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Cora Dvorkin

Publications and source records attributed to Cora Dvorkin.

At least 55 records · Page 3Linked to original sources
arXiv2022

Image segmentation for analyzing galaxy-galaxy strong lensing systems

The goal of this paper is to develop a machine learning model to analyze the main gravitational lens and detect dark substructure (subhalos) within simulated images of strongly lensed galaxies. Using the technique of image segmentation, we turn the task of identifying subhalos into a classification problem, where we label each pixel in an image as coming from the main lens, a subhalo within a binned mass range, or neither. Our network is only trained on images with a single smooth lens and either zero or one subhalo near the Einstein ring. On an independent test set with lenses with large ellipticities, quadrupole and octopole moments, and for source apparent magnitudes between 17-25, the area of the main lens is recovered accurately. On average, only 1.3% of the true area is missed and 1.2% of the true area is added to another part of the lens. In addition, subhalos as light as $10^{8.5}M_{\odot}$ can be detected if they lie in bright pixels along the Einstein ring. Furthermore, the model is able to generalize to new contexts it has not been trained on, such as locating multiple subhalos with varying masses or more than one large smooth lens.

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arXiv2021

Small-scale clumping at recombination and the Hubble tension

Despite the success of the standard $Λ$CDM model of cosmology, recent data improvements have made tensions emerge between low- and high-redshift observables, most importantly in determinations of the Hubble constant $H_0$ and the (rescaled) clustering amplitude $S_8$. The high-redshift data, from the cosmic microwave background (CMB), crucially relies on recombination physics for its interpretation. Here we study how small-scale baryon inhomogeneities (i.e., clumping) can affect recombination and consider whether they can relieve both the $H_0$ and $S_8$ tensions. Such small-scale clumping, which may be caused by primordial magnetic fields or baryon isocurvature below kpc scales, enhances the recombination rate even when averaged over larger scales, shifting recombination to earlier times. We introduce a flexible clumping model, parametrized via three spatial zones with free densities and volume fractions, and use it to study the impact of clumping on CMB observables. We find that increasing $H_0$ decreases both $Ω_m$ and $S_8$, which alleviates the $S_8$ tension. On the other hand, the shift in $Ω_m$ is disfavored by the low-$z$ baryon-acoustic-oscillations measurements. We find that the clumping parameters that can change the CMB sound horizon enough to explain the $H_0$ tension also alter the damping tail, so they are disfavored by current Planck 2018 data. We test how the CMB damping-tail information rules out changes to recombination by first removing $\ell>1000$ multipoles in Planck data, where we find that clumping could resolve the $H_0$ tension. Furthermore, we make predictions for future CMB experiments, as their improved damping-tail precision can better constrain departures from standard recombination. Both the Simons Observatory and CMB-S4 will provide decisive evidence for or against clumping as a resolution to the $H_0$ tension.

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arXiv2021

Precise and Accurate Cosmology with CMBxLSS Power Spectra and Bispectra

With the advent of a new generation of cosmological experiments that will provide high-precision measurements of the cosmic microwave background (CMB) and galaxies in the large-scale structure, it is pertinent to examine the potential of performing a joint analysis of multiple cosmological probes. In this paper, we study the cosmological information content contained in the one-loop power spectra and tree bispectra of galaxies cross-correlated with CMB lensing. We use the FFTLog method to compute angular correlations in spherical harmonic space, applicable for wide angles that can be accessed by forthcoming galaxy surveys. We find that adding the bispectra and cross-correlations with CMB lensing offers a significant improvement in parameter constraints, including those on the total neutrino mass, $M_ν$, and local non-Gaussianity amplitude, $f_{\rm NL}$. In particular, our results suggest that the combination of the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) and CMB-S4 will be able to achieve $σ(M_ν)=42$ meV from galaxy and CMB lensing correlations, and $σ(M_ν)=12$ meV when further combined with the CMB temperature and polarization data, without any prior on the optical depth.

