Computer vision and optimization methods applied to the measurements of in-plane deformations

fi=vertaisarvioitu|en=peerReviewed

Gravitational Wave Tests of General Relativity with the Parameterized Post-Einsteinian Framework

Gravitational wave astronomy has tremendous potential for studying extreme astrophysical phenomena and exploring fundamental physics. The waves produced by binary black hole mergers will provide a pristine environment in which to study strong field, dynamical gravity. Extracting detailed information about these systems requires accurate theoretical models of the gravitational wave signals. If gravity is not described by General Relativity, analyses that are based on waveforms derived from Einstein's field equations could result in parameter biases and a loss of detection efficiency. A new class of "parameterized post-Einsteinian" (ppE) waveforms has been proposed to cover this eventuality. Here we apply the ppE approach to simulated data from a network of advanced ground based interferometers (aLIGO/aVirgo) and from a future spaced based interferometer (LISA). Bayesian inference and model selection are used to investigate parameter biases, and to determine the level at which departures from general relativity can be detected. We find that in some cases the parameter biases from assuming the wrong theory can be severe. We also find that gravitational wave observations will beat the existing bounds on deviations from general relativity derived from the orbital decay of binary pulsars by a large margin across a wide swath of parameter space.Comment: 16 pages, 10 figures. Modified in response to referee comment

Future constraints on halo thermodynamics from combined Sunyaev-Zel'dovich measurements

The improving sensitivity of measurements of the kinetic Sunyaev-Zel'dovich (SZ) effect opens a new window into the thermodynamic properties of the baryons in halos. We propose a methodology to constrain these thermodynamic properties by combining the kinetic SZ, which is an unbiased probe of the free electron density, and the thermal SZ, which probes their thermal pressure. We forecast that our method constrains the average thermodynamic processes that govern the energetics of galaxy evolution like energetic feedback across all redshift ranges where viable halos sample are available. Current Stage-3 cosmic microwave background (CMB) experiments like AdvACT and SPT-3G can measure the kSZ and tSZ to greater than 100$\sigma$ if combined with a DESI-like spectroscopic survey. Such measurements translate into percent-level constraints on the baryonic density and pressure profiles and on the feedback and non-thermal pressure support parameters for a given ICM model. This in turn will provide critical thermodynamic tests for sub-grid models of feedback in cosmological simulations of galaxy formation. The high fidelity measurements promised by the next generation CMB experiment, CMB-S4, allow one to further sub-divide these constraints beyond redshift into other classifications, like stellar mass or galaxy type.Comment: 11 pages, 3 figures, Accepted to JCA

Observations of Cool-Star Magnetic Fields

Cool stars like the Sun harbor convection zones capable of producing substantial surface magnetic fields leading to stellar magnetic activity. The influence of stellar parameters like rotation, radius, and age on cool-star magnetism, and the importance of the shear layer between a radiative core and the convective envelope for the generation of magnetic fields are keys for our understanding of low-mass stellar dynamos, the solar dynamo, and also for other large-scale and planetary dynamos. Our observational picture of cool-star magnetic fields has improved tremendously over the last years. Sophisticated methods were developed to search for the subtle effects of magnetism, which are difficult to detect particularly in cool stars. With an emphasis on the assumptions and capabilities of modern methods used to measure magnetism in cool stars, I review the different techniques available for magnetic field measurements. I collect the analyses on cool-star magnetic fields and try to compare results from different methods, and I review empirical evidence that led to our current picture of magnetic fields and their generation in cool stars and brown dwarfs.Comment: Published version at http://www.livingreviews.org/lrsp-2012-

The pre-launch Planck Sky Model: a model of sky emission at submillimetre to centimetre wavelengths

We present the Planck Sky Model (PSM), a parametric model for the generation of all-sky, few arcminute resolution maps of sky emission at submillimetre to centimetre wavelengths, in both intensity and polarisation. Several options are implemented to model the cosmic microwave background, Galactic diffuse emission (synchrotron, free-free, thermal and spinning dust, CO lines), Galactic H-II regions, extragalactic radio sources, dusty galaxies, and thermal and kinetic Sunyaev-Zeldovich signals from clusters of galaxies. Each component is simulated by means of educated interpolations/extrapolations of data sets available at the time of the launch of the Planck mission, complemented by state-of-the-art models of the emission. Distinctive features of the simulations are: spatially varying spectral properties of synchrotron and dust; different spectral parameters for each point source; modeling of the clustering properties of extragalactic sources and of the power spectrum of fluctuations in the cosmic infrared background. The PSM enables the production of random realizations of the sky emission, constrained to match observational data within their uncertainties, and is implemented in a software package that is regularly updated with incoming information from observations. The model is expected to serve as a useful tool for optimizing planned microwave and sub-millimetre surveys and to test data processing and analysis pipelines. It is, in particular, used for the development and validation of data analysis pipelines within the planck collaboration. A version of the software that can be used for simulating the observations for a variety of experiments is made available on a dedicated website.Comment: 35 pages, 31 figure

From Simple to Complex: A Progressive Framework for Document-level Informative Argument Extraction

Document-level Event Argument Extraction (EAE) requires the model to extract arguments of multiple events from a single document. Considering the underlying dependencies between these events, recent efforts leverage the idea of "memory", where the results of already predicted events are cached and can be retrieved to help the prediction of upcoming events. These methods extract events according to their appearance order in the document, however, the event that appears in the first sentence does not mean that it is the easiest to extract. Existing methods might introduce noise to the extraction of upcoming events if they rely on an incorrect prediction of previous events. In order to provide more reliable memory, we propose a simple-to-complex progressive framework for document-level EAE. Specifically, we first calculate the difficulty of each event and then, we conduct the extraction following a simple-to-complex order. In this way, the memory will store the most certain results, and the model could use these reliable sources to help the prediction of more difficult events. Experiments on WikiEvents show that our model outperforms SOTA by 1.4% in F1, indicating the proposed simple-to-complex framework is useful in the EAE task.Comment: Accepted to the Findings of EMNLP 2023 (Long Paper

Invariant template matching in systems with spatiotemporal coding: a vote for instability

We consider the design of a pattern recognition that matches templates to images, both of which are spatially sampled and encoded as temporal sequences. The image is subject to a combination of various perturbations. These include ones that can be modeled as parameterized uncertainties such as image blur, luminance, translation, and rotation as well as unmodeled ones. Biological and neural systems require that these perturbations be processed through a minimal number of channels by simple adaptation mechanisms. We found that the most suitable mathematical framework to meet this requirement is that of weakly attracting sets. This framework provides us with a normative and unifying solution to the pattern recognition problem. We analyze the consequences of its explicit implementation in neural systems. Several properties inherent to the systems designed in accordance with our normative mathematical argument coincide with known empirical facts. This is illustrated in mental rotation, visual search and blur/intensity adaptation. We demonstrate how our results can be applied to a range of practical problems in template matching and pattern recognition.Comment: 52 pages, 12 figure