Fri·Apr 15, 2022Friday·April 15, 2022

1. 01*

   Performance of a convolutional autoencoder designed to remove electronic noise from p-type point contact germanium detector signals

   Mark R. Anderson🇨🇦 · Vasundhara Basu🇺🇸 · Ryan D. Martin🇨🇦 · Charlotte Z. Reed · Noah J. Rowe · Mehdi Shafiee · Tianai Ye🇺🇸

   We present a convolutional autoencoder to denoise pulses from a p-type point contact high-purity germanium detector similar to those used in several rare event searches. While we focus on training procedures that rely on detailed detector physics simulations, we also present implementations requiring only noisy detector pulses to train the model. We validate our autoencoder on both simulated data and calibration data from an ${}^{241}$Am source, the latter of which is used to show that the denoised pulses are statistically compatible with data pulses. We demonstrate that our denoising method is able to preserve the underlying shapes of the pulses well, offering improvement over traditional denoising methods. We also show that the shaping time used to calculate energy with a trapezoidal filter can be significantly reduced while maintaining a comparable energy resolution. Under certain circumstances, our denoising method can improve the overall energy resolution. The methods we developed to remove electronic noise are straightforward to extend to other detector technologies. Furthermore, the latent representation from the encoder is also of use in quantifying shape-based characteristics of the signals. Our work has great potential to be used in particle physics experiments and beyond.

   nucl-exphysics.data-anphysics.ins-detEPJC(2022)·6 citations
2. 02*

   High-precision half-life determination of ${}^{14}$O via direct $\beta$ counting

   S. Sharma🇨🇦 · G.F. Grinyer🇨🇦 · G.C. Ball🇨🇦 · J.R. Leslie🇨🇦 · C.E. Svensson🇨🇦 · F.A. Ali🇨🇦 · C. Andreoiu🇨🇦 · N. Bernier🇨🇦 · S.S. Bhattacharjee🇨🇦 · V. Bildstein🇨🇦 · C. Burbadge🇨🇦 · R. Caballero-Folch🇨🇦 and 21 other authors

   The half-life of the superallowed Fermi ${\beta }^{+}$ emitter ${}^{14}$O was determined to high precision via a direct $\beta$ counting experiment performed at the Isotope Separator and Accelerator (ISAC) facility at TRIUMF. The result, $T_{1/2}$(${}^{14}$O) = 70619.2(76) ms, is consistent with, but is more precise than, the world average obtained from 11 previous measurements. Combining the ${}^{14}$O half-life deduced in the present work with the previous most precise measurements of this quantity leads to a reduction in the overall uncertainty, by nearly a factor of 2. The new world average is $T_{1/2}$(${}^{14}$O) = 70619.6(63) ms with a reduced ${\chi }^2$ value of 0.87 obtained from 8 degrees of freedom.

   nucl-exEPJA(2022)·1 citation
3. 03*

   Using Machine Learning for Particle Identification in ALICE

   Łukasz Kamil Graczykowski🇵🇱 · Monika Jakubowska🇵🇱 · Kamil Rafał Deja🇵🇱 · Maja Kabus (for the ALICE Collaboration)🇵🇱

   Particle identification (PID) is one of the main strengths of the ALICE experiment at the LHC. It is a crucial ingredient for detailed studies of the strongly interacting matter formed in ultrarelativistic heavy-ion collisions. ALICE provides PID information via various experimental techniques, allowing for the identification of particles over a broad momentum range (from around 100 MeV/$c$ to around 50 GeV/$c$). The main challenge is how to combine the information from various detectors effectively. Therefore, PID represents a model classification problem, which can be addressed using Machine Learning (ML) solutions. Moreover, the complexity of the detector and richness of the detection techniques make PID an interesting area of research also for the computer science community. In this work, we show the current status of the ML approach to PID in ALICE. We discuss the preliminary work with the Random Forest approach for the LHC Run 2 and a more advanced solution based on Domain Adaptation Neural Networks, including a proposal for its future implementation within the ALICE computing software for the upcoming LHC Run 3.

   nucl-exhep-exstat.MLJINST(2022)·21 citations
4. 04*

   Background Shielding by Dense Samples in Low-Level Gamma Spectrometry

   M. Thiesse🇬🇧 · P. Scovell🇬🇧 · L. Thompson

   In low activity gamma spectrometric measurements of large, dense samples, the bulk sample material shields the HPGe crystal from external background sources. If not accounted for in studies that utilise background-subtraction methods, this effect may result in systematic errors in the sample activity and detection limit estimation. We introduce a Monte Carlo based method to minimise the impact of this effect on sample gamma spectra. It is validated using simulated detector backgrounds and applied to a measurement of low-activity Gd${}_2$(SO${}_4$)${}_3\cdot$8H${}_2$O. One main prerequisite for the correct application of this method is to know in advance the nuclides which contribute to the detector background spectrum and their spatial distribution. With a thorough understanding of the detector backgrounds, the method improves the accuracy of sensitive low-background measurements of low-activity samples. Even without knowing the background sources and their distribution, conservative results may still be presented that account for the potential systematic errors introduced by this background shielding effect.

