Revisiting a model-independent dark energy reconstruction method

Model independent reconstructions of dark energy have received some attention. The approach that addresses the reconstruction of the dimensionless coordinate distance and its two first derivatives using a polynomial fit in different redshift windows is well developed \cite{DalyDjorgovski1,DalyDjorgovski2,DalyDjorgovski3}. In this work we offer new insights into the problem by focusing on two types of observational probes: SNeIa and GRBs. Our results allow to highlight some of the intrinsic weaknesses of the method. One of the directions we follow is to consider updated observational samples. Our results indicate than conclusions on the main dark energy features as drawn from this method are intimately related to the features of the samples themselves (which are not quite ideal). This is particularly true of GRBs, which manifest themselves as poor performers in this context. In contrast to original works, we conclude they cannot be used for cosmological purposes, and the state of the art does not allow to regard them on the same quality basis as SNeIa. The next direction we contribute to is the question of how the adjusting of some parameters (window width, overlap, selection criteria) affect the results. We find again there is a considerable sensitivity to these features. Then, we try to establish what is the current redshift range for which one can make solid predictions on dark energy evolution. Finally, we strengthen the former view that this model is modest in the sense it provides only a picture of the global trend. But, on the other hand, we believe it offers an interesting complement to other approaches given that it works on minimal assumptions.Comment: revtex4-1, 17 page

DBI models for the unification of dark matter and dark energy

We propose a model based on a DBI action for the unification of dark matter and dark energy. This is supported by the results of the study of its background behavior at early and late times, and reinforced by the analysis of the evolution of perturbations. We also perform a Bayesian analysis to set observational constraints on the parameters of the model using type Ia SN, CMB shift and BAO data. Finally, to complete the study we investigate its kinematics aspects, such as the effective equation of state parameter, acceleration parameter and transition redshift. Particularizing those parameters for the best fit one appreciates that an effective phantom is preferred.Comment: 11 pages, 8 figures, revtex, new reference

RELICS: Strong Lensing Analysis of MACS J0417.5–1154 and Predictions for Observing the Magnified High-redshift Universe with JWST

Strong gravitational lensing by clusters of galaxies probes the mass distribution at the core of each cluster and magnifies the universe behind it. MACS J0417.5−1154 at z = 0.443 is one of the most massive clusters known based on weak lensing, X-ray, and Sunyaev–Zel'dovich analyses. Here we compute a strong lens model of MACS J0417 based on Hubble Space Telescope imaging observations collected, in part, by the Reionization Lensing Cluster Survey (RELICS), and recently reported spectroscopic redshifts from the MUSE instrument on the Very Large Telescope (VLT). We measure an Einstein radius of θ_{E} ≃ 36" at z = 9 and a mass projected within 200 kpc of M_(200 kpc) = 1.78_(0.03)^(0.01) x 10^(14) M_⊙. Using this model, we measure a ratio between the mass attributed to cluster-member galaxy halos and the main cluster halo of order 1:100. We assess the probability to detect magnified high-redshift galaxies in the field of this cluster, both for comparison with RELICS HST results and as a prediction for the James Webb Space Telescope (JWST) Guaranteed Time Observations upcoming for this cluster. Our lensing analysis indicates that this cluster has similar lensing strength to other clusters in the RELICS program. Our lensing analysis predicts a detection of at least a few z ~ 6–8 galaxies behind this cluster, at odds with a recent analysis that yielded no such candidates in this field. Reliable strong lensing models are crucial for accurately predicting the intrinsic properties of lensed galaxies. As part of the RELICS program, our strong lensing model produced with the Lenstool parametric method is publicly available through the Mikulski Archive for Space Telescopes

RELICS: A Strong Lens Model for SPT-CLJ0615–5746, a z = 0.972 Cluster

We present a lens model for the cluster SPT-CLJ0615−5746, which is the highest-redshift (z = 0.972) system in the Reionization of Lensing Clusters Survey, making it the highest-redshift cluster for which a full, strong lens model is published. We identify three systems of multiply imaged lensed galaxies, two of which we spectroscopically confirm at z = 1.358 and z = 4.013, which we use as constraints for the model. We find a foreground structure at z ~ 0.4, which we include as a second cluster-sized halo in one of our models; however, two different statistical tests find the best-fit model consists of one cluster-sized halo combined with three individually optimized galaxy-sized halos, as well as contributions from the cluster galaxies themselves. We find the total projected mass density within r = 26.”7 (the region where the strong lensing constraints exist) to be M = 2.51^(+0.15)_(-0.09) x 10^(14) M⊙. If we extrapolate out to r_(500), our projected mass density is consistent with the mass inferred from weak lensing and from the Sunyaev–Zel'dovich effect (M ~ 10^(15) M☉). This cluster is lensing a previously reported z ~ 10 galaxy, which, if spectroscopically confirmed, will be the highest-redshift strongly lensed galaxy known

eWOM in reward-based crowdfunding platforms: a behavioral approach

[EN] Electronic word of mouth (eWOM) plays a crucial role in influencing purchasing decisions of consumers in situations governed by asymmetric information. In this context, investors in reward-based crowdfunding platforms might modify their purchasing intentions according to recommendations of peers and/or experts. The goal of this paper is to analyze the power of eWOM to shape consumers’ purchasing decisions. We do so by conducting an experiment through Amazon Mechanical Turk (AMT). This online experimental tool allows for an instant access to a large and culturally diverse subject pool, facilitating behavioral research requiring large amounts of subjects. By recreating a reward-based crowdfunding webpage and tracking how consumers’ choices vary due to recommendations of other buyers and experts, this research confirms eWOM power in modifying purchasing decisions, as well as the prevalence of other buyers’ recommendations over those of experts. Additionally, it is tested AMT as a crowdsourcing platform that enables scholars to carry out online research related to economics and social sciences.Irene Comeig acknowledges financial support from the Spanish Ministry of Economy through grant number ECO2016-75575-R.Comeig Ramírez, I.; Sendra Pons, P. (2020). eWOM in reward-based crowdfunding platforms: a behavioral approach. Editorial Universitat Politècnica de València. 41-49. https://doi.org/10.4995/CARMA2020.2020.11413OCS414