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This paper investigates the dynamics of cosmological models incorporating nonlinear electrodynamics (NLED), focusing on their stability and causality. We explore two specific NLED models: the Power-Law and Rational Lagrangians. We assess these models' viability in describing the universe's evolution using dynamical systems theory and Bayesian inference. We present the theoretical framework of NLED coupled with general relativity, followed by an analysis of the stability and causality through the squared sound speed of the NLED Lagrangians. We then conduct a detailed dynamical analysis to identify the universe's evolution with this matter content. Our results show that the Power-Law Lagrangian model transitions through various cosmological phases from a Maxwell radiation-dominated state to a matter-dominated state. For the Rational Lagrangian model, including the Maxwell term, stable and causal behavior is observed within specific parameter ranges, with critical points indicating the evolutionary pathways of the universe. To validate our theoretical findings, we perform Bayesian parameter estimation using a comprehensive set of observational data, including cosmic chronometers, Baryon Acoustic Oscillation (BAO) measurements, and Type Ia Supernovae (SNeIa). The estimated parameters for both models align with expected values for the current universe, particularly the matter density $\Omega_m$ and the Hubble parameter $h$. However, the parameters $\alpha$ and $b$ are not tightly constrained within the prior ranges. Our model comparison strongly favors the $\Lambda$CDM model over the NLED models for late-universe observations, as the NLED model does not exhibit a cosmological constant behavior. Our results highlight the need for further refinement and exploration of NLED-based cosmological models to fully integrate them into the standard cosmological framework., Comment: 45 pages, 17 figures and one table

Many novel methods have been proposed to mitigate stellar activity for exoplanet detection as the presence of stellar activity in radial velocity (RV) measurements is the current major limitation. Unlike traditional methods that model stellar activity in the RV domain, more methods are moving in the direction of disentangling stellar activity at the spectral level. The goal of this paper is to present a novel convolutional neural network-based algorithm that efficiently models stellar activity signals at the spectral level, enhancing the detection of Earth-like planets. We trained a convolutional neural network to build the correlation between the change in the spectral line profile and the corresponding RV, full width at half maximum (FWHM) and bisector span (BIS) values derived from the classical cross-correlation function. This algorithm has been tested on three intensively observed stars: Alpha Centauri B (HD128621), Tau ceti (HD10700), and the Sun. By injecting simulated planetary signals at the spectral level, we demonstrate that our machine learning algorithm can achieve, for HD128621 and HD10700, a detection threshold of 0.5 m/s in semi-amplitude for planets with periods ranging from 10 to 300 days. This threshold would correspond to the detection of a $\sim$4$\mathrm{M}_{\oplus}$ in the habitable zone of those stars. On the HARPS-N solar dataset, our algorithm is even more efficient at mitigating stellar activity signals and can reach a threshold of 0.2 m/s, which would correspond to a 2.2$\mathrm{M}_{\oplus}$ planet on the orbit of the Earth. To the best of our knowledge, it is the first time that such low detection thresholds are reported for the Sun, but also for other stars, and therefore this highlights the efficiency of our convolutional neural network-based algorithm at mitigating stellar activity in RV measurements., Comment: Accepted for publication in A&A

In this paper, we present a novel approach to accelerate the Bayesian inference process, focusing specifically on the nested sampling algorithms. Bayesian inference plays a crucial role in cosmological parameter estimation, providing a robust framework for extracting theoretical insights from observational data. However, its computational demands can be substantial, primarily due to the need for numerous likelihood function evaluations. Our proposed method utilizes the power of deep learning, employing feedforward neural networks to approximate the likelihood function dynamically during the Bayesian inference process. Unlike traditional approaches, our method trains neural networks on-the-fly using the current set of live points as training data, without the need for pre-training. This flexibility enables adaptation to various theoretical models and datasets. We perform simple hyperparameter optimization using genetic algorithms to suggest initial neural network architectures for learning each likelihood function. Once sufficient accuracy is achieved, the neural network replaces the original likelihood function. The implementation integrates with nested sampling algorithms and has been thoroughly evaluated using both simple cosmological dark energy models and diverse observational datasets. Additionally, we explore the potential of genetic algorithms for generating initial live points within nested sampling inference, opening up new avenues for enhancing the efficiency and effectiveness of Bayesian inference methods.

