Joint Statistical Analysis Research Articles

Development characteristics of joints in the Chang 6-Chang 9 reservoirs of the Yanchang Formation in southern Ordos Basin, China

As the most important source rock of the Ordos Basin, China, the Chang 6-Chang 9 reservoirs of the Yanchang Formation have developed abundant of joints. Understanding the formation, geometry and distribution is very important for flow modelling and shale gas exploration in the reservoir. To this end, we investigate the development characteristics of joints in the Chang 6-Chang 9 reservoirs based on field joint mapping and statistical analysis. A total of 1574 joints were mapped across 43 outcrops, which can be divided into three groups: NW-SE, N-S, and NEE-SWW. The distributions of joint spacing ( s ) and trace lengths ( l ) and their dependence on bed thickness ( t ) are statistically analyzed. We find that the distributions of joint trace lengths follow exponential distributions, while joint spacing distributions are well expressed by lognormal distributions. Besides, both the s - t and l - t relationships fit linear distributions. Here, a total of 12 sandstone layers are selected as representative layers to study the condition of joint saturation. Statistical analysis shows that the joint spacing distribution of the sandstone gradually transforms from a skewed lognormal distribution to a normal distribution as s/t decreases from 1.51 to 0.54, indicating that the development of joints is gradually approaching saturation.

Joint inference for telemetry and spatial survey data.

Data integration, the joint statistical analysis of data from different observation platforms, is pivotal for data-hungry disciplines such as spatial ecology. Pooled data types obtained from the same underlying process, analyzed jointly, can improve both precision and accuracy in models of species distributions and species-habitat associations. However, the integration of telemetry and spatial survey data has proved elusive because of the fundamentally different analytical approaches required by these two data types. Here, "spatial survey" denotes a survey that records spatial locations and has no temporal structure, for example, line or point transects but not capture-recapture or telemetry. Step selection functions (SSFs-the canonical framework for telemetry) and habitat selection functions (HSFs-the default approach to spatial surveys) differ in not only their specifications but also their results. By imposing the constraint that microscopic mechanisms (animal movement) must correctly scale up to macroscopic emergence (population distributions), a relationship can be written between SSFs and HSFs, leading to a joint likelihood using both datasets. We implement this approach using maximum likelihood, explore its estimation precision by systematic simulation, and seek to derive broad guidelines for effort allocation in the field. We find that complementarities in spatial coverage and resolution between telemetry and survey data often lead to marked inferential improvements in joint analyses over those using either data type alone. The optimal allocation of effort between the two methods (or the choice between them, if only one can be selected) depends on the overall effort expended and the pattern of environmental heterogeneity. Examining inferential performance over a broad range of scenarios for the relative cost between the two methods, we find that integrated analysis usually offers higher precision. Our methodological work shows how to integrate the analysis of telemetry and spatial survey data under a novel joint likelihood function, using traditional computational methods. Our simulation experiments suggest that even when the relative costs of the two methods favor the deployment of one field approach over another, their joint use is broadly preferable. Therefore, joint analysis of the two key methods used in spatial ecology is not only possible but also computationally efficient and statistically more powerful.

