Results for “Avalanche variance”

DOE OSTI2022

Avalanche gain and its effect on energy resolution in GEM-based detectors

Here, we present avalanche gain and associated resolution measurements recorded with a 4 He:CO 2 (70:30) gas mixture and pure SF 6 , a Negative Ion (NI) gas. SF 6 is of particular interest to the directional dark matter detection community, as its low thermal diffusion helps to retain recoil ionization track features over long drift lengths. With the aid of a general form of the reduced first Townsend coefficient (RFTC), multiple GEM-based detector data sets are used to study the high-gain behavior of the 4 He:CO 2 gas mixture. The high-gain data is well described purely in terms of the reduced electric field strength and the number of GEMs, and the robust relationship between the RFTC and the average, reduced, electric field strength across the GEMs is emphasized. The associated (pulse-height) resolution measurements are used to discuss the variance of the avalanche distribution and to describe and estimate the lower limits of energy resolution one should expect to measure using a simple relationship with the RFTC. In the end, a description of avalanche gain, its effect on energy resolution, and the contributing experimental parameters in GEM-based detectors is developed over a broad parameter space for further use.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

Fine-scale landscape characteristics, vegetation composition, and snowmelt timing control phenological heterogeneity across low-Arctic tundra landscapes in Western Alaska

The Arctic is warming at over twice the rate of the rest of the Earth, resulting in significant changes in vegetation seasonality that regulates annual carbon, water, and energy fluxes. However, a crucial knowledge gap exists regarding the intricate interplay among climate, permafrost, and vegetation that generates high phenology variability across extensive tundra landscapes. This oversight has led to significant discrepancies in phenological patterns observed across warming experiments, long-term ecological observations, and satellite and modeling studies, undermining our ability to understand and forecast plant responses to climate change in the Arctic. To address this problem, we assessed plant phenology across three low-Arctic tundra landscapes on the Seward Peninsula, Alaska, using a combination of in-situ phenocam observations and high-resolution PlanetScope CubeSat data. We examined the patterns and drivers of phenological diversity across the landscape by (1) quantifying phenological diversity among dominant plant function types (PFTs) and (2) modeling the interrelations between plant phenology and fine-scale landscape features, such as topography, snowmelt, and vegetation. Our findings reveal that both spring and fall phenology varied significantly across Arctic PFTs, accounting for about 25%–44% and 34%–59% of the landscape-scale variation in the start of spring [SOS] and start of fall [SOF], respectively. Deciduous tall shrubs (e.g. alder and willow) had a later SOS (~7 d behind the mean of other PFTs), but completed leaf expansion (within 2 weeks) considerably faster compared to other PFTs. We modeled the landscape-scale variation in SOS and SOF using Random Forest, which showed that plant phenology can be accurately captured by a suite of variables related to vegetation composition, topographic characteristics, and snowmelt timing (variance explained: 53%–68% for SOS and 59%–82% for SOF). Notably, snowmelt timing was a crucial determinant of SOS, a factor often neglected in most spring phenology models. Our study highlights the impact of fine-scale vegetation composition, snow seasonality, and landscape features on tundra phenological heterogeneity. Improved understanding of such considerable intra-site phenological variability and associated proximate controls across extensive Arctic landscapes offers critical insights for representation of tundra phenology in process models and associated impact assessments with climate change.

54 ENVIRONMENTAL SCIENCES↗