Galactic rotation curves have been served as indispensable tools for determining the distribution of mass within galaxies. Despite several advances in precision observations, some discrepancies still persist between the inferred matter distribution from luminosity and observed rotation velocities, often attributed to the presence of dark matter. Traditional parametric models, while insightful, struggle with the complexity of galaxies with prominent bulges or non-circular motions, but in contrast, non-parametric methods offer a promising alternative, adapting to the intricate nature of rotation curves without any prior assumptions. In this work, we employ artificial neural networks to reconstruct rotation curves of 17 spiral galaxies from high-quality data, demonstrating the efficacy of the non-parametric approaches in characterizing galactic dynamics. Our findings underscore the importance of employing diverse methodological approaches to comprehensively understand galactic dynamics in modern astrophysics research. Moreover, the non-parametric reconstruction approach with neural networks presents a promising avenue for further investigations, capable of generating interpolations based on the intrinsic patterns of the data., Comment: 9 pages, 2 figures

In this paper, we present an Artificial Neural Network (ANN) based reconstruction analysis of the Supernova Ia (SNIa) distance moduli ($\mu(z)$), and hence dark energy, using LSST simulated three-year SNIa data. Our ANN reconstruction architecture can model both the distance moduli and their corresponding error estimates. For this we employ astroANN and incorporate Monte Carlo dropout techniques to quantify uncertainties in our predictions. We tune our hyperparameters through advanced genetic algorithms, including elitism, utilizing the DEAP library. We compared the performance of the ANN based reconstruction with two theoretical descriptions of dark energy models, $\Lambda$CDM and Chevallier-Linder-Polarski (CPL). We perform a Bayesian analysis for these two theoretical models using the LSST simulations and also compare with observations from Pantheon and Pantheon+ SNIa real data. We show that our model-independent reconstruction using ANN is consistent with both of them. We assessed the performance using mean squared error (MSE) and showed that the ANN can produce distance estimates in better agreement with the LSST dataset than either $\Lambda$CDM or CPL, albeit very small. We included an additional residual analysis and a null test with $F$-scores to show that the reconstructed distances from the ANN model, are in excellent agreement with the $\Lambda$CDM or CPL model., Comment: 14 Pages, 5 figures. Submitted to journal

Genetic algorithms are a powerful tool in optimization for single and multi-modal functions. This paper provides an overview of their fundamentals with some analytical examples. In addition, we explore how they can be used as a parameter estimation tool in cosmological models to maximize the likelihood function, complementing the analysis with the traditional Markov Chain Monte Carlo methods. We analyze that genetic algorithms provide fast estimates by focusing on maximizing the likelihood function, although they cannot provide confidence regions with the same statistical meaning as Bayesian approaches. Moreover, we show that implementing sharing and niching techniques ensures an effective exploration of the parameter space, even in the presence of local optima, always helping to find the global optima. This approach is invaluable in the cosmological context, where exhaustive space exploration of parameters is essential. We use dark energy models to exemplify the use of genetic algorithms in cosmological parameter estimation, including a multimodal problem, and we also show how to use the output of a genetic algorithm to obtain derived cosmological functions. This paper concludes that genetic algorithms are a handy tool within cosmological data analysis, without replacing the traditional Bayesian methods but providing different advantages., Comment: 19 pages, 6 figures; matches version published in Universe

In this paper, we introduce a novel predict-and-optimize method for profit-driven churn prevention. We frame the task of targeting customers for a retention campaign as a regret minimization problem. The main objective is to leverage individual customer lifetime values (CLVs) to ensure that only the most valuable customers are targeted. In contrast, many profit-driven strategies focus on churn probabilities while considering average CLVs. This often results in significant information loss due to data aggregation. Our proposed model aligns with the guidelines of Predict-and-Optimize (PnO) frameworks and can be efficiently solved using stochastic gradient descent methods. Results from 12 churn prediction datasets underscore the effectiveness of our approach, which achieves the best average performance compared to other well-established strategies in terms of average profit., Comment: 15 pages, 4 figures, submitted to INFORMATION SCIENCES

