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Lessa, L. A., Maluf, R. V., Silva, J. E. G., & Almeida, C. A. S. (2024). Braneworlds in warped Einsteinian cubic gravity. J. Cosmol. Astropart. Phys., 05(5), 123–25pp.
Abstract: Einstenian cubic gravity (ECG) is a modified theory of gravity constructed with cubic contractions of the curvature tensor. This theory has the remarkable feature of having the same two propagating degrees of freedom of Einstein gravity (EG), at the perturbative level on maximally symmetric spacetimes. The additional unstable modes steaming from the higher order derivative dynamics are suppressed provided that we consider the ECG as an effective field theory wherein the cubic terms are seen as perturbative corrections of the Einstein -Hilbert term. Extensions of ECG have been proposed in cosmology and compact objects in order to probe if this property holds in more general configurations. In this work, we construct a modified ECG gravity in a five dimensional warped braneworld scenario. By assuming a specific combination of the cubic parameters, we obtained modified gravity equations of motion with terms up to second -order. For a thin 3-brane, the cubic -gravity corrections yield an effective positive bulk cosmological constant. Thus, in order to keep the 5D bulk warped compact, an upper bound of the cubic parameter with respect to the bulk curvature was imposed. For a thick brane, the cubic -gravity terms modify the scalar field potential and its corresponding vacuum. Nonetheless, the domain -wall structure with a localized source is preserved. At the perturbative level, the Kaluza-Klein (KK) tensor gravitational modes are stable and possess a localized massless mode provided the cubic corrections are small compared to the EG braneworld.
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Babak, S., Caprini, C., Figueroa, D. G., Karnesis, N., Marcoccia, P., Nardini, G., et al. (2023). Stochastic gravitational wave background from stellar origin binary black holes in LISA. J. Cosmol. Astropart. Phys., 08(8), 034–40pp.
Abstract: We use the latest constraints on the population of stellar origin binary black holes (SOBBH) from LIGO/Virgo/KAGRA (LVK) observations, to estimate the stochastic gravi-tational wave background (SGWB) they generate in the frequency band of LISA. In order to account for the faint and distant binaries, which contribute the most to the SGWB, we extend the merger rate at high redshift assuming that it tracks the star formation rate. We adopt different methods to compute the SGWB signal: we perform an analytical evaluation, we use Monte Carlo sums over the SOBBH population realisations, and we account for the role of the detector by simulating LISA data and iteratively removing the resolvable signals until only the confusion noise is left. The last method allows the extraction of both the expected SGWB and the number of resolvable SOBBHs. Since the latter are few for signal-to-noise ratio thresholds larger than five, we confirm that the spectral shape of the SGWB in the LISA band agrees with the analytical prediction of a single power law. We infer the probability dis-tribution of the SGWB amplitude from the LVK GWTC-3 posterior of the binary population model: at the reference frequency of 0.003 Hz it has an interquartile range of h(2 Omega)GW(f = 3 x 10(-3) Hz) is an element of [5.65, 11.5] x 10(-13), in agreement with most previous estimates. We then perform a MC analysis to assess LISA's capability to detect and characterise this signal. Ac-counting for both the instrumental noise and the galactic binaries foreground, with four years of data, LISA will be able to detect the SOBBH SGWB with percent accuracy, narrowing down the uncertainty on the amplitude by one order of magnitude with respect to the range of possible amplitudes inferred from the population model. A measurement of this signal by LISA will help to break the degeneracy among some of the population parameters, and pro-vide interesting constraints, in particular on the redshift evolution of the SOBBH merger rate.
Keywords: gravitational waves; experiments; sources
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Forconi, M., Giare, W., Mena, O., Ruchika, Di Valentino, E., Melchiorri, A., et al. (2024). A double take on early and interacting dark energy from JWST. J. Cosmol. Astropart. Phys., 05(5), 097–37pp.
Abstract: The very first light captured by the James Webb Space Telescope (JWST) revealed a population of galaxies at very high redshifts more massive than expected in the canonical Lambda CDM model of structure formation. Barring, among others, a systematic origin of the issue, in this paper, we test alternative cosmological perturbation histories. We argue that models with a larger matter component ohm m and/or a larger scalar spectral index n s can substantially improve the fit to JWST measurements. In this regard, phenomenological extensions related to the dark energy sector of the theory are appealing alternatives, with Early Dark Energy emerging as an excellent candidate to explain (at least in part) the unexpected JWST preference for larger stellar mass densities. Conversely, Interacting Dark Energy models, despite producing higher values of matter clustering parameters such as sigma 8 , are generally disfavored by JWST measurements. This is due to the energy -momentum flow from the dark matter to the dark energy sector, implying a smaller matter energy density. Upcoming observations may either strengthen the evidence or falsify some of these appealing phenomenological alternatives to the simplest Lambda CDM picture.
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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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