Physics

In this work we analyse results obtained when considering individuals as mobile agents, which can interact for a period of time that depends on the agent dinamics. In this way, as the probability of disease transmission depends on this contact time, the spatial dynamics will strongly influence the disease spreading. Surprisingly, the size of the endemic population will have three different regimes depending on the speed: elimination of the disease if it is very low, inversely proportional to the speed for intermediate values, and a final case that depends on the contact. Moreover, we recently found that these last two regimes also depend heavily on the type of the dynamics (ballistic or diffusive). The same goes for the critical speed at which the disease disappears.

Artificial Neural Networks (ANNs) have been demonstrated to be an excellent method for dealing with various kind of tasks, such as image classification and natural language processing. These ANNs can also be used for regression since they are considered to be an universal function approximator, thanks to it's great capacity of dealing with non linear tasks. On the other hand, Floris Takens have shown to be possible to access and reconstruct the underlying dynamics of a system in the space state, starting from a single measured time series. In other words, Takens' theorem says it is possible to get information of a higher dimensional system from vectors build from a one-dimensional time series. This enables better time-series representations that can be fed into the ANNs. In this work, I will apply Echo State Networks (ESNs), one among the types of ANNs, to make forecasts of the dynamics of physical systems, such as Lorenz, Fokker-Planck and Vlasov Equations. I also pretend to apply these ESNs, together with Takens Vectors, into real time series, such as epidemics spread and financial series.

Beginning with the landmark gravitational wave detections by LIGO/Virgo, we have been gaining an unprecedented view of the dynamics of strongly curved spacetime originally predicted by Einstein. Enabling and motivated by these observations, there has been rapid development in the theoretical and computational tools we use to understand and interpret the nature of gravity and matter in this regime. This is essential to fulfilling the promise of gravitational wave astronomy to not only be a new window on the astrophysics of black hole and neutron star mergers, but also to be a unique probe of fundamental physics. I will illustrate this, highlighting the example of how black holes can be used to search for new particles. The same theoretical tools also allow us to push Einstein's theory to the extreme in search of the limits of its predictability, for example testing cosmic censorship, the idea that the breakdown in Einstein's equations associated with unhalted collapse is always hidden behind a black hole horizon. I will describe some recent work resolving a long-standing putative counterexample to this conjecture.

High resolution hydrodynamic simulations of galaxy formation are powerful tools, which can provide important information on the properties of the dark matter halo. Combined with the information obtained from the second data release of the Gaia satellite, simulations can significantly improve our understanding of the dark matter distribution in the Solar neighborhood. Determining the local dark matter velocity distribution is crucial for the correct analysis and interpretation of data from dark matter direct detection experiments. I will discuss the local dark matter distribution of Milky Way-like galaxies extracted from state-of-the-art hydrodynamic simulations, and present an analysis of direct detection data using this distribution. I will also discuss the properties of the dark matter component of the radially anisotropic stellar population recently discovered in the Gaia data, using the Auriga simulations. In particular, I will present the local dark matter density and velocity distributions of the simulated Milky Way-like galaxies with and without the anisotropic substructure, and discuss their implications for dark matter direct detection.

A multitude of protein bodies have recently been found to be non-membrane-bound liquid droplets that phase-separate spontaneously from the cellular environment, both in the cytoplasm and in the nucleus. For this reason, they are often compared to oil droplets in water. In this talk, I will challenge this analogy at two different levels. First, protein liquids are not quite like oil - they are generally caused by the specific attractive interactions between complementary protein domains, which result in surprising phase behavior and allows cells to fine-tune their dynamical properties. Second, the cellular environment, and in particular the nucleoplasm, is not quite like water - it is crowded, visco-elastic, and highly complex. Using simple theoretical arguments, I will show that the interplay between the elasticity of chromatin and the surface tension of droplets controls phase separation - and I will predict that this could result in exotic, yet-unobserved new phases of matter.

A key feature of living systems is their ability to consume chemical energy to actively generate the forces they use to move and change shape. These forces are typically generated at the nanometer scale by motor proteins, and transmitted to larger scales by networks of fibers. I will first discuss the transmission of these active forces through the cell cytoskeleton and the extracellular matrix. I will show how the nonlinear mechanical properties of these biopolymers crucially affect force transmission by selecting and amplifying contractile stresses. We experimentally confirm these results using a novel stress measurement technique, Nonlinear Stress Inference Microscopy. In a second part, I will discuss how active forces emerge from Brownian noise at the sub-micron scale. From an observer's point of view, there is a fundamental bound to the amount of information that can be recovered by monitoring the dynamics of such systems. I will propose a practical method, Stochastic Force Inference, that efficiently utilizes this limited information to reconstruct force fields and infer dissipative currents in Brownian systems.

The standard model of cosmology is built upon on a series of propositions on how the early, intermediate, and late epochs of the Universe behave. In this seminar, I am going to show how Principle Components can fully parameterize the behavior of dark energy on smooth dark energy, inflaton on single-field inflation, and optical depth on reionization. If the current paradigms of inflation, reionization, and cosmic acceleration are falsified, then the standard model will demand radically new ideas to describe the data. The fourth-generation surveys - including Simons, CLASS, LSST, WFIRST, DESI - can, therefore, be employed to challenge some of the core hypotheses of the standard model.  

The standard model of cosmology is built upon on a series of propositions on how the early, intermediate, and late epochs of the Universe behave. In particular, it predicts that dark energy and dark matter currently pervades the cosmos. Understanding the properties of the dark sector is plausibly the biggest challenge in theoretical physics. There is, however, a broad assumption in cosmology that the Universe on its earlier stages is fully understood and that discrepancies between the standard model of cosmology and current data are suggestive of distinct dark energy properties. Uncertainties on the validity of this hypothesis are not usually taken into account when forecasting survey capabilities, even though our investigations might be obfuscated if the intermediate and early Universe did behave abnormally. In this colloquium, I propose a program to investigate dark energy and earlier aspects of our Universe simultaneously, through space missions in the 2020s in combination with ground-based observatories. This program will help guide the strategy for the future WFIRST supernovae and weak lensing surveys. My investigations on how properties of the early and intermediate Universe affect inferences on dark energy (and vice-versa) will also support community understanding of how future missions can be employed to test some of the core hypotheses of the standard model of cosmology.