The main aim of this paper is to present the multi scalar field components as candidates to be the dark energy of the universe and their observational constraints. We start with the canonical Quintessence and Phantom fields with quadratic potentials and show that a more complex model should bear in mind to satisfy current cosmological observations. Then we present some implications for a combination of two fields, named as Quintom models. We consider two types of models, one as the sum of the quintessence and phantom potentials and other including an interacting term between fields. We find that adding one extra degree of freedom, by the interacting term, the dynamics enriches considerably and could lead to an improvement in the fit of $-2\ln\Delta \Like_{\rm max}= 5.19$, compared to $\Lambda$CDM. The resultant effective equation of state is now able to cross the phantom divide line, and in several cases present an oscillatory or discontinuous behavior, depending on the interaction value. The parameter constraints of the scalar field models (quintessence, phantom, quintom and interacting quintom) were performed using Cosmic Chronometers, Supernovae Ia and Baryon Acoustic Oscillations data; and the Log-Bayes factors were computed to compare the performance of the models. We show that single scalar fields may face serious troubles and hence the necessity of a more complex models, i.e. multiple fields., Comment: 21 pages, 8 figures; matches the published version on PRD

The properties of the matter density field in the initial conditions have a decisive impact on the features of the large-scale structure of the Universe as observed today. These need to be studied via $N$-body simulations, which are imperative to analyze high density collapsed regions into dark matter halos. In this paper, we train Machine Learning algorithms with information from N -body simulations to infer two properties: dark matter particle halo classification that leads to halo formation prediction with the characteristics of the matter density field traced back to the initial conditions, and dark matter halo formation by calculating the Halo Mass Function (HMF), which offers the number density of dark matter halos with a given threshold. We map the initial conditions of the matter density field into classification labels of dark matter halo structures. The Halo Mass Function of the simulations is calculated and reconstructed with theoretical methods as well as our trained algorithms. We test several Machine Learning techniques where we could find that the Random Forest and Neural Networks proved to be the better performing tools to classify dark matter particles in cosmological simulations. We also show that that it is not compulsory to use a high amount of data to train the algorithms in order to reconstruct the HMF, giving us a very good fitting function for both simulation and theoretical results., Comment: 10 pages, 6 figures

The applications of artificial neural networks in the cosmological field have shone successfully during the past decade, this is due to their great ability of modeling large amounts of datasets and complex nonlinear functions. However, in some cases, their use still remains controversial because their ease of producing inaccurate results when the hyperparameters are not carefully selected. In this paper, to find the optimal combination of hyperparameters to artificial neural networks, we propose to take advantage of the genetic algorithms. As a proof of the concept, we analyze three different cosmological cases to test the performance of the architectures achieved with the genetic algorithms and compare them with the standard process, consisting of a grid with all possible configurations. First, we carry out a model-independent reconstruction of the distance modulus using a type Ia supernovae compilation. Second, the neural networks learn to infer the equation of state for the quintessence model, and finally with the data from a combined redshift catalog the neural networks predict the photometric redshift given six photometric bands (urgizy). We found that the genetic algorithms improve considerably the generation of the neural network architectures, which can ensure more confidence in their physical results because of the better performance in the metrics with respect to the grid method., Comment: 15 pages, 4 figures; matches the version published in Physical Review D

In cosmology, the analysis of observational evidence is very important to test theoretical models of the Universe. Artificial neural networks are powerful and versatile computational tools for data modelling and are recently being considered in the analysis of cosmological data. The main goal of this paper is to provide an introduction to artificial neural networks and to describe some applications to cosmology. We present an overview on the fundamentals of neural networks and their technical details. Throughout three examples, we show their capabilities in modelling cosmological data, saving computational time in numerical tasks, and classifying stellar objects. Artificial neural networks offer interesting qualities that make them a viable alternative method for data analysis in cosmological research., Comment: 17 pages, 13 figures; matches the version published in Universe

