arXiv subjects

Lauren Anderson

Publications and source records attributed to Lauren Anderson.

17 recordsLinked to original sources

arXiv2025

Biology-driven assessment of deep learning super-resolution imaging of the porosity network in dentin

The mechanosensory system of teeth is currently believed to partly rely on Odontoblast cells stimulation by fluid flow through a porosity network extending through dentin. Visualizing the smallest sub-microscopic porosity vessels therefore requires the highest achievable resolution from confocal fluorescence microscopy, the current gold standard. This considerably limits the extent of the field of view to very small sample regions. To overcome this limitation, we tested different deep learning (DL) super-resolution (SR) models to allow faster experimental acquisitions of lower resolution images and restore optimal image quality by post-processing. Three supervised 2D SR models (RCAN, pix2pix, FSRCNN) and one unsupervised (CycleGAN) were applied to a unique set of experimentally paired high- and low-resolution confocal images acquired with different sampling schemes, resulting in a pixel size increase of x2, x4, x8. Model performance was quantified using a broad set of similarity and distribution-based image quality assessment (IQA) metrics, which yielded inconsistent results that mostly contradicted our visual perception. This raises the question of the relevance of such generic metrics to efficiently target the specific structure of dental porosity. To resolve this conflicting information, the generated SR images were segmented taking into account the specific scales and morphology of the porosity network and analysed by comparing connected components. Additionally, the capacity of the SR models to preserve 3D porosity connectivity throughout the confocal image stacks was evaluated using graph analysis. This biology-driven assessment allowed a far better mechanistic interpretation of SR performance, highlighting differences in model sensitivity to weak intensity features and the impact of non-linearity in image generation, which explains the failure of standard IQA metrics.

cs.LG↗

arXiv2022

Improving Binary Millisecond Pulsar Distances with Gaia

Improved distance measurements to millisecond pulsars can enhance pulsar timing array (PTA) sensitivity to gravitational-waves, improve tests of general relativity with binary pulsars, improve constraints on fuzzy dark matter, and more. Here we report the parallax and distance measurements to seven Gaia DR2 objects associated with seven International PTA pulsars: J0437-4715, J1012+5307, J1024-0719, J1732-5049, J1910+1256, J1843-1113, and J1949+3106. By multiplying the posteriors of the PTA-based parallax measurements with the Gaia parallax measurement to the pulsar's companion, we improve the distance measurements from a few percent to a factor of five, and a tentative detection of a binary companion to J1843-1113. We also find an order of magnitude improvement in the parallax measurement to J1949+3106.

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arXiv2022

Mapping Interstellar Dust with Gaussian Processes

Interstellar dust corrupts nearly every stellar observation, and accounting for it is crucial to measuring physical properties of stars. We model the dust distribution as a spatially varying latent field with a Gaussian process (GP) and develop a likelihood model and inference method that scales to millions of astronomical observations. Modeling interstellar dust is complicated by two factors. The first is integrated observations. The data come from a vantage point on Earth and each observation is an integral of the unobserved function along our line of sight, resulting in a complex likelihood and a more difficult inference problem than in classical GP inference. The second complication is scale; stellar catalogs have millions of observations. To address these challenges we develop ziggy, a scalable approach to GP inference with integrated observations based on stochastic variational inference. We study ziggy on synthetic data and the Ananke dataset, a high-fidelity mechanistic model of the Milky Way with millions of stars. ziggy reliably infers the spatial dust map with well-calibrated posterior uncertainties.

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arXiv2021

Hierarchical Inducing Point Gaussian Process for Inter-domain Observations

We examine the general problem of inter-domain Gaussian Processes (GPs): problems where the GP realization and the noisy observations of that realization lie on different domains. When the mapping between those domains is linear, such as integration or differentiation, inference is still closed form. However, many of the scaling and approximation techniques that our community has developed do not apply to this setting. In this work, we introduce the hierarchical inducing point GP (HIP-GP), a scalable inter-domain GP inference method that enables us to improve the approximation accuracy by increasing the number of inducing points to the millions. HIP-GP, which relies on inducing points with grid structure and a stationary kernel assumption, is suitable for low-dimensional problems. In developing HIP-GP, we introduce (1) a fast whitening strategy, and (2) a novel preconditioner for conjugate gradients which can be helpful in general GP settings. Our code is available at https: //github.com/cunningham-lab/hipgp.

cs.LG↗

arXiv2021

Visualization in Astrophysics: Developing New Methods, Discovering Our Universe, and Educating the Earth

We present a state-of-the-art report on visualization in astrophysics. We survey representative papers from both astrophysics and visualization and provide a taxonomy of existing approaches based on data analysis tasks. The approaches are classified based on five categories: data wrangling, data exploration, feature identification, object reconstruction, as well as education and outreach. Our unique contribution is to combine the diverse viewpoints from both astronomers and visualization experts to identify challenges and opportunities for visualization in astrophysics. The main goal is to provide a reference point to bring modern data analysis and visualization techniques to the rich datasets in astrophysics.

