NoSOCS in SDSS. VI. The Environmental Dependence of AGN in Clusters and Field in the Local Universe

We investigated the variation in the fraction of optical active galactic nuclei (AGN) hosts with stellar mass, as well as their local and global environments. Our sample is composed of cluster members and field galaxies at $z \le 0.1$ and we consider only strong AGN. We find a strong variation in the AGN fraction ($F_{AGN}$) with stellar mass. The field population comprises a higher AGN fraction compared to the global cluster population, especially for objects with log $M_* > 10.6$. Hence, we restricted our analysis to more massive objects. We detected a smooth variation in the $F_{AGN}$ with local stellar mass density for cluster objects, reaching a plateau in the field environment. As a function of clustercentric distance we verify that $F_{AGN}$ is roughly constant for R $>$ R$_{200}$, but show a steep decline inwards. We have also verified the dependence of the AGN population on cluster velocity dispersion, finding a constant behavior for low mass systems ($\sigma_P \lesssim 650-700$ km s$^{-1}$). However, there is a strong decline in $F_{AGN}$ for higher mass clusters ($>$ 700 km s$^{-1}$). When comparing the $F_{AGN}$ in clusters with or without substructure we only find different results for objects at large radii (R $>$ R$_{200}$), in the sense that clusters with substructure present some excess in the AGN fraction. Finally, we have found that the phase-space distribution of AGN cluster members is significantly different than other populations. Due to the environmental dependence of $F_{AGN}$ and their phase-space distribution we interpret AGN to be the result of galaxy interactions, favored in environments where the relative velocities are low, typical of the field, low mass groups or cluster outskirts.Comment: 11 pages, 10 figures, Accepted to MNRA

Classifying LEP Data with Support Vector Algorithms

We have studied the application of different classification algorithms in the analysis of simulated high energy physics data. Whereas Neural Network algorithms have become a standard tool for data analysis, the performance of other classifiers such as Support Vector Machines has not yet been tested in this environment. We chose two different problems to compare the performance of a Support Vector Machine and a Neural Net trained with back-propagation: tagging events of the type e+e- -> ccbar and the identification of muons produced in multihadronic e+e- annihilation events.Comment: 7 pages, 4 figures, submitted to proceedings of AIHENP99, Crete, April 199

Robust magnetometry with single nitrogen-vacancy centers via two-step optimization

Shallow nitrogen-vacancy (NV) centers are promising candidates for high-precision sensing applications; these defects, when positioned a few nanometers below the surface, provide an atomic-scale resolution along with substantial sensitivity. However, the dangling bonds and impurities on the diamond surface result in a complex environment which reduces the sensitivity and is unique to each shallow NV center. To avoid the environment's detrimental effect, we apply feedback-based quantum optimal control. We first show how a direct search can improve the initialization and readout process. In a second step, we optimize microwave pulses for pulsed optically detected magnetic resonance (ODMR) and Ramsey measurements. Throughout the sensitivity optimizations, we focus on robustness against errors in the control field amplitude. This feature not only protects the protocols' sensitivity from drifts but also enlarges the sensing volume. The resulting ODMR measurements produce sensitivities below 1μT Hz-12 for an 83% decrease in control power, increasing the robustness by approximately one third. The optimized Ramsey measurements produce sensitivities below 100 nT Hz-12 giving a twofold sensitivity improvement. Being on par with typical sensitivities obtained via single NV magnetometry, the complementing robustness of the presented optimization strategy may provide an advantage for other NV-based applications

Introduction to quantum optimal control for quantum sensing with nitrogen-vacancy centers in diamond

Diamond based quantum technology is a fast emerging field with both scientific and technological importance. With the growing knowledge and experience concerning diamond based quantum systems comes an increased demand for performance. Quantum optimal control (QOC) provides a direct solution to a number of existing challenges as well as a basis for proposed future applications. Together with a swift review of QOC strategies, quantum sensing, and other relevant quantum technology applications of nitrogen-vacancy (NV) centers in diamond, the authors give the necessary background to summarize recent advancements in the field of QOC assisted quantum applications with NV centers in diamond

The potential for aflatoxin predictive risk modelling in sub-Saharan Africa: a review

This review presents the current state of aflatoxin risk prediction models and their potential for value actors throughout the food chain in sub-Saharan Africa, with a specific focus on improving smallholder farmer management practices. Several empirical and mechanistic models have been developed either in academic research or by private sector aggregators and processors in high-income countries including Australia, the USA, and Southern Europe, but these models have been only minimally applied in sub-Saharan Africa, where there is significant potential and increasing need due to climate variability. Predictions can be made based on historic occurrence data using either a mechanistic microbiological framework for aflatoxin accumulation or an empirical model based on statistical correlations with climate conditions and local agronomic factors. Model results can then be distributed to smallholders through private, public, or mobile extension services, used by policymakers for strategy or policy, or utilised by private sector institutions for management decisions. Specific agricultural advice can be given during the three most critical points in the phenological cycle: preseason insight including sowing timing and crop varieties, preharvest advice about management and harvest timing, and postharvest optimal practices including storage, drying, and market information. Model development for sub-Saharan Africa is limited by a dearth of georeferenced aflatoxin occurrence data and real-time high resolution climate data; the wide diversity of farm typologies each with significant information and technology gaps; a prevalence of informal market structures and lack of economic incentives systems; and general lack of awareness around aflatoxins and best management practices to mitigate risk. Given advancements towards solving these challenges, predictive aflatoxin models can be integrated into decision support platforms to focus on optimisation of value for smallholders by minimising yield and nutritional losses, which can propagate value throughout the production and postharvest phases