In this paper we propose a novel coupled poroelasticity-diffusion model for the formation of extracellular oedema and infectious myocarditis valid in large deformations, manifested as an interaction between interstitial flow and the immune-driven dynamics between leukocytes and pathogens. The governing partial differential equations are formulated in terms of skeleton displacement, fluid pressure, Lagrangian porosity, and the concentrations of pathogens and leukocytes. A five-field finite element scheme is proposed for the numerical approximation of the problem, and we provide the stability analysis for a simplified system emanating from linearisation. We also discuss the construction of an adequate, Schur complement based, nested preconditioner. The produced computational tests exemplify the properties of the new model and of the finite element schemes., Comment: 29 pages

We introduce a stress/total-pressure formulation for poroelasticity that includes the coupling with steady nonlinear diffusion modified by stress. The nonlinear problem is written in mixed-primal form, coupling a perturbed twofold saddle-point system with an elliptic problem. The continuous formulation is analysed in the framework of abstract fixed-point theory and Fredholm alternative for compact operators. A mixed finite element method is proposed and its stability and convergence analysis are carried out. We also include a few illustrative numerical tests. The resulting model can be used to study waste removal in the brain parenchyma, where diffusion of a tracer alone or a combination of advection and diffusion are not sufficient to explain the alterations in rates of filtration observed in porous media samples.

This paper introduces a new approach to reconstruct cosmological functions using artificial neural networks based on observational measurements with minimal theoretical and statistical assumptions. By using neural networks, we can generate computational models of observational datasets, and then we compare them with the original ones to verify the consistency of our method. This methodology is applicable to even small-size datasets. In particular, we test the proposed method with data coming from cosmic chronometers, $f\sigma_8$ measurements, and the distance modulus of the Type Ia supernovae. Furthermore, we introduce a first approach to generate synthetic covariance matrices through a variational autoencoder, using the systematic covariance matrix of the Type Ia supernova compilation., Comment: 20 pages, 8 figures; matches the version published in EPJC

We propose and analyse an augmented mixed finite element method for the Oseen equations written in terms of velocity, vorticity, and pressure with non-constant viscosity and homogeneous Dirichlet boundary condition for the velocity. The weak formulation includes least-squares terms arising from the constitutive equation and from the incompressibility condition, and we show that it satisfies the hypotheses of the Babu\vska-Brezzi theory. Repeating the arguments of the continuous analysis, the stability and solvability of the discrete problem are established. The method is suited for any Stokes inf-sup stable finite element pair for velocity and pressure, while for vorticity any generic discrete space (of arbitrary order) can be used. A priori and a posteriori error estimates are derived using two specific families of discrete subspaces. Finally, we provide a set of numerical tests illustrating the behaviour of the scheme, verifying the theoretical convergence rates, and showing the performance of the adaptive algorithm guided by residual a posteriori error estimation., Comment: 18 pages

The flux of electron and positron cosmic rays measured by the space-based experiments PAMELA, AMS-02, CALET, DAMPE and Fermi-LAT shows an unexpected behaviour at high energies, in comparison with expectations from standard astrophysical sources. In particular, AMS-02 observations provide compelling evidence for a new source of positrons and electrons whose origin is still unknown. Plausible scenarios include either the contribution of dark matter or unresolved astrophysical sources, such as nearby pulsars. It has been shown that explanations based mostly on dark matter, tend to overproduce gamma-rays, entering in conflict with measurements of the extra-galactic gamma-ray background (EGB). Although this situation seems to be quite generic, it ultimately depends on the properties of the dark matter candidate. In this work we revisit a model in which the gravitino is a dark matter candidate decaying to SM particles through R-parity violating couplings. We show that decay channels allowed by trilinear couplings produce more electrons and positrons and fewer photons in comparison to channels allowed by bilinear couplings. Indeed, when considering AMS-02 data alone we find that the trilinear model is compatible with EGB constraints. However, this result may change when DAMPE or CALET measurements of the sum of electrons and positrons fluxes are considered instead of AMS-02 data. Therefore, in order to evaluate this model further, discrepancies between the data collected by these experiments need to be clarified

