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Martinelli, M., Melchiorri, A., Mena, O., Salvatelli, V., & Girones, Z. (2012). Future constraints on the Hu-Sawicki modified gravity scenario. Phys. Rev. D, 85(2), 024006–7pp.
Abstract: We present current and future constraints on the Hu and Sawicki modified gravity scenario. This model can reproduce a late time accelerated universe and evade Solar System constraints. While current cosmological data still allows for distinctive deviations from the cosmological constant picture, future measurements of the growth of structure combined with supernova Ia luminosity distance data will greatly improve present constraints.
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de Salas, P. F., Forero, D. V., Gariazzo, S., Martinez-Mirave, P., Mena, O., Ternes, C. A., et al. (2021). 2020 global reassessment of the neutrino oscillation picture. J. High Energy Phys., 02(2), 071–36pp.
Abstract: We present an updated global fit of neutrino oscillation data in the simplest three-neutrino framework. In the present study we include up-to-date analyses from a number of experiments. Concerning the atmospheric and solar sectors, besides the data considered previously, we give updated analyses of IceCube DeepCore and Sudbury Neutrino Observatory data, respectively. We have also included the latest electron antineutrino data collected by the Daya Bay and RENO reactor experiments, and the long-baseline T2K and NO nu A measurements, as reported in the Neutrino 2020 conference. All in all, these new analyses result in more accurate measurements of theta (13), theta (12), Delta m212 and Delta m312. The best fit value for the atmospheric angle theta (23) lies in the second octant, but first octant solutions remain allowed at similar to 2.4 sigma. Regarding CP violation measurements, the preferred value of delta we obtain is 1.08 pi (1.58 pi) for normal (inverted) neutrino mass ordering. The global analysis still prefers normal neutrino mass ordering with 2.5 sigma statistical significance. This preference is milder than the one found in previous global analyses. These new results should be regarded as robust due to the agreement found between our Bayesian and frequentist approaches. Taking into account only oscillation data, there is a weak/moderate preference for the normal neutrino mass ordering of 2.00 sigma. While adding neutrinoless double beta decay from the latest Gerda, CUORE and KamLAND-Zen results barely modifies this picture, cosmological measurements raise the preference to 2.68 sigma within a conservative approach. A more aggressive data set combination of cosmological observations leads to a similar preference for normal with respect to inverted mass ordering, namely 2.70 sigma. This very same cosmological data set provides 2 sigma upper limits on the total neutrino mass corresponding to Sigma m(nu)< 0.12 (0.15) eV in the normal (inverted) neutrino mass ordering scenario. The bounds on the neutrino mixing parameters and masses presented in this up-to-date global fit analysis include all currently available neutrino physics inputs.
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Anderson, L. et al, & Mena, O. (2014). The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey: baryon acoustic oscillations in the Data Releases 10 and 11 Galaxy samples. Mon. Not. Roy. Astron. Soc., 441(1), 24–62.
Abstract: We present a one per cent measurement of the cosmic distance scale from the detections of the baryon acoustic oscillations (BAO) in the clustering of galaxies from the Baryon Oscillation Spectroscopic Survey, which is part of the Sloan Digital Sky Survey III. Our results come from the Data Release 11 (DR11) sample, containing nearly one million galaxies and covering approximately 8500 square degrees and the redshift range 0.2 < z < 0.7. We also compare these results with those from the publicly released DR9 and DR10 samples. Assuming a concordance A cold dark matter (ACDM) cosmological model, the DR11 sample covers a volume of 13 Gpc(3) and is the largest region of the Universe ever surveyed at this density. We measure the correlation function and power spectrum, including density- field reconstruction of the BAO feature. The acoustic features are detected at a significance of over 7s in both the correlation function and power spectrum. Fitting for the position of the acoustic features measures the distance relative to the sound horizon at the drag epoch, r(d), which has a value of r(d,fid) = 149.28 Mpc in our fiducial cosmology. We find D-V = (1264 +/- 25 Mpc)(r(d)/r(d,fid)) at z = 0.32 and D-V = (2056 +/- 20 Mpc)(r(d)/r(d,fid)) at z = 0.57. At 1.0 per cent, this latter measure is the most precise distance constraint ever obtained from a galaxy survey. Separating the clustering along and transverse to the line of sight yields measurements at z = 0.57 of D-A = (1421 +/- 20 Mpc)(r(d)/r(d,fid)) and H = (96.8 +/- 3.4 kms(-1) Mpc(-1))(r(d),(fid)/r(d)). Our measurements of the distance scale are in good agreement with previous BAO measurements and with the predictions from cosmic microwave background data for a spatially flat CDM model with a cosmological constant.
