · H. Ishiyama · Y. Ito🇯🇵 · T. Niwase · M. Rosenbusch🇩🇪 · P. Schury · H. Ueno🇯🇵 · Y.X. Watanabe🇺🇸 · Y. Yamanouchi

   Nuclear mass is a key indicator of how the nuclear shell structure evolves. The recent mass measurement study of neutron-rich lanthanum isotopes [A. Jaries, $etal$., Phys. Rev. Lett. {\bf 134}, 042501(2025)] reveals the presence of a distinct prominence in their two-neutron separation energies. However, its presence has been called into question based on the results of another mass determination [B. Liu, Ph.D. thesis, University of Notre Dame (2025)]. In this letter, we report an effort to clarify these contradictory results through the use of the simultaneous mass-lifetime measurement of the neutron-rich lanthanum isotope ${}^{149}$La using a multi-reflection time-of-flight mass spectrograph combined with a $\beta$-TOF detector. The peak corresponding to a $\beta$-decaying state was observed in the time-of-flight spectra at a position of $221(6)keV/{\mathrm{c}}^2$ lighter than the reported ${}^{149}$La mass in A. Jaries, $etal$., but our measured result is in excellent agreement with the mass value reported in B. Liu. We have concluded that this peak is the ground state of ${}^{149}$La. With this, the previously reported distinct prominence in the two-neutron separation energies disappears, while a new kink structure, similar to that in the cerium isotopes, appears. Comparison with theoretical models suggests that a nuclear shape transition from octupole deformation to another type of deformation occurs around $N=91$ and is likely the cause of this kink structure.

   nucl-exPRL(2026)·2 citations
2. 02*

   Constraining Neutron Capture Cross Sections for ${}^{88}\mathrm{Y}$ with Gamma-ray Strength Function in $(p,p^{\prime }\gamma )$ Surrogate Reaction

   Shu-Tong Zhang · Wen Luo🇨🇳 · Dan-Yang Pang🇨🇳 · Zhi-Cai Li🇨🇳 · Jing Feng🇨🇳 · Bing JIang · Xin-Xiang Li🇨🇳 · Yi Xu🇨🇳 · Bao-Hua Sun

   We demonstrate to extract ${}^{88}\mathrm{Y}(n,\gamma )$ cross sections using the $(p,p^{\prime }\gamma )$ surrogate reaction with proper treatment of the spin-parity distribution of the compound nucleus ${}^{89}\mathrm{Y}$. Experimental data of both $\gamma$-decay probability and $\gamma$-ray strength function are used to constrain the nuclear model parameters within a computational framework combining the Bayesian optimization and Markov chain Monte Carlo method, which helps to significantly reduce the $(n,\gamma )$ data uncertainty. The ${}^{88}\mathrm{Y}(n,\gamma )$ cross sections are then extracted with a narrow uncertainty of 7.6\%-23.1\% within neutron energy range of 0.01 to 3.0 MeV for the first time, where no experimental data are available. Moreover, our method is verified with the ${}^{88}\mathrm{Sr}(p,\gamma )$ reaction, of which the measured data are available for comparison. This work opens interesting perspectives on the matter of extracting ($n,\gamma$) reaction cross sections on unstable nuclei as surrogate reaction experiments are becoming widely available.

   ↳ nucl-thnucl-ex0 citations
3. 03*

   Sparse Methods for Vector Embeddings of TPC Data

   Tyler Wheeler · Michelle P. Kuchera🇺🇸 · Raghuram Ramanujan🇺🇸 · Ryan Krupp · Chris Wrede🇺🇸 · Saiprasad Ravishankar🇺🇸 · Connor L. Cross · Hoi Yan Ian Heung · Andrew J. Jones · Benjamin Votaw

   Time Projection Chambers (TPCs) are versatile detectors that reconstruct charged-particle tracks in an ionizing medium, enabling sensitive measurements across a wide range of nuclear physics experiments. We explore sparse convolutional networks for representation learning on TPC data, finding that a sparse ResNet architecture, even with randomly set weights, provides useful structured vector embeddings of events. Pre-training this architecture on a simple physics-motivated binary classification task further improves the embedding quality. Using data from the GAseous Detector with GErmanium Tagging (GADGET) II TPC, a detector optimized for measuring low-energy $\beta$-delayed particle decays, we represent raw pad-level signals as sparse tensors, train Minkowski Engine ResNet models, and probe the resulting event-level embeddings which reveal rich event structure. As a cross-detector test, we embed data from the Active-Target TPC (AT-TPC) -- a detector designed for nuclear reaction studies in inverse kinematics -- using the same encoder. We find that even an untrained sparse ResNet model provides useful embeddings of AT-TPC data, and we observe improvements when the model is trained on GADGET data. Together, these results highlight the potential of sparse convolutional techniques as a general tool for representation learning in diverse TPC experiments.

   ↳ cs.LGnucl-ex1 citation
4. 04*

   Exclusive photoproduction of a ${\pi }^0\gamma$ pair in the saturation framework

   Michael Fucilla🇫🇷 · Saad Nabeebaccus🇫🇷 · Lech Szymanowski🇵🇱 · Samuel Wallon🇫🇷 · Joseph Yarwick🇫🇷

   We consider the exclusive photoproduction of a ${\pi }^0\gamma$ pair with large invariant mass, as a promising channel to study the effects of gluon saturation. It has recently been demonstrated that this process is incompatible with a collinear factorization approach in terms of generalized parton distributions (GPDs) at the leading twist. In such a situation, a (generalized) $k_T$-dependent factorization at small $x$ is a valid alternative approach. We perform this calculation using the shockwave formalism, which resums multiple gluon exchanges between the projectile and the dense nuclear target. We find that the polarized amplitude changes sign as a function of back-to-back transverse momentum $|{\vec{p}}_t|$ of the pion-photon pair, resulting in a dip-like structure in the fully differential cross section as a function of $|{\vec{p}}_t|$.

   ↳ hep-phhep-exnucl-exnucl-th2 citations
5. 05*

   Regularized Unfolding of gamma-ray Spectra for Nuclear Physics Applications

   E. Lima · L. L. Braseth🇳🇴 · A. H. Mjøs · M. Hjorth-Jensen🇳🇴 · A. Kvellestad🇳🇴 · A. C. Larsen🇳🇴

   Reconstructing gamma-ray spectra from detector measurements is an ill-posed inverse problem. Standard methods, such as Folding Iteration with Compton Subtraction (FICS), provide point estimates but lack calibrated uncertainties and may bias the spectrum. We introduce an unfolding framework based on regularized maximum-likelihood estimation (RMLE) that enforces non-negativity and detector-response constraints while explicitly modeling background and contaminant contributions. Simulations and analytical results show that RMLE yields smoother reconstructions with well-calibrated confidence intervals and outperforms existing techniques for low-complexity spectra. Although high-complexity data remain challenging, the intervals produced by RMLE maintain correct coverage.

   ↳ physics.ins-detnucl-exphysics.data-an1 citation

* Reconstructed cohort: no mailing for this day survives in the archive. Papers are grouped by their submission times and arXiv's announcement cut-off, assuming announcement without delay; positions follow identifier order. Validated at ~91% exact-day agreement against the archived era.