The main aim of this article is that students, at the basic level of education, gain a quantitative understanding of the size of molecules by performing a simple experiment easily designed within the classroom., Comment: 2 pages, 2 figures

We perform the linear stability analysis for a new model for poromechanical processes with inertia (formulated in mixed form using the solid deformation, fluid pressure, and total pressure) interacting with diffusing and reacting solutes convected in the medium. We find parameter regions that lead to spatio-temporal instabilities of the coupled system. The mutual dependences between deformation and diffusive patterns are of substantial relevance in the study of morphoelastic changes in biomaterials. We provide a set of computational examples in 2D and 3D (related to brain mechanobiology) that can be used to form a better understanding on how, and up to which extent, the deformations of the porous structure dictate the generation and suppression of spatial patterning dynamics, also related to the onset of mechano-chemical waves.

In $R$-parity violating supersymmetry, the gravitino as the lightest supersymmetric particle (LSP) is a good candidate for dark matter, with the interesting characteristic to be detectable through $\gamma$-ray telescopes. We extend this analysis considering an axino next-to-LSP (NLSP) as a coexisting dark matter particle contributing with a detectable signal in the $\gamma$-ray spectrum. The analysis is carried out in the framework of the $\mu\nu$SSM, which solves the $\mu$ problem reproducing simultaneously neutrino data only with the addition of right-handed neutrinos. We find that important regions of the parameter space can be tested by future MeV-GeV $\gamma$-ray telescopes through the line signal coming from the decay of the axino NLSP into photon-neutrino. In a special region, a double-line signal from axino NLSP and gravitino LSP is possible with both contributions detectable., Comment: Version published in Astroparticle Physics. Discussions expanded, appendix and references added, 23 pages, 4 figures. The analysis of the opposite case (axino LSP with gravitino NLSP) was carried out in arXiv:1911.03191

Axino and gravitino are promising candidates to solve the dark matter (DM) problem in the framework of supersymmetry. In this work, we assume that the axino is the lightest supersymmetric particle (LSP), and therefore contributes to DM. In the case of R-parity violating models, the axino can decay into a neutrino-photon pair with a lifetime much longer than the age of the Universe, yielding a potentially detectable signal. Interestingly, a gravitino next-to-LSP (NLSP) can live enough as to contribute to the relic density. We study both scenarios, only axino LSP as DM, and axino LSP with gravitino NLSP as DM. We carry out the analysis in the context of the $\mu\nu$SSM, which solves the $\mu$ problem and reproduces neutrino data, only adding couplings involving right-handed neutrinos. In particular, we perform a complete analysis of the relevant parameter space of the model considering constraints from neutrino physics, cosmological observations, and $\gamma$-ray detection. We find that the axino or the gravitino can produce a signal detectable by future MeV-GeV $\gamma$-ray telescopes. In addition, in a parameter region where we get a well-tempered mixture of both particles, a double-line signal arises as a smoking gun., Comment: Version published in JCAP, discussions expanded, references added, 27 pages, 6 figures

We analyse a PDE system modelling poromechanical processes (formulated in mixed form using the solid deformation, fluid pressure, and total pressure) interacting with diffusing and reacting solutes in the medium. We investigate the well-posedness of the nonlinear set of equations using fixed-point theory, Fredholm's alternative, a priori estimates, and compactness arguments. We also propose a mixed finite element method and rigorously demonstrate the stability of the scheme. Error estimates are derived in suitable norms, and numerical experiments are conducted to illustrate the mechano-chemical coupling and to verify the theoretical rates of convergence.