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arXiv2020

Exploring the evolution of stellar rotation using Galactic kinematics

The rotational evolution of cool dwarfs is poorly constrained after around 1-2 Gyr due to a lack of precise ages and rotation periods for old main-sequence stars. In this work we use velocity dispersion as an age proxy to reveal the temperature-dependent rotational evolution of low-mass Kepler dwarfs, and demonstrate that kinematic ages could be a useful tool for calibrating gyrochronology in the future. We find that a linear gyrochronology model, calibrated to fit the period-Teff relationship of the Praesepe cluster, does not apply to stars older than around 1 Gyr. Although late-K dwarfs spin more slowly than early-K dwarfs when they are young, at old ages we find that late-K dwarfs rotate at the same rate or faster than early-K dwarfs of the same age. This result agrees qualitatively with semi-empirical models that vary the rate of surface-to-core angular momentum transport as a function of time and mass. It also aligns with recent observations of stars in the NGC 6811 cluster, which indicate that the surface rotation rates of K dwarfs go through an epoch of inhibited evolution. We find that the oldest Kepler stars with measured rotation periods are late-K and early-M dwarfs, indicating that these stars maintain spotted surfaces and stay magnetically active longer than more massive stars. Finally, based on their kinematics, we confirm that many rapidly rotating GKM dwarfs are likely to be synchronized binaries.

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arXiv2019

Astro2020 APC White Paper: Theoretical Astrophysics 2020-2030

The past two decades have seen a tremendous investment in observational facilities that promise to reveal new and unprecedented discoveries about the universe. In comparison, the investment in theoretical work is completely dwarfed, even though theory plays a crucial role in the interpretation of these observations, predicting new types of phenomena, and informing observing strategies. In this white paper, we argue that in order to reach the promised critical breakthroughs in astrophysics over the next decade and well beyond, the national agencies must take a serious approach to investment in theoretical astrophysics research. We discuss the role of theory in shaping our understanding of the universe, and then we provide a multi-level strategy, from the grassroots to the national, to address the current underinvestment in theory relative to observational work.

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arXiv2019

Modeling the Gaia Color-Magnitude Diagram with Bayesian Neural Flows to Constrain Distance Estimates

We demonstrate an algorithm for learning a flexible color-magnitude diagram from noisy parallax and photometry measurements using a normalizing flow, a deep neural network capable of learning an arbitrary multi-dimensional probability distribution. We present a catalog of 640M photometric distance posteriors to nearby stars derived from this data-driven model using Gaia DR2 photometry and parallaxes. Dust estimation and dereddening is done iteratively inside the model and without prior distance information, using the Bayestar map. The signal-to-noise (precision) of distance measurements improves on average by more than 48% over the raw Gaia data, and we also demonstrate how the accuracy of distances have improved over other models, especially in the noisy-parallax regime. Applications are discussed, including significantly improved Milky Way disk separation and substructure detection. We conclude with a discussion of future work, which exploits the normalizing flow architecture to allow us to exactly marginalize over missing photometry, enabling the inclusion of many surveys without losing coverage.

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arXiv2018

Cosmological Hydrodynamic Simulations with Suppressed Variance in the Lyman-$α$ Forest Power Spectrum

We test a method to reduce unwanted sample variance when predicting Lyman-$α$ (ly$α$) forest power spectra from cosmological hydrodynamical simulations. Sample variance arises due to sparse sampling of modes on large scales and propagates to small scales through non-linear gravitational evolution. To tackle this, we generate initial conditions in which the density perturbation amplitudes are {\it fixed} to the ensemble average power spectrum -- and are generated in {\it pairs} with exactly opposite phases. We run $50$ such simulations ($25$ pairs) and compare their performance against $50$ standard simulations by measuring the ly$α$ 1D and 3D power spectra at redshifts $z=2$, 3, and 4. Both ensembles use periodic boxes of $40$ Mpc/h containing $512^3$ particles each of dark matter and gas. As a typical example of improvement, for wavenumbers $k=0.25$ h/Mpc at $z=3$, we find estimates of the 1D and 3D power spectra converge $34$ and $12$ times faster in a paired-fixed ensemble compared with a standard ensemble. We conclude that, by reducing the computational time required to achieve fixed accuracy on predicted power spectra, the method frees up resources for exploration of varying thermal and cosmological parameters -- ultimately allowing the improved precision and accuracy of statistical inference.