Disentangling the Faraday rotation sky

Context. Magnetic fields permeate the diffuse interstellar medium (ISM) of the Milky Way, and are essential to explain the dynamical evolution and current shape of the Galaxy. Magnetic fields reveal themselves via their influence on the surrounding matter, and as such are notoriously hard to measure independently of other tracers. Aims. In this work, we attempt to disentangle an all-sky map of the line-of-sight (LoS)-parallel component of the Galactic magnetic field from the Faraday effect, utilizing several tracers of the Galactic electron density, ne. Additionally, we aim to produce a Galactic electron dispersion measure map and quantify several tracers of the structure of the ionized medium of the Milky Way. Methods. The method developed to reach these aims is based on information field theory, a Bayesian inference framework for fields, which performs well when handling noisy and incomplete data and constraining high-dimensional-parameter spaces. We rely on compiled catalogs of extragalactic Faraday rotation measures and Galactic pulsar dispersion measures, a well as data on bremsstrahlung and the hydrogen α spectral line to trace the ionized medium of the Milky Way. Results. We present the first full sky map of the LoS-averaged Galactic magnetic field. Within this map, we find LoS-parallel and LoS-averaged magnetic field strengths of up to 4 µG, with an all-sky root mean square of 1.1 µG, which is consistent with previous local measurements and global magnetic field models. Additionally, we produce a detailed electron dispersion measure map that agrees with existing parametric models at high latitudes but suffers from systematic effects in the disk. Further analysis of our results with regard to the 3D structure of ne reveals that it follows a Kolmogorov-type turbulence for most of the sky. From the reconstructed dispersion measure and emission measure maps, we construct several tracers of variability in ne along the LoS. Conclusions. This work demonstrates the power of consistent joint statistical analysis including multiple datasets and physical quantities and defines a road map toward a full three-dimensional joint reconstruction of the Galactic magnetic field and the ionized ISM.

Dynamic Characterization of Equatorial Plasma Bubble Based on Triangle Network‐Joint Slope Approach

AbstractThis paper introduces a Triangle Network‐Joint Slope (TN‐JS) approach to characterize the spatial and temporal dynamics of Equatorial Plasma Bubbles (EPBs) during geomagnetic storms. To collaboratively determine the EPB drift directions from multiple stations, a Delaunay triangle network is constructed, utilizing the distribution of Ionospheric Piercing Points (IPPs). The Time Difference of Arrival (TDOA) is extracted through cross‐correlating the Rate of Total Electron Content (ROT). The EPB drift direction can be approximately calculated by considering TDOA and IPP distances within each individual triangle of the network. This calculation is then refined through a joint statistical analysis. Using a reference station as the origin, the remaining stations within the network are projected along the estimated EPB drift direction. A spatial‐temporal color map illustrating regional ionospheric anomaly ROT observations is constructed. The EPB drift velocity among multiple stations can be collectively estimated by fitting the slope of this map, facilitating outlier exclusion. Accounting for satellite dynamic effects and the diverse orbit characteristics of GPS and BDS, corresponding IPP scan velocity compensation is performed and analyzed for EPB dynamic estimation. Using the geomagnetic storm event that occurred on September 8 as a case study, the spatial‐temporal kinetic properties of EPBs is characterized by analyzing Global Navigation Satellite System (GNSS) observations from 17 Hong Kong monitoring stations with the proposed TN‐JS approach. The results indicate during this magnetic event, that EPBs exhibit a westward drift trend with velocities ranging from a few tens to hundreds of meters per second in GPS and BDS observations.

Consistency, variability, and predictability of on-farm nutrient responses in four grain legumes across East and West Africa.

Grain legumes are key components of sustainable production systems in sub-Saharan Africa, but wide-spread nutrient deficiencies severely restrict yields. Whereas legumes can meet a large part of their nitrogen (N) requirement through symbiosis with N2-fixing bacteria, elements such as phosphorus (P), potassium (K) and secondary and micronutrients may still be limiting and require supplementation. Responses to P are generally strong but variable, while evidence for other nutrients tends to show weak or highly localised effects. Here we present the results of a joint statistical analysis of a series of on-farm nutrient addition trials, implemented across four legumes in four countries over two years. Linear mixed models were used to quantify both mean nutrient responses and their variability, followed by a random forest analysis to determine the extent to which such variability can be explained or predicted by geographic, environmental or farm survey data. Legume response to P was indeed variable, but consistently positive and we predicted application to be profitable for 67% of farms in any given year, based on prevailing input costs and grain prices. Other nutrients did not show significant mean effects, but considerable response variation was found. This response heterogeneity was mostly associated with local or temporary factors and could not be explained or predicted by spatial, biophysical or management factors. An exception was K response, which displayed appreciable spatial variation that could be partly accounted for by spatial and environmental covariables. While of apparent relevance for targeted recommendations, the minor amplitude of expected response, the large proportion of unexplained variation and the unreliability of the predicted spatial patterns suggests that such data-driven targeting is unlikely to be effective with current data.