   ↳ physics.ins-detnucl-exphysics.data-anAppl.Radiat.Isot.(2022)·5 citations
5. 05*

   Calculation of $\beta$-decay half-lives with Skyrme Hartree-Fock-Bogoliubov+$pn$-QRPA and isoscalar pairing strengths optimized by a Bayesian method

   Futoshi Minato🇯🇵 · Z. M. Niu🇨🇳 · Haozhao Liang🇯🇵

   For radioactive nuclear data, $\beta$ decay is one of the most important information and is applied to various fields. However, some of the $\beta$-decay data are not available due to experimental difficulties. From this respect, theoretically calculated results have been embedded in the $\beta$-decay data to compensate the missing information. To calculate the $\beta$-decay half-lives, a proton-neutron quasi-particle random phase approximation on top of a Skryme energy density functional is applied with an assumption of spherical symmetry. The isoscalar pairing strength is estimated by a Bayesian neutral network (BNN). We verify the predicted isoscalar pairing strengths by preparing the training data and test data. It was confirmed that the finite-range isovector pairing ensures the $\beta$-decay half-lives insensitive to the model space, while the zero-range one was largely dependent on it. The half-lives calculated with the BNN isoscalar pairing strengths reproduced most of experimental data, although those of highly deformed nuclei were underestimated. We also studied that the predictive performance on new experimental data that were not used for the BNN training and found that they were reproduced well. Our study demonstrates that the isoscalar pairing strengths determined by the BNN can reproduce experimental data in the same accuracy as other theoretical works.

   ↳ nucl-thnucl-exPRC(2022)·19 citations
6. 06*

   New calculations of neutron and kaon decays

   Chien-Yeah Seng🇩🇪

   We describe two recent developments of the radiative correction theory in semileptonic beta decays. For neutron, a novel dispersive analysis of the $\gamma W$-box diagram making use of neutrino scattering data leads to an improved precision and a shifted central value of $V_{ud}$; for kaon, an appropriate combination of meson form factors, lattice QCD inputs and chiral perturbation theory gives a much-improved determination of the radiative corrections in semileptonic kaon decays. These new calculations help to unveil/sharpen a series of anomalies in the measurements of $V_{ud}$ and $V_{us}$ that may point towards the existence of new physics.

   ↳ hep-phhep-exhep-latnucl-ex+10 citations
7. 07*

   Constraining nucleosynthesis in neutrino-driven winds: observations, simulations and nuclear physics

   A. Psaltis🇨🇦 · A. Arcones🇩🇪 · F. Montes🇺🇸 · P. Mohr🇭🇺 · C.J. Hansen🇩🇪 · M. Jacobi🇩🇪 · H. Schatz🇺🇸

   A promising astrophysical site to produce the lighter heavy elements of the first $r$-process peak ($Z=38-47$) is the moderately neutron rich ($0.4<Y_e<0.5$) neutrino-driven ejecta of explosive environments, such as core-collapse supernovae and neutron star mergers, where the weak $r$-process operates. This nucleosynthesis exhibits uncertainties from the absence of experimental data from $(\alpha ,xn)$ reactions on neutron-rich nuclei, which are currently based on statistical model estimates. In this work, we report on a new study of the nuclear reaction impact using a Monte Carlo approach and improved $(\alpha ,xn)$ rates based on the Atomki-V2 $\alpha$ Optical Model Potential ($\alpha$OMP). We compare our results with observations from an up-to-date list of metal-poor stars with [Fe/H] $<$ -1.5 to find conditions of the neutrino-driven wind where the lighter heavy elements can be synthesized. We identified a list of $(\alpha ,xn)$ reaction rates that affect key elemental ratios in different astrophysical conditions. Our study aims on motivating more nuclear physics experiments on $(\alpha ,xn)$ reactions using current and the new generation of radioactive beam facilities and also more observational studies of metal-poor stars.

   ↳ astro-ph.HEnucl-exApJ(2022)·31 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.