Barenboim, G., Fernandez-Martinez, E., Mena, O., & Verde, L. (2010). The dark side of curvature. J. Cosmol. Astropart. Phys., 03(3), 008–17pp.
Abstract: Geometrical tests such as the combination of the Hubble parameter H(z) and the angular diameter distance d(A)(z) can, in principle, break the degeneracy between the dark energy equation of state parameter w(z), and the spatial curvature Omega(k) in a direct, model-independent way. In practice, constraints on these quantities achievable from realistic experiments, such as those to be provided by Baryon Acoustic Oscillation (BAO) galaxy surveys in combination with CMB data, can resolve the cosmic confusion between the dark energy equation of state parameter and curvature only statistically and within a parameterized model for w(z). Combining measurements of both H(z) and d(A)(z) up to sufficiently high redshifts z similar to 2 and employing a parameterization of the redshift evolution of the dark energy equation of state are the keys to resolve the w(z) – Omega(k) degeneracy.
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Choi, K. Y., Lopez-Fogliani, D. E., Muñoz, C., & Ruiz de Austri, R. (2010). Gamma-ray detection from gravitino dark matter decay in the μnu SSM. J. Cosmol. Astropart. Phys., 03(3), 028–14pp.
Abstract: The μnu SSM provides a solution to the mu-problem of the MSSM and explains the origin of neutrino masses by simply using right-handed neutrino superfields. Given that R-parity is broken in this model, the gravitino is a natural candidate for dark matter since its lifetime becomes much longer than the age of the Universe. We consider the implications of gravitino dark matter in the μnu SSM, analyzing in particular the prospects for detecting gamma rays from decaying gravitinos. If the gravitino explains the whole dark matter component, a gravitino mass larger than 20 GeV is disfavored by the isotropic diffuse photon background measurements. On the other hand, a gravitino with a mass range between 0.1 – 20 GeV gives rise to a signal that might be observed by the FERMI satellite. In this way important regions of the parameter space of the μnu SSM can be checked.
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Reid, B. A., Verde, L., Jimenez, R., & Mena, O. (2010). Robust neutrino constraints by combining low redshift observations with the CMB. J. Cosmol. Astropart. Phys., 01(1), 003–21pp.
Abstract: We illustrate how recently improved low-redshift cosmological measurements can tighten constraints on neutrino properties. In particular we examine the impact of the assumed cosmological model on the constraints. We first consider the new HST H-0 = 74.2 +/- 3.6 measurement by Riess et al. (2009) and the sigma(8)(Omega(m)/0.25)(0.41) = 0.832 +/- 0.033 constraint from Rozo et al. (2009) derived from the SDSS maxBCG Cluster Catalog. In a ACDM model and when combined with WMAP5 constraints, these low-redshift measurements constrain Sigma m(v) < 0.4 eV at the 95% confidence level. This bound does not relax when allowing for the running of the spectral index or for primordial tensor perturbations. When adding also Supernovae and BAO constraints, we obtain a 95% upper limit of Sigma m(v) < 0.3eV. We test the sensitivity of the neutrino mass constraint to the assumed expansion history by both allowing a dark energy equation of state parameter w not equal -1 and by studying a model with coupling between dark energy and dark matter, which allows for variation in w, Omega(k), and dark coupling strength xi. When combining CMB, H-0 and the SDSS LRG halo power spectrum from Reid et al. 2009, we find that in this very general model, Sigma m(v) < 0.51 eV with 95% confidence. If we allow the number of relativistic species N-rel to vary in a ACDM model with Sigma m(v) = 0, we find N-rel = 3.76(-0.68)(+0.63)(+1.38 -1.21) for the 68% and 95% confidence intervals. We also report prior-independent constraints, which are in excellent agreement with the Bayesian constraints.
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Herrero-Garcia, J., Patrick, R., & Scaffidi, A. (2022). A semi-supervised approach to dark matter searches in direct detection data with machine learning. J. Cosmol. Astropart. Phys., 02, 039–19pp.
Abstract: The dark matter sector remains completely unknown. It is therefore crucial to keep an open mind regarding its nature and possible interactions. Focusing on the case of Weakly Interacting Massive Particles, in this work we make this general philosophy more concrete by applying modern machine learning techniques to dark matter direct detection. We do this by encoding and decoding the graphical representation of background events in the XENONnT experiment with a convolutional variational autoencoder. We describe a methodology that utilizes the `anomaly score' derived from the reconstruction loss of the convolutional variational autoencoder as well as a pre-trained standard convolutional neural network, in a semi-supervised fashion. Indeed, we observe that optimum results are obtained only when both unsupervised and supervised anomaly scores are considered together. A data set that has a higher proportion of anomaly score is deemed anomalous and deserves further investigation. Contrary to classical analyses, in principle all information about the events is used, preventing unnecessary information loss. Lastly, we demonstrate the reach of learning-focused anomaly detection in this context by comparing results with classical inference, observing that, if tuned properly, these techniques have the potential to outperform likelihood-based methods.
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