A COMPLEX APPROACH IN ASSESSING RELIABILITY INDICATORS OF ELEMENTS OF TECHNICAL SYSTEMS WHEN DEGRADATION FAILURES OCCUR

The construction and practical use of complexes of mechanical reliability models creates real prerequisites for the transition to resource design of assembly units and aggregates of mobile machines. Therefore, the article solves the tasks of building complexes of inverted stochastic models, which provide the possibility of joint statistical analysis of heterogeneous information about reliability in cases of types of destruction typical for machine elements: during wear and fatigue. A fairly complete and convenient method for solving many problems of mechanical reliability is given for describing a non-stationary monotonic degradation process, which consists in the task of the type of its function or the density of distribution in time segments with parameters that are deterministic monotonic functions of time or working time in load cycles. Such functions should be perceived as parametric. Schemes of models of two main types of degradation processes in machine elements are also given: monotonically increasing, associated with wear and monotonically decreasing, due to fatigue failure. At the same time, the process corresponding to wear is formed by random realizations of a change in the failure parameter over time, and the process characterizing fatigue failure is formed by a set of random individual fatigue curves that depend on cyclic durability. The considered theoretical analysis of the main degradation processes that determine the mechanical reliability of machine elements made it possible to develop a universal approach to building complexes of stochastic reliability models, which provides the possibility of joint statistical evaluation of model parameters for processes caused by wear and tear and fatigue destruction. The practical use of the proposed complexes of mechanical reliability models is expedient both for accelerated assessment and for predicting reliability at the stage of resource design of elements of mobile machines.

Intervention Methods of College Counselors on Students’ Psychological Crisis under the Background of Deep Learning

In order to quantitatively evaluate the effect of college counselors’ intervention methods on students’ psychological crisis, an evaluation model of college counselors’ intervention methods on students’ psychological crisis based on the deep learning model is put forward. The learning model function of college counselors’ intervention in students’ psychological crisis is constructed. By using the joint statistical feature analysis method, the dynamic factor analysis of college counselors’ intervention in students’ psychological crisis is established, and the machine learning model of college counselors’ intervention in students’ psychological crisis is constructed. By using the methods of big data fusion and correlation dimension feature analysis, combined with a fuzzy C-means clustering algorithm, the reliability evaluation of college counselors’ intervention on students’ psychological crisis and the construction of a large database are realized. By using the deep learning model, the parameters of college counselors’ intervention model on students’ psychological crisis are optimized and analyzed, and the methods of college counselors’ intervention on students’ psychological crisis are optimized and designed. The test shows that it is reliable and expandable to use this method for college counselors’ intervention in students’ psychological crisis, and it can establish the applicable technology of knowledge network to realize the correct intervention of college counselors in students’ psychological crisis.

Combining GWAS and TWAS to identify candidate causal genes for tocochromanol levels in maize grain.

Tocochromanols (tocopherols and tocotrienols, collectively vitamin E) are lipid-soluble antioxidants important for both plant fitness and human health. The main dietary sources of vitamin E are seed oils that often accumulate high levels of tocopherol isoforms with lower vitamin E activity. The tocochromanol biosynthetic pathway is conserved across plant species but an integrated view of the genes and mechanisms underlying natural variation of tocochromanol levels in seed of most cereal crops remains limited. To address this issue, we utilized the high mapping resolution of the maize Ames panel of ∼1,500 inbred lines scored with 12.2 million single-nucleotide polymorphisms to generate metabolomic (mature grain tocochromanols) and transcriptomic (developing grain) data sets for genetic mapping. By combining results from genome- and transcriptome-wide association studies, we identified a total of 13 candidate causal gene loci, including 5 that had not been previously associated with maize grain tocochromanols: 4 biosynthetic genes (arodeH2 paralog, dxs1, vte5, and vte7) and a plastid S-adenosyl methionine transporter (samt1). Expression quantitative trait locus (eQTL) mapping of these 13 gene loci revealed that they are predominantly regulated by cis-eQTL. Through a joint statistical analysis, we implicated cis-acting variants as responsible for colocalized eQTL and GWAS association signals. Our multiomics approach provided increased statistical power and mapping resolution to enable a detailed characterization of the genetic and regulatory architecture underlying tocochromanol accumulation in maize grain and provided insights for ongoing biofortification efforts to breed and/or engineer vitamin E and antioxidant levels in maize and other cereals.