astro-ph.CO↗

arXiv2018

Statistical properties of paired fixed fields

The initial conditions of cosmological simulations are commonly drawn from a Gaussian ensemble. The limited number of modes inside a simulation volume gives rise to statistical fluctuations known as \textit{sample variance}, limiting the accuracy of simulation predictions. Fixed fields offer an alternative initialization strategy; they have the same power spectrum as standard Gaussian fields but without intrinsic amplitude scatter at linear order. Paired fixed fields consists of two fixed fields with opposite phases that cancel phase correlations which otherwise induce second-order scatter in the non-linear power spectrum. We study the statistical properties of those fields for 19 different quantities at different redshifts through a large set of 600 N-body and 506 state-of-the-art magneto-hydrodynamic simulations covering a wide range of scales, mass and spatial resolutions. We find that paired fixed simulations do not introduce a bias on any of the examined quantities. We quantify the statistical improvement brought by these simulations, over standard ones, on different power spectra such as matter, halos, CDM, gas, stars, black-holes and magnetic fields, finding that they can reduce their variance by factors as large as $10^6$. We quantify the improvement achieved by fixing and by pairing, showing that sample variance in some quantities can be highly suppressed by pairing after fixing. Paired fixed simulations do not change the scatter in quantities such as the probability distribution function of matter density, or the halo, void or stellar mass functions. We argue that procedures aiming at reducing the sample variance of those quantities are unlikely to work. Our results show that paired fixed simulations do not affect either mean relations or scatter of galaxy properties, and suggest that the information embedded in 1-pt statistics is highly complementary to that in clustering.

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arXiv2017

The Romulus Cosmological Simulations: A Physical Approach to the Formation, Dynamics and Accretion Models of SMBHs

We present a novel implementation of supermassive black hole (SMBH) formation, dynamics, and accretion in the massively parallel tree+SPH code, ChaNGa. This approach improves the modeling of SMBHs in fully cosmological simulations, allowing for a more de- tailed analysis of SMBH-galaxy co-evolution throughout cosmic time. Our scheme includes novel, physically motivated models for SMBH formation, dynamics and sinking timescales within galaxies, and SMBH accretion of rotationally supported gas. The sub-grid parameters that regulate star formation (SF) and feedback from SMBHs and SNe are optimized against a comprehensive set of z = 0 galaxy scaling relations using a novel, multi-dimensional parameter search. We have incorporated our new SMBH implementation and parameter optimization into a new set of high resolution, large-scale cosmological simulations called Romulus. We present initial results from our flagship simulation, Romulus25, showing that our SMBH model results in SF efficiency, SMBH masses, and global SF and SMBH accretion histories at high redshift that are consistent with observations. We discuss the importance of SMBH physics in shaping the evolution of massive galaxies and show how SMBH feedback is much more effective at regulating star formation compared to SNe feedback in this regime. Further, we show how each aspect of our SMBH model impacts this evolution compared to more common approaches. Finally, we present a science application of this scheme studying the properties and time evolution of an example dual AGN system, highlighting how our approach allows simulations to better study galaxy interactions and SMBH mergers in the context of galaxy-BH co-evolution.

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arXiv2017

Improving \textsl{Gaia} parallax precision with a data-driven model of stars

Converting a noisy parallax measurement into a posterior belief over distance requires inference with a prior. Usually this prior represents beliefs about the stellar density distribution of the Milky Way. However, multi-band photometry exists for a large fraction of the \textsl{\small{Gaia}} \textsl{\small{TGAS}} Catalog and is incredibly informative about stellar distances. Here we use \textsl{\small{2MASS}} colors for 1.4 million \textsl{\small{TGAS}} stars to build a noise-deconvolved empirical prior distribution for stars in color--magnitude space. This model contains no knowledge of stellar astrophysics or the Milky Way, but is precise because it accurately generates a large number of noisy parallax measurements under an assumption of stationarity; that is, it is capable of combining the information from many stars. We use the Extreme Deconvolution (\textsl{\small{XD}}) algorithm---an Empirical Bayes approximation to a full hierarchical model of the true parallax and photometry of every star---to construct this prior. The prior is combined with a \textsl{\small{TGAS}} likelihood to infer a precise photometric parallax estimate and uncertainty (and full posterior) for every star. Our parallax estimates are more precise than the \textsl{\small{TGAS}} catalog entries by a median factor of 1.2 (14% are more precise by a factor >2) and are more precise than previous Bayesian distance estimates that use spatial priors. We validate our parallax inferences using members of the Milky Way star cluster M67, which is not visible as a cluster in the \textsl{\small{TGAS}} parallax estimates, but appears as a cluster in our posterior parallax estimates. Our results, including a parallax posterior pdf for each of 1.4 million \textsl{\small{TGAS}} stars, are available in companion electronic tables.

