Beecroft Institute for Particle Astrophysics and Cosmology

Abstract:

We investigate the dependence of the mass function of dark-matter haloes on their environment within the cosmic web of large-scale structure. A dependence of the halo mass function on large-scale mean density is a standard element of cosmological theory, allowing mass-dependent biasing to be understood via the peak-background split. On the assumption of a Gaussian density field, this analysis can be extended to ask how the mass function depends on the geometrical environment: clusters, filaments, sheets and voids, as classified via the tidal tensor (the Hessian matrix of the gravitational potential). In linear theory, the problem can be solved exactly, and the result is attractively simple: the conditional mass function has no explicit dependence on the local tidal field, and is a function only of the local density on the filtering scale used to define the tidal tensor. There is nevertheless a strong implicit predicted dependence on geometrical environment, because the local density couples statistically to the derivatives of the potential. We compute the predictions of this model and study the limits of their validity by comparing them to results deduced empirically from N-body simulations. We have verified that, to a good approximation, the abundance of haloes in different environments depends only on their densities, and not on their tidal structure. In this sense we find relative differences between halo abundances in different environments with the same density which are smaller than ∼13 per cent. Furthermore, for sufficiently large filtering scales, the agreement with the theoretical prediction is good, although there are important deviations from the Gaussian prediction at small, non-linear scales. We discuss how to obtain improved predictions in this regime, using the ‘effective-universe’ approach.

Abstract:

Ultralight axions (ULAs) with masses in the range 10-33 eV ma 10-18 eV (motivated by string theory) might contribute to the dark-matter or dark-energy density of the Universe. ULAs would suppress the growth of structure on small scales and change the shape of the cosmic microwave background (CMB) anisotropy power spectra. In this work, we compute cosmological observables over the full ULA mass range and then use them to search for evidence of ULAs using CMB temperature data from the Planck satellite, large-scale CMB polarization data from Wilkinson Microwave Anisotropy Probe (WMAP), smallerscale CMB experiments, as well as the WiggleZ galaxy-redshift survey. In the mass range 10-32 eV ma 10-25.5 eV, the ULA relic-density must obey the constraint ah2 0.006 at 95%-confidence. For ma & 10-24 eV, ULAs are indistinguishable from standard cold dark matter on the length scales probed while for ma . 10-32 eV, ULAs are allowed to compose a significant fraction of the dark energy. If primordial gravitational waves are detected, limits to the primordial isocurvature fraction will put severe constraints on ULA dark matter. In the future, weak-lensing measurements of the CMB will yield even more powerful probes of the ULA hypothesis.

Abstract:

Although current literature highlights a wide variety of potential citizen science project outcomes, no prior studies have systematically assessed performance against a comprehensive set of criteria. The study reported here is the first to propose a novel framework for assessing citizen science projects against multiple dimensions of success. The authors apply this framework to a sample of projects that form part of the online Zooniverse platform and position these projects against a success matrix that measures both contribution to science and public engagement levels relative to other projects in the sample. Their results indicate that better-performing projects tend to be those that are more established, as well as those in the area of astronomy. Implications for citizen science practitioners include the need to consider the impact of core competencies on project performance, as well as the importance of relationships between the central organization and science teams.

Abstract:

Processing our increasingly large datasets (e.g., image, audio, video, multidimensional data, etc. poses a bottleneck for producing real scientific outcomes. Citizen science-engaging the publi in research-provides a solution, particularly when coupled with machine learning algorithm and sophisticated task allocation and retirement rules. Zooniverse is the most widely used an successful citizen science platform, with almost 1.5 million volunteers worldwide and havin supported over 50 projects across the disciplines (including ecology, archaeology, climate science oncology, physics, astronomy, and the humanities). To date, Zooniverse projects have produce over 100 peer-reviewed publications. Faced with a rapidly growing demand for citizen scienc projects, Zooniverse has launched a new 'Project Builder' interface which allows anyone to buil and maintain their own citizen science project using a set of browser-based tools.