Optimal Spiking Experiment for Noninferiority of Qualitative Microbiological Methods on Accuracy With Multiple Microorganisms

The European and United States Pharmacopoeia demand a noninferiority study on the detection of microorganisms when an alternate qualitative microbiological method is intended to replace the compendial microbiological method. However, without imposing any modeling assumptions or constraints, noninferiority studies require large numbers of test samples for a proposed noninferiority criterion of 0.7 or higher for each microorganism. When we can assume that the accuracy of the alternate method with respect to the compendial method is homogeneous across microorganisms, a joint statistical analysis of the data from all microorganisms can be used to help reduce the sample size dramatically. For this situation, we provide a test statistic for noninferiority, an optimal spiking experiment, and a sample size calculation approach under only mild modeling assumptions of the microorganism-specific detection proportions. We illustrate our approach on a real dataset and demonstrate good performance of our method using simulation studies.

Assessment of Patient Perspectives and Barriers to Self-Infusion of Augmentation Therapy for Alpha-1 Antitrypsin Deficiency During the COVID-19 Pandemic.

IntroductionAlpha-1 antitrypsin (AAT) deficiency is an autosomal co-dominant genetic condition that predisposes individuals to pulmonary and hepatic disease, and in severe cases is treated with augmentation by intravenous infusion. Our aim was to assess patient reluctance to transition to self-administered augmentation of alpha-1-antitrypsin, during the pandemic of SARS-CoV-2.MethodsA phone questionnaire was administered to 22 patients with severe alpha-1-antitrypsin deficiency who were currently receiving AAT augmentation therapy. Inclusion criteria included patients ge 18 years old, diagnosed with AATD, and receiving intravenous AAT protein augmentation therapy. Information was gathered regarding demographics, perspectives on transitioning to self-administered treatment, and anxiety and depression prevalence. Results were collected anonymously using REDCap. Joint and marginal statistical analysis was done to quantify links between participants’ willingness to transition to self-infusion and correlations with sex, age, years of therapy, anxiety, and depression.ResultsOf 22 patients, 14 were male and eight were female. Ages ranged from 36 to 79 years, with an average of 62.5. Genotypes were ZZ (14), MZ (3), and SZ (2) among others. Average length of intravenous augmentation was 9.5 years. The majority, 16 participants, were aware self-infusion was an option. Eight participants were willing to consider transitioning to self-infusion if trained and educated. Eight patients reported that fear of COVID-19 transmission influenced their decision-making. Above-normal anxiety, and depression scores, were found in four, and six patients, respectively. Neither sex, age, years of treatment, anxiety, or depression were found to be associated with willingness to consider self-infusion therapy.ConclusionsAlthough there are many reasons AATD patients may benefit from AAT self-infusion, including decreased exposure to SARS-CoV-2, the majority preferred home nurse-infused therapy.Supplementary InformationThe online version contains supplementary material available at 10.1007/s41030-022-00182-z.

