Posters

We present an extension of the excursion-set theory presented in [1] and originally formulated by [2] by taking into account the large-scale perturbations induced by nearby protofilaments defined as saddle point in the potential field. The mass, accretion rate and formation time of dark matter haloes are analytically predicted. The model predicts that at fixed mass, mass accretion rate and formation vary with orientation and distance from the saddle, demonstrating the indeed assembly bias is influenced by the large-scale tides. Our results are in agreement with recent observations of [3] and provide a new framework to compute the effects of the large scale anisotropy on halo populations.

The nature of the most abundant components of the Universe, dark energy and dark matter, is still to be uncovered. What can be learned considering the 21cm radiation observed with the intensity mapping (IM) technique? This poster shortly present the 21cm IM, i.e. what kind of signal it is, then shows how it varies when competitive and realistic dark energy and dark matter scenarios are at play. The focus is on how distinctive and detectable these effects are, presenting forecasts for the bounds that a radio telescope like the Square Kilometre Array (SKA) would be able to uniquely set.

This work is based on [1]. In the mean field limit, isolated gravitational systems often evolve towards a steady state through a violent relaxation phase. One question is to understand the nature of this relaxation phase, in particular the role of radial instabilities in the establishment/destruction of the steady profile. Here, through a detailed phase-space analysis based both on a spherical Vlasov solver, a shell code and a N-body code, we revisit the evolution of collisionless self-gravitating spherical systems with initial power-law density profiles \( \rho(r) \propto r^n \), \( 0 \le n \le -1.5 \), and Gaussian velocity dispersion. Two sub-classes of models are considered, with initial virial ratios \( \eta = 0.5 \) (“warm”) and \( \eta = 0.1 \) (“cool”). Thanks to the numerical techniques used and the high resolution of the simulations, our numerical analyses are able, for the first time, to show the clear separation between two or three well known dynamical phases: (i) the establishment of a spherical quasi-steady state through a violent relaxation phase during which the phase-space density displays a smooth spiral structure presenting a morphology consistent with predictions from self-similar dynamics, (ii) a quasi-steady state phase during which radial instabilities can take place at small scales and destroy the spiral structure but do not change quantitatively the properties of the phase-space distribution at the coarse grained level and (iii) relaxation to non spherical state due to radial orbit instabilities for \( n \le -1 \) in the cool case.

Hybrid metric-Palatini gravity theory is a newly proposed theory, whose action depend linearly on the metric scalar curvature and nonlinearly on the arbitrary function of Palatine scalar curvature. The fascinating property of this theory is to maintain all the positive results of GR at solar system and compact object scale as well as to add explanation to the recent cosmological observations. Looking at the recent popularity of this theory, we study the issue of cosmic inflation in this theory. For this purpose, we calculate the slow-roll parameter, scalar-to-tensor ratio and spectral index and then investigate their behavior graphically to check the growth of the universe. We also check our findings with the recent observational data

The main subject of this work is the study of a general linear boundary value problems with Drazin and right Drazin (resp. left Drazin) invertible operators and corresponding to initial boundary operators. The obtained results are then employed to solve Schrodinger equation equation.

Massive neutrinos uniquely affect cosmic voids. We explore their impact on void clustering using both the DEMNUni and MassiveNuS simulations. For voids, neutrino effects depend on the observational tracers. As neutrino mass increases, the number of small voids traced by cold dark matter particles increases and the number of large voids decreases. Surprisingly, when massive halos are used as tracers, we see the opposite effect. How neutrinos impact the scale at which voids cluster and the void correlation is similarly sensitive to the tracers. This scale dependent bias is not due to simulation volume or halo density. The interplay of these signatures in the void abundance and clustering leaves a distinct fingerprint that could be detected with observations and potentially help break degeneracies between different cosmological parameters. This paper paves the way to exploit cosmic voids in future surveys to constrain the mass of neutrinos.