astro-ph.GA↗

arXiv2017

The Little Galaxies that Could (Reionize the Universe): Predicting Faint End Slopes & Escape Fractions at z > 4

The sources that reionized the universe are still unknown, but likely candidates are faint but numerous galaxies. In this paper we present results from running a high resolution, uniform volume simulation, the Vulcan, to predict the number densities of undetectable, faint galaxies and their escape fractions of ionizing radiation, $f_\mathrm{esc}$, during reionization. Our approach combines a high spatial resolution, a realistic treatment of feedback and hydro processes, a strict threshold for minimum number of resolution elements per galaxy, and a converged measurement of $f_\mathrm{esc}$. We calibrate our physical model using a novel approach to create realistic galaxies at z=0, so the simulation is predictive at high redshifts. With this approach we can (1) robustly predict the evolution of the galaxy UV luminosity function at faint magnitudes down to $M_\mathrm{UV}$~-15, two magnitudes fainter than observations, and (2) estimate $f_\mathrm{esc}$ over a large range of galaxy masses based on the detailed stellar and gas distributions in resolved galaxies. We find steep faint end slopes, implying high number densities of faint galaxies, and the dependence of $f_\mathrm{esc}$ on the UV magnitude of a galaxy, given by the power-law: log $f_\mathrm{esc} = (0.51 \pm 0.04)M_\mathrm{UV} + 7.3 \pm 0.8$, with the faint population having $f_\mathrm{esc}$~35%. Convolving the UV luminosity function with $f_\mathrm{esc}$($M_\mathrm{UV}$), we find an ionizing emissivity that is (1) dominated by the faintest galaxies and (2) reionizes the universe at the appropriate rate, consistent with observational constraints of the ionizing emissivity and the optical depth to the decoupling surface tau_es, without the need for additional sources of ionizing radiation.

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arXiv2014

The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey: Baryon Acoustic Oscillations in the Data Release 10 and 11 galaxy samples

We present a one per cent measurement of the cosmic distance scale from the detections of the baryon acoustic oscillations in the clustering of galaxies from the Baryon Oscillation Spectroscopic Survey (BOSS), which is part of the Sloan Digital Sky Survey III (SDSS-III). Our results come from the Data Release 11 (DR11) sample, containing nearly one million galaxies and covering approximately $8\,500$ square degrees and the redshift range $0.2<z<0.7$. We also compare these results with those from the publicly released DR9 and DR10 samples. Assuming a concordance $Λ$CDM cosmological model, the DR11 sample covers a volume of 13\,Gpc${}^3$ and is the largest region of the Universe ever surveyed at this density. We measure the correlation function and power spectrum, including density-field reconstruction of the baryon acoustic oscillation (BAO) feature. The acoustic features are detected at a significance of over $7\,σ$ in both the correlation function and power spectrum. Fitting for the position of the acoustic features measures the distance relative to the sound horizon at the drag epoch, $r_d$, which has a value of $r_{d,{\rm fid}}=149.28\,$Mpc in our fiducial cosmology. We find $D_V=(1264\pm25\,{\rm Mpc})(r_d/r_{d,{\rm fid}})$ at $z=0.32$ and $D_V=(2056\pm20\,{\rm Mpc})(r_d/r_{d,{\rm fid}})$ at $z=0.57$. At 1.0 per cent, this latter measure is the most precise distance constraint ever obtained from a galaxy survey. Separating the clustering along and transverse to the line-of-sight yields measurements at $z=0.57$ of $D_A=(1421\pm20\,{\rm Mpc})(r_d/r_{d,{\rm fid}})$ and $H=(96.8\pm3.4\,{\rm km/s/Mpc})(r_{d,{\rm fid}}/r_d)$. Our measurements of the distance scale are in good agreement with previous BAO measurements and with the predictions from cosmic microwave background data for a spatially flat cold dark matter model with a cosmological constant.