Large-Scale Assessment of Mobile Crowdsensed Data: A Case Study

Mobile crowdsensing (MCS) is a well-established paradigm that leverages mobile devices’ ubiquitous nature and processing capabilities for large-scale data collection to monitor phenomena of common interest. Crowd-powered data collection is significantly faster and more cost-effective than traditional methods. However, it poses challenges in assessing the accuracy and extracting information from large volumes of user-generated data. SmartRoadSense (SRS) is an MCS technology that utilises sensors embedded in mobile phones to monitor the quality of road surfaces by computing a crowdsensed road roughness index (referred to as PPE). The present work performs statistical modelling of PPE to analyse its distribution across the road network and elucidate how it can be efficiently analysed and interpreted. Joint statistical analysis of open datasets is then carried out to investigate the effect of both internal and external road features on PPE. Several road properties affecting PPE as predicted are identified, providing evidence that SRS can be effectively applied to assess road quality conditions. Finally, the effect of road category and the speed limit on the mean and standard deviation of PPE is evaluated, incorporating previous results on the relationship between vehicle speed and PPE. These results enable more effective and confident use of the SRS platform and its data to help inform road construction and renovation decisions, especially where a lack of resources limits the use of conventional approaches. The work also exemplifies how crowdsensing technologies can benefit from open data integration and highlights the importance of making coherent, comprehensive, and well-structured open datasets available to the public.

Linear perturbations spectra for dynamical dark energy inspired by modified gravity

In this paper, we study a particular modified gravity Equation of State (EoS), the so-called Jaime–Jaber–Escamilla, that emerges from the first gravity modified action principle and can reproduce three cosmological viable [Formula: see text] theories: the Starobinsky, Hu–Sawicki and Exponential models. This EoS is a suitable candidate to reproduce the dynamical dark energy behaviour already reconstructed by the current data sets. Based on the joint statistical analysis, we found that our results are still in good agreement (within [Formula: see text]) with the [Formula: see text]CDM, while at perturbative level, we note that the matter power spectrum normalisation factor [Formula: see text] shows an agreement with SDSS and SNeIa+IRAS at 1-[Formula: see text] for the Starobinsky model and with SDSS-vec for the Hu–Sawicki and Exponential models. Furthermore, we found that for the [Formula: see text] values, Starobinsky and Hu–Sawicki show the least tension in comparison with PL18 TT. All these aspects cannot be observed directly from other alternatives theories, where an equation of state is difficult to compute analytically.

A COMPREHENSIVE EVALUATION OF FLOOD HAZARDS FOR SABINE TO GALVESTON, TX, USA REGION

The Sabine Pass to Galveston Bay, TX Pre-Construction, Engineering and Design: Hurricane Coastal Storm Surge and Wave Hazard Assessment (S2G) includes flood hazard assessment for over 100 km of levee/floodwall/gate coastal storm risk management systems. The focus of the flood hazard assessment was to compute coupled water level and wave response near the structures, runup, and overtopping rate, combined hydrostatic and hydrodynamic pressure distributions on walls, overtopping nappe characteristics and other pertinent responses stochastically. Joint statistical analysis of forcing parameters, including epistemic uncertainty, was conducted to compute hazards. Design was based on varied confidence levels and thus reflected uncertainty. The approach brought high-fidelity components together to produce accurate probabilistic coastal flood hazard assessment.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/U_dFfj2oJ2A

Detection of haplotype-dependent allele-specific DNA methylation in WGBS data

In heterozygous genomes, allele-specific measurements can reveal biologically significant differences in DNA methylation between homologous alleles associated with local changes in genetic sequence. Current approaches for detecting such events from whole-genome bisulfite sequencing (WGBS) data perform statistically independent marginal analysis at individual cytosine-phosphate-guanine (CpG) sites, thus ignoring correlations in the methylation state, or carry-out a joint statistical analysis of methylation patterns at four CpG sites producing unreliable statistical evidence. Here, we employ the one-dimensional Ising model of statistical physics and develop a method for detecting allele-specific methylation (ASM) events within segments of DNA containing clusters of linked single-nucleotide polymorphisms (SNPs), called haplotypes. Comparisons with existing approaches using simulated and real WGBS data show that our method provides an improved fit to data, especially when considering large haplotypes. Importantly, the method employs robust hypothesis testing for detecting statistically significant imbalances in mean methylation level and methylation entropy, as well as for identifying haplotypes for which the genetic variant carries significant information about the methylation state. As such, our ASM analysis approach can potentially lead to biological discoveries with important implications for the genetics of complex human diseases.