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arXiv2021

Accurately Weighing Neutrinos with Cosmological Surveys

A promising avenue to measure the total, and potentially individual, mass of neutrinos consists of leveraging cosmological datasets, such as the cosmic microwave background and surveys of the large-scale structure of the universe. In order to obtain unbiased estimates of the neutrino mass, however, many effects ought to be included. Here we forecast, via a Markov Chain Monte Carlo likelihood analysis, whether measurements by two galaxy surveys: DESI and {\it Euclid}, when added to the CMB-S4 experiment, are sensitive to two effects that can alter neutrino-mass measurements. The first is the slight difference in the suppression of matter fluctuations that each neutrino-mass hierarchy generates, at fixed total mass. The second is the growth-induced scale-dependent bias (GISDB) of haloes produced by massive neutrinos. We find that near-future surveys can distinguish hierarchies with the same total mass only at the $1σ$ level; thus, while these are poised to deliver a measurement of the sum of neutrino masses, they cannot significantly discern the mass of each individual neutrino in the foreseeable future. We further find that neglecting the GISDB induces up to a $1σ$ overestimation of the total neutrino mass, and we show how to absorb this effect via a redshift-dependent parametrization of the scale-independent bias.

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arXiv2021

Finding eV-scale Light Relics with Cosmological Observables

Cosmological data provide a powerful tool in the search for physics beyond the Standard Model (SM). An interesting target are light relics, new degrees of freedom which decoupled from the SM while relativistic. Nearly massless relics contribute to the radiation energy budget, and are commonly parametrized as variations in the effective number $N_{\rm eff}$ of neutrino species. Additionally, relics with masses greater than $10^{-4}$ eV become non-relativistic before today, and thus behave as matter instead of radiation. This leaves an imprint in the clustering of the large-scale structure of the universe, as light relics have important streaming motions, mirroring the case of massive neutrinos. Here we forecast how well current and upcoming cosmological surveys can probe light massive relics (LiMRs). We consider minimal extensions to the SM by both fermionic and bosonic relic degrees of freedom. By combining current and upcoming cosmic-microwave-background and large-scale-structure surveys, we forecast the significance at which each LiMR, with different masses and temperatures, can be detected. We find that a very large coverage of parameter space will be attainable by upcoming experiments, opening the possibility of exploring uncharted territory for new physics beyond the SM.

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arXiv2021

The Role of Machine Learning in the Next Decade of Cosmology

In recent years, machine learning (ML) methods have remarkably improved how cosmologists can interpret data. The next decade will bring new opportunities for data-driven cosmological discovery, but will also present new challenges for adopting ML methodologies and understanding the results. ML could transform our field, but this transformation will require the astronomy community to both foster and promote interdisciplinary research endeavors.

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arXiv2020

The cosmology of sub-MeV dark matter freeze-in

Dark matter (DM) could be a relic of freeze-in through a light mediator, where the DM is produced by extremely feeble, IR-dominated processes in the thermal Standard Model plasma. In the simplest viable models with the DM mass below the MeV scale, the DM has a small effective electric charge and is born with a nonthermal phase-space distribution. This DM candidate would cause observable departures from standard cosmological evolution. In this work, we combine data from the cosmic microwave background (CMB), Lyman-$α$ forest, quasar lensing, stellar streams, and Milky Way satellite abundances to set a lower limit on freeze-in DM masses up to $\sim 20\,$keV, with the exact constraint depending on whether the DM thermalizes in its own sector. We perform forecasts for the CMB-S4 experiment, the Hydrogen Epoch of Reionization Array, and the Vera Rubin Observatory, finding that freeze-in DM masses up to $\sim 80\,$keV can be explored. These cosmological probes are highly complementary with proposed direct-detection efforts to search for this DM candidate.

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arXiv2020

Flow-Based Likelihoods for Non-Gaussian Inference

We investigate the use of data-driven likelihoods to bypass a key assumption made in many scientific analyses, which is that the true likelihood of the data is Gaussian. In particular, we suggest using the optimization targets of flow-based generative models, a class of models that can capture complex distributions by transforming a simple base distribution through layers of nonlinearities. We call these flow-based likelihoods (FBL). We analyze the accuracy and precision of the reconstructed likelihoods on mock Gaussian data, and show that simply gauging the quality of samples drawn from the trained model is not a sufficient indicator that the true likelihood has been learned. We nevertheless demonstrate that the likelihood can be reconstructed to a precision equal to that of sampling error due to a finite sample size. We then apply FBLs to mock weak lensing convergence power spectra, a cosmological observable that is significantly non-Gaussian (NG). We find that the FBL captures the NG signatures in the data extremely well, while other commonly used data-driven likelihoods, such as Gaussian mixture models and independent component analysis, fail to do so. This suggests that works that have found small posterior shifts in NG data with data-driven likelihoods such as these could be underestimating the impact of non-Gaussianity in parameter constraints. By introducing a suite of tests that can capture different levels of NG in the data, we show that the success or failure of traditional data-driven likelihoods can be tied back to the structure of the NG in the data. Unlike other methods, the flexibility of the FBL makes it successful at tackling different types of NG simultaneously. Because of this, and consequently their likely applicability across datasets and domains, we encourage their use for inference when sufficient mock data are available for training.