In this brief note, we introduce a non-symmetric mixed finite element formulation for Brinkman equations written in terms of velocity, vorticity and pressure with non-constant viscosity. The analysis is performed by the classical Babu\v{s}ka-Brezzi theory, and we state that any inf-sup stable finite element pair for Stokes approximating velocity and pressure can be coupled with a generic discrete space of arbitrary order for the vorticity. We establish optimal a priori error estimates which are further confirmed through computational examples

Sterile neutrinos could provide a link between the Standard Model particles and a dark sector, besides generating active neutrino masses via the seesaw mechanism type I. We show that, if dark matter annihilation into sterile neutrinos determines its observed relic abundance, it is possible to explain the Galactic Center $\gamma$-ray excess reported by the Fermi-LAT Collaboration as due to an astrophysical component plus dark matter annihilations. We observe that sterile neutrino portal to dark matter provides an impressively good fit, with a p-value of 0.78 in the best fit point, to the Galactic Center $\gamma$-ray flux, for DM masses in the range (40-80) GeV and sterile neutrino masses 20 GeV $\lesssim M_N < M_{DM}$. Such values are compatible with the limits from Fermi-LAT observations of the dwarfs spheroidal galaxies in the Milky Way halo, which rule out dark matter masses below $\sim$ 50 GeV (90 GeV), for sterile neutrino masses $M_N \lesssim M_{DM}$ ($M_N \ll M_{DM}$). We also estimate the impact of AMS-02 anti-proton data on this scenario., Comment: 16 pages + references, 7 figures. Discussion about AMS-02 antiproton constraints added. Published version

We show that the Galactic Center Excess (GCE) emission, as recently updated by the Fermi-LAT Collaboration, could be explained by the sum of Fermi-bubbles-like emission plus dark matter (DM) annihilation, in the context of a scalar-singlet Higgs portal scenario (SHP). In fact, the standard SHP, where the DM particle, $S$, only has renormalizable interactions with the Higgs, is non-operational due to strong constraints, specially from DM direct detection limits. Thus we consider a most economical extension, called ESHP (for extended SHP), which simply consists in the addition of a second (heavier) scalar singlet in the dark sector. The second scalar can be integrated-out, leaving a standard SHP plus a dimension-6 operator. Essentially, this model has only two relevant parameters (the DM mass and the coupling of the dim-6 operator). DM annihilation occurs mainly into two Higgs bosons, $SS\rightarrow hh$. We demonstrate that, despite its economy, the ESHP model provides excellent fits to the GCE (with p-value $\sim 0.6-0.7$) for very reasonable values of the parameters, in particular $m_S \simeq 130$ GeV. This is achieved without conflicting with other observables and keeping the $S-$particle relic density at the accepted value for the DM content in the universe., Comment: 20 pages, 7 figures. Version published in JCAP

The Fermi Collaboration has recently updated their analysis of gamma rays from the center of the Galaxy. They reconfirm the presence of an unexplained emission feature which is most prominent in the region of $1-10$ GeV, known as the Galactic Center GeV excess (GCE). Although the GCE is now firmly detected, an interpretation of this emission as a signal of self-annihilating dark matter (DM) particles is not unambiguously possible due to systematic effects in the gamma-ray modeling estimated in the Galactic Plane. In this paper we build a covariance matrix, collecting different systematic uncertainties investigated in the Fermi Collaboration's paper that affect the GCE spectrum. We show that models where part of the GCE is due to annihilating DM can still be consistent with the new data. We also re-evaluate the parameter space regions of the minimal supersymmetric Standard Model (MSSM) that can contribute dominantly to the GCE via neutralino DM annihilation. All recent constraints from DM direct detection experiments such as PICO, LUX, PandaX and Xenon1T, limits on the annihilation cross section from dwarf spheroidal galaxies and Large Hadron Collider limits are considered in this analysis. Due to a slight shift in the energy spectrum of the GC excess with respect to the previous Fermi analysis, and the recent limits from direct detection experiments, we find a slightly shifted parameter region of the MSSM compared to our previous analysis that is consistent with the GCE. Neutralinos with a mass between $85-220$ GeV can describe the excess via annihilation into a pair of $W$-bosons or top quarks. Remarkably, there are low fine-tuning models among the regions that we have found. The complete set of solutions will be probed by upcoming direct detection experiments and with dedicated searches in the upcoming data of the Large Hadron Collider., Comment: Replaced with the published version in JCAP. Auxiliary material on the construction of the covariance matrix and the GCE spectrum is included

