arXiv subjects

Kate Storey-Fisher

Publications and source records attributed to Kate Storey-Fisher.

23 records · Page 2Linked to original sources

arXiv2021

The Low Redshift Circumgalactic Medium in Simba

We examine the properties of the low-redshift circumgalactic medium (CGM) around star-forming and quenched galaxies in the Simba cosmological hydrodynamic simulations, focusing on comparing HI and metal line absorption to observations from the COS-Halos and COS-Dwarfs surveys. Halo baryon fractions are generally $\lesssim 50\%$ of the cosmic fraction due to stellar feedback at low masses, and jet-mode AGN feedback at high masses. Baryons and metals in the CGM of quenched galaxies are $\gtrsim 90\%$ hot gas, while the CGM of star-forming galaxies is more multi-phase. Hot CGM gas has low metallicity, while warm and cool CGM gas have metallicity close to that of galactic gas. Equivalent widths, covering fractions and total path absorption of HI and selected metal lines (MgII, SiIII, CIV and OVI) around a matched sample of Simba star-forming galaxies are mostly consistent with COS-Halos and COS-Dwarfs observations to $\lesssim 0.4$~dex, depending on ion and assumed ionising background. Around matched quenched galaxies, absorption in all ions is lower, with HI absorption significantly under-predicted. Metal-line absorption is sensitive to choice of photo-ionising background; assuming recent backgrounds, Simba matches OVI but under-predicts low ions, while an older background matches low ions but under-predicts OVI. Simba reproduces the observed dichotomy of OVI absorption around star forming and quenched galaxies. CGM metals primarily come from stellar feedback, while jet-mode AGN feedback reduces absorption particularly for lower ions.

astro-ph.GA↗

arXiv2021

Two-point statistics without bins: A continuous-function generalization of the correlation function estimator for large-scale structure

The two-point correlation function (2pcf) is the key statistic in structure formation; it measures the clustering of galaxies or other density field tracers. Estimators of the 2pcf, including the standard Landy-Szalay (LS) estimator, evaluate the 2pcf in hard-edged separation bins, which is scientifically inappropriate and results in a poor trade-off between bias and variance. We present a new 2pcf estimator, the Continuous-Function Estimator, which generalizes LS to a continuous representation and obviates binning in separation or any other pair property. Our estimator, inspired by the mathematics of least-squares fitting, replaces binned pair counts with projections onto basis functions; it outputs the best linear combination of basis functions to describe the 2pcf. The choice of basis can take into account the expected form of the 2pcf, as well as its dependence on pair properties other than separation. We show that the Continuous-Function Estimator with a cubic-spline basis better represents the shape of the 2pcf compared to LS. We also estimate directly the baryon acoustic scale, using a small number of physically-motivated basis functions. Critically, this leads to a reduction in the number of mock catalogs required for covariance estimation, which is currently the limiting step in many 2pcf analyses. We discuss further applications of the Continuous-Function Estimator, including determination of the dependence of clustering on galaxy properties and searches for potential inhomogeneities or anisotropies in large-scale structure.

astro-ph.CO↗

arXiv2020

Anomaly Detection in Astronomical Images with Generative Adversarial Networks

We present an anomaly detection method using Wasserstein generative adversarial networks (WGANs) on optical galaxy images from the wide-field survey conducted with the Hyper Suprime-Cam (HSC) on the Subaru Telescope in Hawai'i. The WGAN is trained on the entire sample, and learns to generate realistic HSC-like images that follow the distribution of the training data. We identify images which are less well-represented in the generator's latent space, and which the discriminator flags as less realistic; these are thus anomalous with respect to the rest of the data. We propose a new approach to characterize these anomalies based on a convolutional autoencoder (CAE) to reduce the dimensionality of the residual differences between the real and WGAN-reconstructed images. We construct a subsample of ~9,000 highly anomalous images from our nearly million object sample, and further identify interesting anomalies within these; these include galaxy mergers, tidal features, and extreme star-forming galaxies. The proposed approach could boost unsupervised discovery in the era of big data astrophysics.

astro-ph.GA↗

arXiv2020

Detecting outliers in astronomical images with deep generative networks

With the advent of future big-data surveys, automated tools for unsupervised discovery are becoming ever more necessary. In this work, we explore the ability of deep generative networks for detecting outliers in astronomical imaging datasets. The main advantage of such generative models is that they are able to learn complex representations directly from the pixel space. Therefore, these methods enable us to look for subtle morphological deviations which are typically missed by more traditional moment-based approaches. We use a generative model to learn a representation of expected data defined by the training set and then look for deviations from the learned representation by looking for the best reconstruction of a given object. In this first proof-of-concept work, we apply our method to two different test cases. We first show that from a set of simulated galaxies, we are able to detect $\sim90\%$ of merging galaxies if we train our network only with a sample of isolated ones. We then explore how the presented approach can be used to compare observations and hydrodynamic simulations by identifying observed galaxies not well represented in the models.

astro-ph.GA↗

arXiv2019

Astrobites as a Community-led Model for Education, Science Communication, and Accessibility in Astrophysics

Support for early career astronomers who are just beginning to explore astronomy research is imperative to increase retention of diverse practitioners in the field. Since 2010, Astrobites has played an instrumental role in engaging members of the community -- particularly undergraduate and graduate students -- in research. In this white paper, the Astrobites collaboration outlines our multi-faceted online education platform that both eases the transition into astronomy research and promotes inclusive professional development opportunities. We additionally offer recommendations for how the astronomy community can reduce barriers to entry to astronomy research in the coming decade.

astro-ph.IM↗