astro-ph.CO↗

arXiv2013

The Multi-Object, Fiber-Fed Spectrographs for SDSS and the Baryon Oscillation Spectroscopic Survey

We present the design and performance of the multi-object fiber spectrographs for the Sloan Digital Sky Survey (SDSS) and their upgrade for the Baryon Oscillation Spectroscopic Survey (BOSS). Originally commissioned in Fall 1999 on the 2.5-m aperture Sloan Telescope at Apache Point Observatory, the spectrographs produced more than 1.5 million spectra for the SDSS and SDSS-II surveys, enabling a wide variety of Galactic and extra-galactic science including the first observation of baryon acoustic oscillations in 2005. The spectrographs were upgraded in 2009 and are currently in use for BOSS, the flagship survey of the third-generation SDSS-III project. BOSS will measure redshifts of 1.35 million massive galaxies to redshift 0.7 and Lyman-alpha absorption of 160,000 high redshift quasars over 10,000 square degrees of sky, making percent level measurements of the absolute cosmic distance scale of the Universe and placing tight constraints on the equation of state of dark energy. The twin multi-object fiber spectrographs utilize a simple optical layout with reflective collimators, gratings, all-refractive cameras, and state-of-the-art CCD detectors to produce hundreds of spectra simultaneously in two channels over a bandpass covering the near ultraviolet to the near infrared, with a resolving power R = λ/FWHM ~ 2000. Building on proven heritage, the spectrographs were upgraded for BOSS with volume-phase holographic gratings and modern CCD detectors, improving the peak throughput by nearly a factor of two, extending the bandpass to cover 360 < λ< 1000 nm, and increasing the number of fibers from 640 to 1000 per exposure. In this paper we describe the original SDSS spectrograph design and the upgrades implemented for BOSS, and document the predicted and measured performances.

astro-ph.IM↗

arXiv2013

The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey: Measuring D_A and H at z=0.57 from the Baryon Acoustic Peak in the Data Release 9 Spectroscopic Galaxy Sample

We present measurements of the angular diameter distance to and Hubble parameter at z=0.57 from the measurement of the baryon acoustic peak in the correlation of galaxies from the Sloan Digital Sky Survey III Baryon Oscillation Spectroscopic Survey. Our analysis is based on a sample from Data Release 9 of 264,283 galaxies over 3275 square degrees in the redshift range 0.43<z<0.70. We use two different methods to provide robust measurement of the acoustic peak position across and along the line of sight in order to measure the cosmological distance scale. We find D_A(0.57) = 1408 +/- 45 Mpc and H(0.57) = 92.9 +/- 7.8 km/s/Mpc for our fiducial value of the sound horizon. These results from the anisotropic fitting are fully consistent with the analysis of the spherically averaged acoustic peak position presented in Anderson et al, 2012. Our distance measurements are a close match to the predictions of the standard cosmological model featuring a cosmological constant and zero spatial curvature.

astro-ph.CO↗

arXiv2012

The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey: Baryon Acoustic Oscillations in the Data Release 9 Spectroscopic Galaxy Sample

We present measurements of galaxy clustering from the Baryon Oscillation Spectroscopic Survey (BOSS), which is part of the Sloan Digital Sky Survey III (SDSS-III). These use the Data Release 9 (DR9) CMASS sample, which contains 264,283 massive galaxies covering 3275 square degrees with an effective redshift z=0.57 and redshift range 0.43 < z < 0.7. Assuming a concordance Lambda-CDM cosmological model, this sample covers an effective volume of 2.2 Gpc^3, and represents the largest sample of the Universe ever surveyed at this density, n = 3 x 10^-4 h^-3 Mpc^3. We measure the angle-averaged galaxy correlation function and power spectrum, including density-field reconstruction of the baryon acoustic oscillation (BAO) feature. The acoustic features are detected at a significance of 5σin both the correlation function and power spectrum. Combining with the SDSS-II Luminous Red Galaxy Sample, the detection significance increases to 6.7σ. Fitting for the position of the acoustic features measures the distance to z=0.57 relative to the sound horizon DV /rs = 13.67 +/- 0.22 at z=0.57. Assuming a fiducial sound horizon of 153.19 Mpc, which matches cosmic microwave background constraints, this corresponds to a distance DV(z=0.57) = 2094 +/- 34 Mpc. At 1.7 per cent, this is the most precise distance constraint ever obtained from a galaxy survey. We place this result alongside previous BAO measurements in a cosmological distance ladder and find excellent agreement with the current supernova measurements. We use these distance measurements to constrain various cosmological models, finding continuing support for a flat Universe with a cosmological constant.

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