Constraints on sigma _8 and degeneracies from linear Nash-Greene perturbations in subhorizon scale

Using a joint statistical analysis, we test a four-dimensional FLRW model embedded in a five-dimensional bulk based on the Nash-Greene embedding theorem. Performing a Markov Chain Monte Carlo (MCMC) modelling, we combine observational data sets as those of the recent growth data, the best-fit Planck2018/varLambda CDM parameters on the Cosmic Microwave Background (CMB), the Baryon Acoustic Oscillations (BAO) measurements, the Pantheon Supernovae type Ia and the Hubble parameter data. From linear Nash-Greene fluctuations of the metric, we show the related perturbed equations in longitudinal Newtonian gauge to obtain the evolution of growth matter. A mild alleviation may be obtained from the degeneracies on the model parameter analyzing the sigma tension between the growth amplitude factor and the matter content in the plane (sigma _8-varOmega _m) on the observations from CMB and Large Scale Structure (LSS) probes. The Akaike Information Criterion (AIC) is also applied and we find a relative statistical consistence of the present model with both varLambda CDM and wCDM models lower than 1% of percentage difference at early times on the evolution of the Hubble function H(z). We also apply the Om(z) diagnosis to distinguish the present model from varLambda CDM and wCDM models.

Analysis of The Joint Impact of Synchronous Discharges in Estimating the Flood Risk: Case Study on Hron River

The paper presents the methodology of bivariate statistical analysis of the joint impact of synchronous discharges in estimating the flood risk on Hron River and its tributary Slatina in Central Slovakia. Basic statistical analysis approach gives satisfactory results in the case of simple systems, for example, where the main river does not capture major tributaries. These conventional approaches may not give satisfactory results for the evaluation of flood risk in situations where floods occur on two or more rivers and join together at the same time. As input data mean daily discharges were used. Some Archimedean copula functions as a mathematical tool for joint as well as conditional probability distribution calculation of the synchronous variables were used. This class of copulas is popular in empirical applications for flexibility, easy construction and includes a whole suite of closed-form copulas that covers a wide range of dependency structures, including comprehensive and non-comprehensive copulas, radial symmetry and asymmetry, and asymptotic tail dependence and independence. The first part of the paper presents the preparation of the input data and choice of appropriate marginal probability distributions. The next part, presents the testing and selection of the appropriate copula function for the bivariate joint statistical analysis of the synchronous discharges. Tested Archimedes copula functions have achieved relatively equal calculated distribution probabilities. Probabilities calculated using the Gumbel-Hougaard copula function achieved the least error of the estimation. This copula function has been selected as the most appropriate for illustrating the joint distribution probability and consequently to determine the joint probability of occurrence of the synchronic discharges. The results showed that the joint probability of maximum discharges is relatively low, but not unlikely. In the context of climatic extreme events, statistical techniques such as event coincidence analysis will be relevant for investigating the impacts of anthropogenic climate change on human societies and ecosystems worldwide. The results obtained by the bivariate analysis of the variables which characterize the hydrological regime can contribute to a more reliable assessment of the flood risks.

Measuring the supernova unknowns at the next-generation neutrino telescopes through the diffuse neutrino background

The detection of the diffuse supernova neutrino background (DSNB) will preciously contribute to gauge the properties of the core-collapse supernova population. We estimate the DSNB event rate in the next-generation neutrino detectors, Hyper-Kamiokande enriched with Gadolinium, JUNO, and DUNE. The determination of the supernova unknowns through the DSNB will be heavily driven by Hyper-Kamiokande, given its higher expected event rate, and complemented by DUNE that will help in reducing the parameters uncertainties. Meanwhile, JUNO will be sensitive to the DSNB signal over the largest energy range. A joint statistical analysis of the expected rates in 20 years of data taking from the above detectors suggests that we will be sensitive to the local supernova rate at most at a 20−33% level. A non-zero fraction of supernovae forming black holes will be confirmed at a 90% CL, if the true value of that fraction is ≳20%. On the other hand, the DSNB events show extremely poor statistical sensitivity to the nuclear equation of state and mass accretion rate of the progenitors forming black holes.