The Bayesian Estimation Applied to Multiple Species (BEAMS; [1]) framework employs probabilistic supernova (SN) classifications to estimate the Hubble parameter that quantifies the relationship between distance and redshift over cosmic time. However, it requires knowledge of the host galaxy spectroscopic redshifts, limiting its use in upcoming missions such as that of the Large Synoptic Survey Telescope (LSST) for which follow-up spectroscopy will be infeasible. Photometric redshifts (photo-\( z \)) point estimates suffer from significant bias, scatter, and outlier effects, making them unsuitable for precision SN cosmology. Photo-\( z \) probability density functions (PDFs) appropriately encapsulate the nontrivial uncertainties, but there are few mathematically motivated methodologies that make use of the information. We build on one such mathematically motivated approach, the Cosmological Hierarchical Inference with Probabilistic Photometric Redshfts (CHIPPR; [2]) probabilistic graphical model and present Supernova Cosmology Inference with Probabilistic Photometric Redshifts (SCIPPR), a Bayesian hierarchical model that naturally melds the BEAMS and CHIPPR approaches to use both posterior PDFs of SN type, redshift, and distance modulus based on photometric lightcurve fits and posterior PDFs of redshift based on host galaxy photometry. By combining probabilistic data products in a fully self-consistent way, we infer a posterior PDF over the cosmological parameters in the absence of spectroscopic observations.

We study the cosmological consequences of codecaying dark matter?a recently proposed mechanism for depleting the density of dark matter through the decay of nearly degenerate particles. A generic prediction of this framework is an early dark matter dominated phase in the history of the Universe, that results in the enhanced growth of dark matter perturbations on small scales. We compute the duration of the early matter dominated phase and show that the perturbations are robust against washout from free streaming. The enhanced small-scale structure is expected to survive today in the form of compact microhalos and can lead to significant boost factors for indirect-detection experiments, such as FERMI, where dark matter would appear as point sources [1]. Following [2], we also comment on the likelihood of primordial black hole formation.

The basic tenet of the present work is the assumption of the lack of external and fixed time in the Universe. This assumption is best embodied by general relativity, which replaces the fixed space-time structure with the gravitational field, which is subject to dynamics. The lack of time does not imply the lack of evolution but rather brings to the forefront the role of internal clocks which are some largely arbitrary internal degrees of freedom with respect to which the evolution of timeless systems can be described. We take this idea seriously and try to understand what it implies for quantum mechanics when the fixed external time is replaced by an arbitrary internal clock.

Inferring the distribution of dark matter from the distribution of tracers such as galaxies and dark matter halos is one of the most important inference problem in current cosmology. It has far reaching consequences in accurate distance measurements using Baryon acoustic oscillations(BAOs), understanding the nature of dark matter, constraining modification of gravity on large-scales. However, this is a difficult task owing to our incomplete knowledge of structure formation on non-linear scales and biased formation of dark matter halos. However, with the advent of large-scale computing, clever algorithms and better understanding of structure formation, it is now possible to do a full forward-modelled Bayesian reconstruction of the large-scale structures. This has important advantages in that correlation of different measurement errors in inference are automatically incorporated in the analysis. Also, observational effects can be easily incorporated into the analytically modelled structure formation process. We are currently working to enhance the performance of these methods by using novel understanding of the biased formation of dark matter halos.

With the advent of powerful telescopes such as the Square Kilometre Array (SKA), the Large Synoptic Survey Telescope (LSST) and the Laser Interferometer Gravitational-Wave Observatory (LIGO), we are entering a golden era of multimessenger transient astronomy. In order to cope with the dramatic increase in data volume, as well as successfully prioritise spectroscopic follow-up resources, we propose a new machine learning approach for the classification of radio transients. In this talk I will outline the algorithm's three main steps: (1) augmentation and interpolation of the data using Gaussian processes; (2) feature extraction using a wavelet decomposition; (3) classification with the popular machine learning algorithm random forests. I will present an application of our algorithm to existing radio transient data, illustrating its ability to accurately classify most radio transients after just 8 hours, and how performance is expected to increase as more training data is acquired. Finally, I will present a general approach for including multimessenger data for general transient classification, and demonstrate its effectiveness by showing the impact of incorporating a single optical data point into the analysis, which significantly reduces confusion.