In the framework of the scotogenic model, which features radiative generation of neutrino masses, we explore light dark matter scenario. Throughout the paper we chiefly focus on keV-scale dark matter which can be produced either via freeze-in through the decays of the new scalars, or from the decays of next-to-lightest fermionic particle in the spectrum, which is produced through freeze-out. The latter mechanism is required to be suppressed as it typically produces a hot dark matter component. Constraints from BBN are also considered and in combination with the former production mechanism they impose the dark matter to be light. For this scenario we consider signatures at High Luminosity LHC and proposed future hadron and lepton colliders, namely FCC-hh and CLIC, focusing on searches with two leptons and missing energy as a final state. While a potential discovery at High Luminosity LHC is in tension with limits from cosmology, the situation greatly improves for future colliders.
We present a pairwise learning to rank approach based on a neural net, called DirectRanker, that generalizes the RankNet architecture. We show mathematically that our model is reflexive, antisymmetric, and transitive allowing for simplified training and improved performance. Experimental results on the LETOR MSLR-WEB10K, MQ2007 and MQ2008 datasets show that our model outperforms numerous state-of-the-art methods, while being inherently simpler in structure and using a pairwise approach only.
Ranking algorithms based on Neural Networks have been a topic of recent research. Ranking is employed in everyday applications like product recommendations, search results, or even in finding good candidates for hiring. However, Neural Networks are mostly opaque tools, and it is hard to evaluate why a specific candidate, for instance, was not considered. Therefore, for neural-based ranking methods to be trustworthy it is crucial to guarantee that the outcome is fair and that the decisions are not discriminating people according to sensitive attributes such as gender, sexual orientation, or ethnicity.In this work we present a family of fair pairwise learning to rank approaches based on Neural Networks, which are able to produce balanced outcomes for underprivileged groups and, at the same time, build fair representations of data, i.e. new vectors having no correlation with regard to a sensitive attribute. We compare our approaches to recent work dealing with fair ranking and evaluate them using both relevance and fairness metrics. Our results show that the introduced fair pairwise ranking methods compare favorably to other methods when considering the fairness/relevance trade-off.
We present work designed to compute baryon masses on = 2 + 1 CLS ensembles including isospin-breaking effects due to non-degenerate light quark masses and electromagnetic interactions. These effects are determined at leading order via a perturbative expansion around the iso-symmetric theory. We furthermore apply a group-theoretical operator construction for the various interpolators describing the different members of the baryon octet and decuplet based on a classification by spin, parity, and flavor content.
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