LISA Cosmology Working Group(Bartolo, N. et al), & Figueroa, D. G. (2022). Probing anisotropies of the Stochastic Gravitational Wave Background with LISA. J. Cosmol. Astropart. Phys., 11, 009–65pp.
Abstract: We investigate the sensitivity of the Laser Interferometer Space Antenna (LISA) to the anisotropies of the Stochastic Gravitational Wave Background (SGWB). We first discuss the main astrophysical and cosmological sources of SGWB which are characterized by anisotropies in the GW energy density, and we build a Signal-to-Noise estimator to quantify the sensitivity of LISA to different multipoles. We then perform a Fisher matrix analysis of the prospects of detectability of anisotropic features with LISA for individual multipoles, focusing on a SGWB with a power-law frequency profile. We compute the noise angular spectrum taking into account the specific scan strategy of the LISA detector. We analyze the case of the kinematic dipole and quadrupole generated by Doppler boosting an isotropic SGWB. We find that beta Omega(GW) similar to 2 x 10(-11) is required to observe a dipolar signal with LISA. The detector response to the quadrupole has a factor similar to 10(3) beta relative to that of the dipole. The characterization of the anisotropies, both from a theoretical perspective and from a map-making point of view, allows us to extract information that can be used to understand the origin of the SGWB, and to discriminate among distinct superimposed SGWB sources.
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Martinelli, M., Scarcella, F., Hogg, N. B., Kavanagh, B. J., Gaggero, D., & Fleury, P. (2022). Dancing in the dark: detecting a population of distant primordial black holes. J. Cosmol. Astropart. Phys., 08(8), 006–47pp.
Abstract: Primordial black holes (PBHs) are compact objects proposed to have formed in the early Universe from the collapse of small-scale over-densities. Their existence may be detected from the observation of gravitational waves (GWs) emitted by PBH mergers, if the signals can be distinguished from those produced by the merging of astrophysical black holes. In this work, we forecast the capability of the Einstein Telescope, a proposed third-generation GW observatory, to identify and measure the abundance of a subdominant population of distant PBHs, using the difference in the redshift evolution of the merger rate of the two populations as our discriminant. We carefully model the merger rates and generate realistic mock catalogues of the luminosity distances and errors that would be obtained from GW signals observed by the Einstein Telescope. We use two independent statistical methods to analyse the mock data, finding that, with our more powerful, likelihood-based method, PBH abundances as small as fPBH approximate to 7 x 10(-6) ( fPBH approximate to 2 x 10(-6)) would be distinguishable from f(PBH) = 0 at the level of 3 sigma with a one year (ten year) observing run of the Einstein Telescope. Our mock data generation code, darksirens, is fast, easily extendable and publicly available on GitLab.
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Gariazzo, S., Gerbino, M., Brinckmann, T., Lattanzi, M., Mena, O., Schwetz, T., et al. (2022). Neutrino mass and mass ordering: no conclusive evidence for normal ordering. J. Cosmol. Astropart. Phys., 10(10), 010–18pp.
Abstract: The extraction of the neutrino mass ordering is one of the major challenges in particle physics and cosmology, not only for its implications for a fundamental theory of mass generation in nature, but also for its decisive role in the scale of future neutrinoless double beta decay experimental searches. It has been recently claimed that current oscillation, beta decay and cosmological limits on the different observables describing the neutrino mass parameter space provide robust decisive Bayesian evidence in favor of the normal ordering of the neutrino mass spectrum [1]. We further investigate these strong claims using a rich and wide phenomenology, with different sampling techniques of the neutrino parameter space. Contrary to the findings of Jimenez et al. [1], no decisive evidence for the normal mass ordering is found. Neutrino mass ordering analyses must rely on priors and parameterizations that are ordering-agnostic: robust results should be regarded as those in which the preference for the normal neutrino mass ordering is driven exclusively by the data, while we find a difference of up to a factor of 33 in the Bayes factors among the different priors and parameterizations exploited here. An ordering-agnostic prior would be represented by the case of parameterizations sampling over the two mass splittings and a mass scale, or those sampling over the individual neutrino masses via normal prior distributions only. In this regard, we show that the current significance in favor of the normal mass ordering should be taken as 2.7 sigma (i.e. moderate evidence), mostly driven by neutrino oscillation data. Let us stress that, while current data favor NO only mildly, we do not exclude the possibility that this may change in the future. Eventually, upcoming oscillation and cosmological data may (or may not) lead to a more significant exclusion of IO.
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Plaza, J., Martinez, T., Becares, V., Cano-Ott, D., Villamarin, D., de Rada, A. P., et al. (2023). Thermal neutron background at Laboratorio Subterraneo de Canfranc (LSC). Astropart Phys., 146, 102793–9pp.
Abstract: The thermal neutron background at Laboratorio Subterraneo de Canfranc (LSC) has been determined using several He-3 proportional counter detectors. Bare and Cd shielded counters were used in a series of long measurements. Pulse shape discrimination techniques were applied to discriminate between neutron and gamma signals as well as other intrinsic contributions. Montecarlo simulations allowed us to estimate the sensitivity of the detectors and calculate values for the background flux of thermal neutrons inside Hall-A of LSC. The obtained value is (3.5 +/- 0.8)x10(-6) n/cm(2)s, and is within an order of magnitude compared to similar facilities.
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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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