HNL mass degeneracy: implications for low-scale seesaws, LNV at colliders and leptogenesis

Low-scale seesaw variants protected by lepton number symmetry provide a natural explanation of the smallness of neutrino masses but, unlike their higher-scale counterparts, with potentially testable phenomenology. The approximate lepton number symmetry arranges the heavy neutrinos in pseudo-Dirac pairs, which might be accessible at collider or even beam dump experiments if their mass is low enough and their mixing with the active neutrinos sufficiently large. Despite their pseudo-Dirac nature, their small mass splittings may lead to oscillations that prevent the cancellation of their potential lepton-number-violating signals. Interestingly, these small splittings may also resonantly enhance the production of a lepton number asymmetry for low-scale leptogenesis scenarios or, for extremely degenerate states, lead to an asymmetry large enough to resonantly produce a keV sterile neutrino dark matter candidate with the correct relic abundance via the Shi-Fuller mechanism. In this work we explore the parameter space of the different low-scale seesaw mechanisms and study the size of these splittings, given their important and interesting phenomenological consequences. While all low-scale seesaw variants share the same dimension 5 and 6 operators when integrating out the heavy states, we point out that the mass splitting of the pseudo-Dirac pairs are very different in different realizations such as the inverse or linear seesaw. This different phenomenology could offer a way to discriminate between low-scale seesaw realizations.Comment: 27 pages, 6 figures. Matches published version in JHE

Bounds on lepton non-unitarity and heavy neutrino mixing

We present an updated and improved global fit analysis of current flavor and electroweak precision observables to derive bounds on unitarity deviations of the leptonic mixing matrix and on the mixing of heavy neutrinos with the active flavours. This new analysis is motivated by new and updated experimental results on key observables such as $V_{ud}$, the invisible decay width of the $Z$ boson and the $W$ boson mass. It also improves upon previous studies by considering the full correlations among the different observables and explicitly calibrating the test statistic, which may present significant deviations from a $\chi^2$ distribution. The results are provided for three different Type-I seesaw scenarios: the minimal scenario with only two additional right-handed neutrinos, the next to minimal one with three extra neutrinos, and the most general one with an arbitrary number of heavy neutrinos that we parametrize via a generic deviation from a unitary leptonic mixing matrix. Additionally, we also analyze the case of generic deviations from unitarity of the leptonic mixing matrix, not necessarily induced by the presence of additional neutrinos. This last case relaxes some correlations among the parameters and is able to provide a better fit to the data. Nevertheless, inducing only leptonic unitarity deviations avoiding both the correlations implied by the right-handed neutrino extension as well as more strongly constrained operators is challenging and would imply significantly more complex UV completions.Comment: 27 pages + appendices, 7 figures, 7 table

Genetic Programming Based on Novelty Search

Novelty Search (NS) is a unique approach towards search and optimization,where an explicit objective function is replaced by a measureof solution novelty. However, NS has been mostly used in evolutionaryrobotics, its usefulness in classic machine learning problems has beenunexplored. This thesis presents a NS-based Genetic Programming(GP) algorithms for common machine learning problems, with the followingcontributions. It is shown that NS can solve real-world classification,clustering and symbolic regression tasks, validated on realworldbenchmarks and synthetic problems. These results are madepossible by using a domain-specific behavior descriptor, related to theconcept of semantics in GP. Moreover, two new versions of the NS algorithmare proposed, Probabilistic NS (PNS) and a variant of MinimalCriteria NS (MCNS). The former models the behavior of each solutionas a random vector and eliminates all the NS parameters while reducingthe computational overhead of the NS algorithm; the latter uses astandard objective function to constrain and bias the search towardshigh performance solutions. The thesis also discusses the effects of NSon GP search dynamics and code growth. Results show that NS can beused as a realistic alternative for machine learning, and particularly forGP-based classification

Comprehensive Analysis of Learning Cases in an Autonomous Navigation Task for the Evolution of General Controllers

Robotics technology has made significant advancements in various fields in industry and society. It is clear how robotics has transformed manufacturing processes and increased productivity. Additionally, navigation robotics has also been impacted by these advancements, with investors now investing in autonomous transportation for both public and private use. This research aims to explore how training scenarios affect the learning process for autonomous navigation tasks. The primary objective is to address whether the initial conditions (learning cases) have a positive or negative impact on the ability to develop general controllers. By examining this research question, the study seeks to provide insights into how to optimize the training process for autonomous navigation tasks, ultimately improving the quality of the controllers that are developed. Through this investigation, the study aims to contribute to the broader goal of advancing the field of autonomous navigation and developing more sophisticated and effective autonomous systems. Specifically, we conducted a comprehensive analysis of a particular navigation environment using evolutionary computing to develop controllers for a robot starting from different locations and aiming to reach a specific target. The final controller was then tested on a large number of unseen test cases. Experimental results provide strong evidence that the initial selection of the learning cases plays a role in evolving general controllers. This work includes a preliminary analysis of a specific set of small learning cases chosen manually, provides an in-depth analysis of learning cases in a particular navigation task, and develops a tool that shows the impact of the selected learning cases on the overall behavior of a robot’s controller

