Beecroft Institute for Particle Astrophysics and Cosmology

Abstract:

Wide-area imaging surveys are one of the key ways of advancing our understanding of cosmology, galaxy formation physics, and the large-scale structure of the Universe in the coming years. These surveys typically require calculating redshifts for huge numbers (hundreds of millions to billions) of galaxies – almost all of which must be derived from photometry rather than spectroscopy. In this paper, we investigate how using statistical models to understand the populations that make up the colour–magnitude distribution of galaxies can be combined with machine learning photometric redshift codes to improve redshift estimates. In particular, we combine the use of Gaussian mixture models with the high-performing machine-learning photo-z algorithm GPz and show that modelling and accounting for the different colour–magnitude distributions of training and test data separately can give improved redshift estimates, reduce the bias on estimates by up to a half, and speed up the run-time of the algorithm. These methods are illustrated using data from deep optical and near-infrared data in two separate deep fields, where training and test data of different colour–magnitude distributions are constructed from the galaxies with known spectroscopic redshifts, derived from several heterogeneous surveys.

Abstract:

ABSTRACT We study how star formation is regulated in low-mass field dwarf galaxies ($10^5 \le M_{\star } \le 10^6 \, \mbox{M}_\mathrm{\odot }$), using cosmological high-resolution ($3 \, \mathrm{pc}$) hydrodynamical simulations. Cosmic reionization quenches star formation in all our simulated dwarfs, but three galaxies with final dynamical masses of $3 \times 10^{9} \, \mbox{M}_\mathrm{\odot }$ are subsequently able to replenish their interstellar medium by slowly accreting gas. Two of these galaxies reignite and sustain star formation until the present day at an average rate of $10^{-5} \, \mbox{M}_\mathrm{\odot } \, \text{yr}^{-1}$, highly reminiscent of observed low-mass star-forming dwarf irregulars such as Leo T. The resumption of star formation is delayed by several billion years due to residual feedback from stellar winds and Type Ia supernovae; even at z = 0, the third galaxy remains in a temporary equilibrium with a large gas content but without any ongoing star formation. Using the ‘genetic modification’ approach, we create an alternative mass growth history for this gas-rich quiescent dwarf and show how a small $(0.2\, \mathrm{dex})$ increase in dynamical mass can overcome residual stellar feedback, reigniting star formation. The interaction between feedback and mass build-up produces a diversity in the stellar ages and gas content of low-mass dwarfs, which will be probed by combining next-generation H i and imaging surveys.

Abstract:

© 2020 IOP Publishing Ltd and Sissa Medialab. One of the most important checks for systematic errors in CMB studies is the cross correlation of maps made by independent experiments. In this paper we report on the cross correlation between maps from the Atacama B-mode Search (ABS) and Atacama Cosmology Telescope (ACT) experiments in both temperature and polarization. These completely different measurements have a clear correlation with each other and with the Planck satellite in both the EE and TE spectra at ℓ<400 over the roughly 0110 deg2 common to all three. The TB, EB, and BB cross spectra are consistent with noise. Exploiting such cross-correlations will be important for future experiments operating in Chile that aim to probe the 30<ℓ<8,000 range.

Abstract:

The Heliospheric Imagers on board National Aeronautics and Space Administration (NASA)'s twin STEREO spacecraft show that coronal mass ejections (CMEs) can be visually complex structures. To explore this complexity, we created a citizen science project with the U.K. Science Museum, in which participants were shown pairs of CME images and asked to decide which image in each pair appeared the most “complicated.” A Bradley‐Terry model was then applied to these data to rank the CMEs by their “complicatedness,” or “visual complexity.” This complexity ranking revealed that the annual average visual complexity values follow the solar activity cycle, with a higher level of complexity being observed at the peak of the cycle. The average complexity of CMEs observed by STEREO‐A was also found to be significantly higher than those observed by STEREO‐B. Visual complexity was found to be associated with CME size and brightness, but our results suggest that complexity may be influenced by the scale‐sizes of structure in the CMEs.