Tue·Apr 23, 2024Tuesday·April 23, 2024

1. 01*

   The axially-deformed relativistic quasiparticle random phase approximation based on point-coupling interactions

   A. Ravlić🇺🇸 · T. Nikšić🇭🇷 · Y. F. Niu🇨🇳 · P. Ring🇩🇪 · N. Paar🇭🇷

   Collective nuclear excitations, like giant resonances, are sensitive to nuclear deformation, as evidenced by alterations in their excitation energies and transition strength distributions. A common theoretical framework to study these collective modes, the random-phase approximation (RPA), has to deal with large dimensions spanned by all possible particle-hole configurations satisfying certain symmetries. This work aims to establish a new theoretical framework to study the impact of deformation on spin-isospin excitations, that can provide fast and reliable solutions of the RPA equations. The nuclear ground state is determined with the axially-deformed relativistic Hartree-Bogoliubov (RHB) model based on relativistic point-coupling energy density functionals (EDFs). To study the excitations in the charge-exchange channel, an axially-deformed proton-neutron relativistic quasiparticle RPA (pnRQRPA) is developed in the linear response approach. After benchmarking the axially-deformed pnRQRPA in the spherical limit, a study of spin-isospin excitations including Fermi, Gamow-Teller (GT), and Spin-Dipole (SD) is performed for selected $pf$-shell nuclei. For GT transitions, it is demonstrated that deformation leads to considerable fragmentation of the strength function. A mechanism inducing the fragmentation is studied by decomposing the total strength to different projections of total angular momentum $K$ and constraining the nuclear shape to either spherical, prolate or oblate. A similar fragmentation is also observed for SD transitions, although somewhat moderated by the complex structure of these transitions, while the Fermi strength is almost shape-independent. The axially-deformed pnRQRPA introduced in this work opens perspectives for future studies of deformation effects on astrophysically relevant weak interaction processes, in particular beta decay and electron capture.

   ↳ nucl-thnucl-exPRC(2024)·9 citations
2. 02*

   The environmental low-frequency background for macro-calorimeters at the millikelvin scale

   L. Aragão🇬🇧 · A. Armigliato🇮🇹 · R. Brancaccio🇮🇹 · C. Brofferio🇮🇹 · S. Castellaro🇮🇹 · A. D'Addabbo🇮🇹 · G. De Luca🇮🇹 · F. Del Corso🇮🇹 · S. Di Sabatino🇮🇹 · R. Liu🇺🇸 · L. Marini🇮🇹 · I. Nutini🇮🇹 and 5 other authors

   Many of the most sensitive physics experiments searching for rare events, like neutrinoless double beta ($0\nu \beta \beta$) decay and dark matter interactions, rely on cryogenic macro-calorimeters operating at the mK-scale. Located underground at the Gran Sasso National Laboratory (LNGS), in central Italy, CUORE (Cryogenic Underground Observatory for Rare Events) is one of the leading experiments for the search of $0\nu \beta \beta$ decay, implementing the low-temperature calorimetric technology. We present a novel multi-detector analysis to correlate environmental phenomena with the low-frequency noise of low-temperature calorimeters. Indeed, the correlation of marine and seismic data with data from a pair of CUORE detectors indicates that cryogenic detectors are sensitive not only to intense vibrations generated by earthquakes, but also to the much fainter vibrations induced by marine microseisms in the Mediterranean Sea due to the motion of sea waves. Proving that cryogenic macro-calorimeters are sensitive to such environmental sources of noise opens the possibility of studying their impact on the detectors physics-case sensitivity. Moreover, this study could pave the road for technology developments dedicated to the mitigation of the noise induced by marine microseisms, from which the entire community of cryogenic calorimeters can benefit.

   ↳ physics.ins-detnucl-exphysics.ao-phEPJC(2024)·6 citations
3. 03*

   Investigating Resource-efficient Neutron/Gamma Classification ML Models Targeting eFPGAs

   Jyothisraj Johnson · Billy Boxer🇺🇸 · Tarun Prakash · Carl Grace🇺🇸 · Peter Sorensen · Mani Tripathi🇺🇸

   There has been considerable interest and resulting progress in implementing machine learning (ML) models in hardware over the last several years from the particle and nuclear physics communities. A big driver has been the release of the Python package, hls4ml, which has enabled porting models specified and trained using Python ML libraries to register transfer level (RTL) code. So far, the primary end targets have been commercial FPGAs or synthesized custom blocks on ASICs. However, recent developments in open-source embedded FPGA (eFPGA) frameworks now provide an alternate, more flexible pathway for implementing ML models in hardware. These customized eFPGA fabrics can be integrated as part of an overall chip design. In general, the decision between a fully custom, eFPGA, or commercial FPGA ML implementation will depend on the details of the end-use application. In this work, we explored the parameter space for eFPGA implementations of fully-connected neural network (fcNN) and boosted decision tree (BDT) models using the task of neutron/gamma classification with a specific focus on resource efficiency. We used data collected using an AmBe sealed source incident on Stilbene, which was optically coupled to an OnSemi J-series SiPM to generate training and test data for this study. We investigated relevant input features and the effects of bit-resolution and sampling rate as well as trade-offs in hyperparameters for both ML architectures while tracking total resource usage. The performance metric used to track model performance was the calculated neutron efficiency at a gamma leakage of 10${}^{-3}$. The results of the study will be used to aid the specification of an eFPGA fabric, which will be integrated as part of a test chip.

   ↳ cs.LGhep-exnucl-exphysics.ins-detJINST(2024)·3 citations

* 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.