The Cherenkov Telescope Array, CTA, will be the major global observatory for very high energy gamma-ray astronomy over the next decade and beyond. The scientific potential of CTA is extremely broad: from understanding the role of relativistic cosmic particles to the search for dark matter. CTA is an explorer of the extreme universe, probing environments from the immediate neighbourhood of black holes to cosmic voids on the largest scales. Covering a huge range in photon energy from 20 GeV to 300 TeV, CTA will improve on all aspects of performance with respect to current instruments. The observatory will operate arrays on sites in both hemispheres to provide full sky coverage and will hence maximize the potential for the rarest phenomena such as very nearby supernovae, gamma-ray bursts or gravitational wave transients. With 99 telescopes on the southern site and 19 telescopes on the northern site, flexible operation will be possible, with sub-arrays available for specific tasks. CTA will have important synergies with many of the new generation of major astronomical and astroparticle observatories. Multi-wavelength and multi-messenger approaches combining CTA data with those from other instruments will lead to a deeper understanding of the broad-band non-thermal properties of target sources. The CTA Observatory will be operated as an open, proposal-driven observatory, with all data available on a public archive after a pre-defined proprietary period. Scientists from institutions worldwide have combined together to form the CTA Consortium. This Consortium has prepared a proposal for a Core Programme of highly motivated observations. The programme, encompassing approximately 40% of the available observing time over the first ten years of CTA operation, is made up of individual Key Science Projects (KSPs), which are presented in this document., Comment: 213 pages, including references and glossary. Version 2: credits and references updated, some figures updated, and author list updated

List of contributions from the Cherenkov Telescope Array Consortium presented at the 35th International Cosmic Ray Conference, July 12-20 2017, Busan, Korea., Comment: Index of Cherenkov Telescope Array conference proceedings at the ICRC2017, Busan, Korea

We present a new method to interpret the $\gamma$-ray data of our inner Galaxy as measured by the Fermi Large Area Telescope (Fermi LAT). We train and test convolutional neural networks with simulated Fermi-LAT images based on models tuned to real data. We use this method to investigate the origin of an excess emission of GeV $\gamma$-rays seen in previous studies. Interpretations of this excess include $\gamma$ rays created by the annihilation of dark matter particles and $\gamma$ rays originating from a collection of unresolved point sources, such as millisecond pulsars. Our new method allows precise measurements of the contribution and properties of an unresolved population of $\gamma$-ray point sources in the interstellar diffuse emission model., Comment: 24 pages, 11 figures

The $\mu\nu$SSM solves the $\mu$ problem of supersymmetric models and reproduces neutrino data, simply using couplings with right-handed neutrinos $\nu$'s. Given that these couplings break explicitly $R$ parity, the gravitino is a natural candidate for decaying dark matter in the $\mu \nu$SSM. In this work we carry out a complete analysis of the detection of $\mu \nu$SSM gravitino dark matter through $\gamma$-ray observations. In addition to the two-body decay producing a sharp line, we include in the analysis the three-body decays producing a smooth spectral signature. We perform first a deep exploration of the low-energy parameter space of the $\mu \nu$SSM taking into account that neutrino data must be reproduced. Then, we compare the $\gamma$-ray fluxes predicted by the model with Fermi-LAT observations. In particular, with the 95$\%$ CL upper limits on the total diffuse extragalactic $\gamma$-ray background using 50 months of data, together with the upper limits on line emission from an updated analysis using 69.9 months of data. For standard values of bino and wino masses, gravitinos with masses larger than about 4 GeV, or lifetimes smaller than $10^{28}$ s, produce too large fluxes and are excluded as dark matter candidates. However, when limiting scenarios with large and close values of the gaugino masses are considered, the constraints turn out to be less stringent, excluding masses larger than 17 GeV and lifetimes smaller than $4\times 10^{25}$ s., Comment: Minor changes, references added, version published in JCAP. 23 pages, 7 figures, 3 tables