Cosmological constraints from a joint analysis of cosmic growth and expansion

Abstract Combining measurements on the expansion history of the Universe and on the growth rate of cosmic structures is key to discriminate between alternative cosmological frameworks and to test gravity. Recently, Linder proposed a new diagram to investigate the joint evolutionary track of these two quantities. In this letter, we collect the most recent cosmic growth and expansion rate data sets to provide the state-of-the-art observational constraints on this diagram. By performing a joint statistical analysis of both probes, we test the standard Λcold dark matter model, confirming a mild tension between cosmic microwave background predictions from Planck mission and cosmic growth measurements at low redshift (z < 2). Then we test alternative models allowing the variation of one single cosmological parameter at a time. In particular, we find a larger growth index than the one predicted by general relativity $\gamma =0.65^{+0.05}_{-0.04}$. However, also a standard model with total neutrino mass of 0.26 ± 0.10 eV provides a similarly accurate description of the current data. By simulating an additional data set consistent with next-generation dark-energy mission forecasts, we show that growth rate constraints at z > 1 will be crucial to discriminate between alternative models.

Dark matter constraints from a joint analysis of dwarf Spheroidal galaxy observations with VERITAS

We present constraints on the annihilation cross section of WIMP dark matter based on the joint statistical analysis of four dwarf galaxies with VERITAS. These results are derived from an optimized photon weighting statistical technique that improves on standard imaging atmospheric Cherenkov telescope (IACT) analyses by utilizing the spectral and spatial properties of individual photon events. We report on the results of $\sim$230 hours of observations of five dwarf galaxies and the joint statistical analysis of four of the dwarf galaxies. We find no evidence of gamma-ray emission from any individual dwarf nor in the joint analysis. The derived upper limit on the dark matter annihilation cross section from the joint analysis is $1.35\times 10^{-23} {\mathrm{ cm^3s^{-1}}}$ at 1 TeV for the bottom quark ($b\bar{b}$) final state, $2.85\times 10^{-24}{\mathrm{ cm^3s^{-1}}}$ at 1 TeV for the tau lepton ($\tau^{+}\tau^{-}$) final state and $1.32\times 10^{-25}{\mathrm{ cm^3s^{-1}}}$ at 1 TeV for the gauge boson ($\gamma\gamma$) final state.

Testing Einstein’s gravity and dark energy with growth of matter perturbations: Indications for new physics?

The growth index of matter fluctuations is computed for ten distinct accelerating cosmological models and confronted to the latest growth rate data via a two-step process. First, we implement a joint statistical analysis in order to place constraints on the free parameters of all models using solely background data. Second, using the observed growth rate of clustering from various galaxy surveys we test the performance of the current cosmological models at the perturbation level while either marginalizing over $\sigma_8$ or having it as a free parameter. As a result, we find that at a statistical level, i.e. after considering the best-fit $\chi^2$ or the value of the Akaike information criterion, most models are in very good agreement with the growth rate data and are practically indistinguishable from $\Lambda$CDM. However, when we also consider the internal consistency of the models by comparing the theoretically predicted values of $(\gamma_0, \gamma_1)$, i.e. the value of the growth index $\gamma(z)$ and its derivative today, with the best-fit ones, we find that the predictions of three out of ten dark energy models are in mild tension with the best-fit ones when $\sigma_8$ is marginalized over. When $\sigma_8$ is free we find that most models are not only in mild tension, but also predict low values for $\sigma_8$. This could be attributed to either a systematic problem with the growth-rate data or the emergence of new physics at low redshifts, with the latter possibly being related to the well-known issue of the lack of power at small scales. Finally, by utilizing mock data based on an LSST-like survey we show that with future surveys and by using the growth index parameterization, it will be possible to resolve the issue of the low $\sigma_8$ but also the tension between the fitted and theoretically predicted values of $(\gamma_0, \gamma_1)$.