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Abstract A search for a chargino-neutralino pair decaying via the 125 GeV Higgs boson into photons is presented. The study is based on the data collected between 2015 and 2018 with the ATLAS detector at the LHC, corresponding to an integrated luminosity of 139 fb−1 of pp collisions at a centre-of-mass energy of 13 TeV. No significant excess over the expected background is observed. Upper limits at 95% confidence level for a massless $$ {\tilde{\chi}}_1^0 $$ χ ˜ 1 0 are set on several electroweakino production cross-sections and the visible cross-section for beyond the Standard Model processes. In the context of simplified supersymmetric models, 95% confidence-level limits of up to 310 GeV in $$ m\left({\tilde{\chi}}_1^{\pm }/{\tilde{\chi}}_2^0\right) $$ m χ ˜ 1 ± / χ ˜ 2 0 , where $$ m\left({\tilde{\chi}}_1^0\right) $$ m χ ˜ 1 0 = 0.5 GeV, are set. Limits at 95% confidence level are also set on the $$ {\tilde{\chi}}_1^{\pm }{\tilde{\chi}}_2^0 $$ χ ˜ 1 ± χ ˜ 2 0 cross-section in the mass plane of $$ m\left({\tilde{\chi}}_1^{\pm }/{\tilde{\chi}}_2^0\right) $$ m χ ˜ 1 ± / χ ˜ 2 0 and $$ m\left({\tilde{\chi}}_1^0\right) $$ m χ ˜ 1 0 , and on scenarios with gravitino as the lightest supersymmetric particle. Upper limits at the 95% confidence-level are set on the higgsino production cross-section. Higgsino masses below 380 GeV are excluded for the case of the higgsino fully decaying into a Higgs boson and a gravitino.

Food insecurity is a growing concern due to man-made conflicts, climate change, and economic downturns. Forecasting the state of food insecurity is essential to be able to trigger early actions, for example, by humanitarian actors. To measure the actual state of food insecurity, expert and consensus-based approaches and surveys are currently used. Both require substantial manpower, time, and budget. This paper introduces an extreme gradient-boosting machine learning model to forecast monthly transitions in the state of food security in Ethiopia, at a spatial granularity of livelihood zones, and for lead times of one to 12 months, using open-source data. The transition in the state of food security, hereafter referred to as predictand, is represented by the Integrated Food Security Phase Classification Data. From 19 categories of datasets, 130 variables were derived and used as predictors of the transition in the state of food security. The predictors represent changes in climate and land, market, conflict, infrastructure, demographics and livelihood zone characteristics. The most relevant predictors are found to be food security history and surface soil moisture. Overall, the model performs best for forecasting Deteriorations and Improvements in the state of food security compared to the baselines. The proposed method performs (F1 macro score) at least twice as well as the best baseline (a dummy classifier) for a Deterioration. The model performs better when forecasting long-term (7 months; F1 macro average = 0.61) compared to short-term (3 months; F1 macro average = 0.51). Combining machine learning, Integrated Phase Classification (IPC) ratings from monitoring systems, and open data can add value to existing consensus-based forecasting approaches as this combination provides longer lead times and more regular updates. Our approach can also be transferred to other countries as most of the data on the predictors are openly available from global data repositories.