Genetic Programming Based on Novelty Search

Novelty Search (NS) is a unique approach towards search and optimization,where an explicit objective function is replaced by a measureof solution novelty. However, NS has been mostly used in evolutionaryrobotics, its usefulness in classic machine learning problems has beenunexplored. This thesis presents a NS-based Genetic Programming(GP) algorithms for common machine learning problems, with the followingcontributions. It is shown that NS can solve real-world classification,clustering and symbolic regression tasks, validated on realworldbenchmarks and synthetic problems. These results are madepossible by using a domain-specific behavior descriptor, related to theconcept of semantics in GP. Moreover, two new versions of the NS algorithmare proposed, Probabilistic NS (PNS) and a variant of MinimalCriteria NS (MCNS). The former models the behavior of each solutionas a random vector and eliminates all the NS parameters while reducingthe computational overhead of the NS algorithm; the latter uses astandard objective function to constrain and bias the search towardshigh performance solutions. The thesis also discusses the effects of NSon GP search dynamics and code growth. Results show that NS can beused as a realistic alternative for machine learning, and particularly forGP-based classification

Genetic Programming Based on Novelty Search

Novelty Search (NS) is a unique approach towards search and optimization,where an explicit objective function is replaced by a measureof solution novelty. However, NS has been mostly used in evolutionaryrobotics, its usefulness in classic machine learning problems has beenunexplored. This thesis presents a NS-based Genetic Programming(GP) algorithms for common machine learning problems, with the followingcontributions. It is shown that NS can solve real-world classification,clustering and symbolic regression tasks, validated on realworldbenchmarks and synthetic problems. These results are madepossible by using a domain-specific behavior descriptor, related to theconcept of semantics in GP. Moreover, two new versions of the NS algorithmare proposed, Probabilistic NS (PNS) and a variant of MinimalCriteria NS (MCNS). The former models the behavior of each solutionas a random vector and eliminates all the NS parameters while reducingthe computational overhead of the NS algorithm; the latter uses astandard objective function to constrain and bias the search towardshigh performance solutions. The thesis also discusses the effects of NSon GP search dynamics and code growth. Results show that NS can beused as a realistic alternative for machine learning, and particularly forGP-based classification

Searching for Novel Clustering Programs

Novelty search (NS) is an open-ended evolutionary algorithm that eliminates the need for an explicit objective function. Instead, NS focuses selective pressure on the search for novel solutions. NS has produced intriguing results in specialized domains, but has not been applied in most machine learning areas. The key component of NS is that each individual is described by the behavior it exhibits, and this description is used to determine how novel each individual is with respect to what the search has producedthus far. However, describingindividuals in behavioral space is not trivial, and care must be taken to properly define a descriptor for a particular domain. This paper applies NS to a mainstream patternanalysis area: dataclustering. To doso, adescriptor of clustering performance is proposed and tested on several problems, and compared with two control methods, Fuzzy C-means and K-means. Results show that NS can effectively be applied to data clustering in some circumstances. NS performance is quite poor on simple or easy problems, achieving basically random performance. Conversely, as the problems get harder NS performs better, and outperforming the control methods. It seems that the search space exploration inducedby NS is fully exploited only when generating good solutions is more challenging

Evolving Genetic Programming Classifiers with Novelty Search

Novelty Search (NS) is a unique approach towards search and optimization,where an explicit objective function is replaced by a measure of solution noveltyto provide the selective pressure in an artificial evolutionary system. However,NS has been mostly used in evolutionary robotics, while it’s applicability to classicmachine learning problems has been mostly unexplored. This work presents a NSbasedGenetic Programming (GP) algorithm for supervised classification, with thefollowing noteworthy contributions. It is shown that NS can solve real-world classificationtasks, validated over several commonly used benchmarks. These results aremade possible by using a domain-specific behavioral descriptor, closely related to theconcept of semantics in GP.Moreover, two new variants of the NS algorithm are proposed,Probabilistic NS (PNS) and a variant ofMinimum Criterion NS (MCNS). Theformer models the behavior of each solution as a random vector, eliminating all theNS parameters and reducing the computational overhead of the traditional NS algorithm;the latter uses a standard objective function to constrain the search and bias theprocess towards high performance solutions. The paper also discusses the effects ofNS on an important GP phenomenon, bloat. In particular, results indicate that somevariants of the NS approach can have a beneficial effect on the search process bycurtailing code growth

A Behavior-based Analysis of Modal Problems

Genetic programming (GP) has proven to be a powerful tool for (semi)automated problem solving in various domains. However, while the algorithmic aspects of GP have been a primary object of study, there is a need to enhance the understanding of the problems where GP is applied. One particular goal is to categorize problems in a meaningful way, in order to select the best tools that can possibly be used to solve them. This paper studies modal problems, a conceptual class of problems recently proposed by Spector at GECCO 2012. Modal problems are those for which a solution program requires different modes of operation for different contexts. The thesis of this paper is that modality, in this sense, is better understood by analyzing program performance in behavioral space. The behavior-based perspective is seen as part of a scale of different forms of analyzing performance; with a coarse view given by a global fitness value and a highly detailed view provided by the semantics approach. On the other hand, behavioral analysis is seen as a flexible approach where the context of a program’s performance is considered at in a domain-specific manner. The experimental evidence presented here suggests that behavior-based search could allow a GP to find programs with disjoint behavioral structures, that can satisfy the requirements of each mode of operation of a modal problem