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The Arctic is undergoing rapid changes in climate, altering the status and functioning of high-latitude soils and permafrost. The vast majority of studies on Arctic soils and permafrost are conducted during the summer period due to ease of accessibility, sampling, instrument operation, and making measurements, in comparison to during winter and transition seasons. However, there is increasing evidence that microbial activity continues in Arctic soils outside of the summer period. Moreover, it is becoming clear that understanding the seasonal dynamics of Arctic soils is of critical importance, especially considering that the under-studied winter is the period that is most sensitive to climate warming. Soil biogeochemical models have advanced our understanding of the functioning and fate of soils in the Arctic, however it is vital that seasonality in biotic and abiotic processes is accurately captured in these models. Here we synthesize recent investigations and observations of the year-round functioning of Arctic soils, review soil biogeochemical modelling frameworks, and highlight certain processes and behaviors that are shaped by seasonality and thus warrant particular consideration within these models. More attention to seasonal processes will be critical to improving datasets and soil biogeochemical models that can be used to understand the year-round functioning of soils and the fate of the soil carbon reservoir in the Arctic. 

ABSTRACT Obtaining accurately calibrated redshift distributions of photometric samples is one of the great challenges in photometric surveys like LSST, Euclid, HSC, KiDS, and DES. We present an inference methodology that combines the redshift information from the galaxy photometry with constraints from two-point functions, utilizing cross-correlations with spatially overlapping spectroscopic samples, and illustrate the approach on CosmoDC2 simulations. Our likelihood framework is designed to integrate directly into a typical large-scale structure and weak lensing analysis based on two-point functions. We discuss efficient and accurate inference techniques that allow us to scale the method to the large samples of galaxies to be expected in LSST. We consider statistical challenges like the parametrization of redshift systematics, discuss and evaluate techniques to regularize the sample redshift distributions, and investigate techniques that can help to detect and calibrate sources of systematic error using posterior predictive checks. We evaluate and forecast photometric redshift performance using data from the CosmoDC2 simulations, within which we mimic a DESI-like spectroscopic calibration sample for cross-correlations. Using a combination of spatial cross-correlations and photometry, we show that we can provide calibration of the mean of the sample redshift distribution to an accuracy of at least 0.002(1 + z), consistent with the LSST-Y1 science requirements for weak lensing and large-scale structure probes. 

Asymptotic giant branch stars are responsible for the production of most of the heavy isotopes beyond Sr observed in the solar system. Among them, isotopes shielded from the $r$-process contribution by their stable isobars are defined as $s$-only nuclei. For a long time the abundance of ${}^{204}\mathrm{Pb}$, the heaviest $s$-only isotope, has been a topic of debate because state-of-the-art stellar models appeared to systematically underestimate its solar abundance. Besides the impact of uncertainties from stellar models and galactic chemical evolution simulations, this discrepancy was further obscured by rather divergent theoretical estimates for the neutron capture cross section of its radioactive precursor in the neutron-capture flow, ${}^{204}\mathrm{Tl}$( $t_{1/2}=3.78\mathrm{yr}$), and by the lack of experimental data on this reaction. We present the first ever neutron capture measurement on ${}^{204}\mathrm{Tl}$, conducted at the CERN neutron time-of-flight facility n_TOF, employing a sample of only 9 mg of ${}^{204}\mathrm{Tl}$produced at the Institute Laue Langevin high flux reactor. By complementing our new results with semiempirical calculations we obtained, at the $s$-process temperatures of $kT\approx 8\mathrm{keV}$and $kT\approx 30\mathrm{keV}$, Maxwellian-averaged cross sections (MACS) of 580(168) mb and 260(90) mb, respectively. These figures are about 3% lower and 20% higher than the corresponding values widely used in astrophysical calculations, which were based only on theoretical calculations. By using the new ${}^{204}\mathrm{Tl}$MACS, the uncertainty arising from the ${}^{204}\mathrm{Tl}(\mathrm{n},\gamma )$cross section on the $s$-process abundance of ${}^{204}\mathrm{Pb}$has been reduced from $\sim 30\%$down to $+8\%/-6\%$, and the $s$-process calculations are in agreement with the latest solar system abundance of ${}^{204}\mathrm{Pb}$reported by K. Lodders in 2021. Published by the American Physical Society2024 

Abstract A new search for two-neutrino double-beta (2νββ) decay of¹³⁶Xe to theexcited state of¹³⁶Ba is performed with the full EXO-200 dataset. A deep learning-based convolutional neural network is used to discriminate signal from background events. Signal detection efficiency is increased relative to previous searches by EXO-200 by more than a factor of two. With the addition of the Phase II dataset taken with an upgraded detector, the median 90% confidence level half-life sensitivity of 2νββdecay to thestate of¹³⁶Ba isyr using a total¹³⁶Xe exposure of 234.1 kg yr. No statistically significant evidence for 2νββdecay to thestate is observed, leading to a lower limit ofyr at 90% confidence level, improved by 70% relative to the current world's best constraint.