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Gerbino, M., Lattanzi, M., Mena, O., & Freese, K. (2017). A novel approach to quantifying the sensitivity of current and future cosmological datasets to the neutrino mass ordering through Bayesian hierarchical modeling. Phys. Lett. B, 775, 239–250.
Abstract: We present a novel approach to derive constraints on neutrino masses, as well as on other cosmological parameters, from cosmological data, while taking into account our ignorance of the neutrino mass ordering. We derive constraints from a combination of current as well as future cosmological datasets on the total neutrino mass M-nu and on the mass fractions f(nu),i = m(i)/M-nu (where the index i = 1, 2, 3 indicates the three mass eigenstates) carried by each of the mass eigenstates m(i), after marginalizing over the (unknown) neutrino mass ordering, either normal ordering (NH) or inverted ordering (IH). The bounds on all the cosmological parameters, including those on the total neutrino mass, take therefore into account the uncertainty related to our ignorance of the mass hierarchy that is actually realized in nature. This novel approach is carried out in the framework of Bayesian analysis of a typical hierarchical problem, where the distribution of the parameters of the model depends on further parameters, the hyperparameters. In this context, the choice of the neutrino mass ordering is modeled via the discrete hyperparameter h(type), which we introduce in the usual Markov chain analysis. The preference from cosmological data for either the NH or the IH scenarios is then simply encoded in the posterior distribution of the hyper-parameter itself. Current cosmic microwave background (CMB) measurements assign equal odds to the two hierarchies, and are thus unable to distinguish between them. However, after the addition of baryon acoustic oscillation (BAO) measurements, a weak preference for the normal hierarchical scenario appears, with odds of 4 : 3 from Planck temperature and large-scale polarization in combination with BAO (3 : 2 if small-scale polarization is also included). Concerning next-generation cosmological experiments, forecasts suggest that the combination of upcoming CMB (COrE) and BAO surveys (DESI) may determine the neutrino mass hierarchy at a high statistical significance if the mass is very close to the minimal value allowed by oscillation experiments, as for NH and a fiducial value of M-nu = 0.06 eV there is a 9 : 1 preference of normal versus inverted hierarchy. On the contrary, if the sum of the masses is of the order of 0.1 eV or larger, even future cosmological observations will be inconclusive. The innovative statistical strategy exploited here represents a very simple, efficient and robust tool to study the sensitivity of present and future cosmological data to the neutrino mass hierarchy, and a sound competitor to the standard Bayesian model comparison. The unbiased limit on M-nu we obtain is crucial for ongoing and planned neutrinoless double beta decay searches.
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Gariazzo, S., Archidiacono, M., de Salas, P. F., Mena, O., Ternes, C. A., & Tortola, M. (2018). Neutrino masses and their ordering: global data, priors and models. J. Cosmol. Astropart. Phys., 03(3), 011–22pp.
Abstract: We present a full Bayesian analysis of the combination of current neutrino oscillation, neutrinoless double beta decay and Cosmic Microwave Background observations. Our major goal is to carefully investigate the possibility to single out one neutrino mass ordering, namely Normal Ordering or Inverted Ordering, with current data. Two possible parametrizations (three neutrino masses versus the lightest neutrino mass plus the two oscillation mass splittings) and priors (linear versus logarithmic) are exhaustively examined. We find that the preference for NO is only driven by neutrino oscillation data. Moreover, the values of the Bayes factor indicate that the evidence for NO is strong only when the scan is performed over the three neutrino masses with logarithmic priors; for every other combination of parameterization and prior, the preference for NO is only weak. As a by-product of our Bayesian analyses, we are able to (a) compare the Bayesian bounds on the neutrino mixing parameters to those obtained by means of frequentist approaches, finding a very good agreement; (b) determine that the lightest neutrino mass plus the two mass splittings parametrization, motivated by the physical observables, is strongly preferred over the three neutrino mass eigenstates scan and (c) find that logarithmic priors guarantee a weakly-to-moderately more efficient sampling of the parameter space. These results establish the optimal strategy to successfully explore the neutrino parameter space, based on the use of the oscillation mass splittings and a logarithmic prior on the lightest neutrino mass, when combining neutrino oscillation data with cosmology and neutrinoless double beta decay. We also show that the limits on the total neutrino mass Sigma m(nu) can change dramatically when moving from one prior to the other. These results have profound implications for future studies on the neutrino mass ordering, as they crucially state the need for self-consistent analyses which explore the best parametrization and priors, without combining results that involve different assumptions.
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