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arXiv2020

Quantifying the Line-of-Sight Halo Contribution to the Dark Matter Convergence Power Spectrum from Strong Gravitational Lenses

Galaxy-galaxy strong gravitational lenses have become a popular probe of dark matter (DM) by providing a window into structure formation on the smallest scales. In particular, the convergence power spectrum of subhalos within lensing galaxies has been suggested as a promising observable to study DM. However, the distances involved in strong-lensing systems are vast, and we expect the relevant volume to contain line-of-sight (LOS) halos that are not associated with the main lens. We develop a formalism to calculate the effect of LOS halos as an effective convergence power spectrum. The multi-lens plane equation couples the angular deflections of consecutive lens planes, but by assuming that the perturbations due to the LOS halos are small, we show that they can be projected onto the main-lens plane as effective subhalos. We test our formalism by simulating lensing systems using the full multi-plane lens equation and find excellent agreement. We show how the relative contribution of LOS halos and subhalos depends on the source and lens redshift, as well as the assumed halo and subhalo mass functions. For a fiducial system with fraction of DM halo mass in substructure $f_{\rm sub}=0.4\%$ for subhalo masses $[10^5-10^8]\rm{M}_{\odot}$, the interloper contribution to the power spectrum is at least several times greater than that of subhalos for source redshifts $z_s\gtrsim0.5$. Furthermore, it is likely that for the SLACS and BELLS lenses the interloper contribution dominates: $f_{\rm sub}\gtrsim2\%$ ($4\%$) is needed for subhalos to dominate in SLACS (BELLS), which is higher than current upper bounds on $f_{\rm sub}$ for our mass range. Since the halo mass function is better understood from first principles, the dominance of interlopers in galaxy-galaxy lenses with high-quality imaging can be seen as a significant advantage when translating this observable into a constraint on DM.

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arXiv2020

CMB-S4: Forecasting Constraints on Primordial Gravitational Waves

CMB-S4---the next-generation ground-based cosmic microwave background (CMB) experiment---is set to significantly advance the sensitivity of CMB measurements and enhance our understanding of the origin and evolution of the Universe, from the highest energies at the dawn of time through the growth of structure to the present day. Among the science cases pursued with CMB-S4, the quest for detecting primordial gravitational waves is a central driver of the experimental design. This work details the development of a forecasting framework that includes a power-spectrum-based semi-analytic projection tool, targeted explicitly towards optimizing constraints on the tensor-to-scalar ratio, $r$, in the presence of Galactic foregrounds and gravitational lensing of the CMB. This framework is unique in its direct use of information from the achieved performance of current Stage 2--3 CMB experiments to robustly forecast the science reach of upcoming CMB-polarization endeavors. The methodology allows for rapid iteration over experimental configurations and offers a flexible way to optimize the design of future experiments given a desired scientific goal. To form a closed-loop process, we couple this semi-analytic tool with map-based validation studies, which allow for the injection of additional complexity and verification of our forecasts with several independent analysis methods. We document multiple rounds of forecasts for CMB-S4 using this process and the resulting establishment of the current reference design of the primordial gravitational-wave component of the Stage-4 experiment, optimized to achieve our science goals of detecting primordial gravitational waves for $r > 0.003$ at greater than $5σ$, or, in the absence of a detection, of reaching an upper limit of $r < 0.001$ at $95\%$ CL.

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arXiv2020

Cosmological Angular Trispectra and Non-Gaussian Covariance

Angular cosmological correlators are infamously difficult to compute due to the highly oscillatory nature of the projection integrals. Motivated by recent development on analytic approaches to cosmological perturbation theory, in this paper we present an efficient method for computing cosmological four-point correlations in angular space, generalizing previous works on lower-point functions. This builds on the FFTLog algorithm that approximates the matter power spectrum as a sum over power-law functions, which makes certain momentum integrals analytically solvable. The computational complexity is drastically reduced for correlators in a "separable" form---we define a suitable notion of separability for cosmological trispectra, and derive formulas for angular correlators of different separability classes. As an application of our formalism, we compute the angular galaxy trispectrum at tree level, with and without primordial non-Gaussianity. This includes effects of redshift space distortion and bias parameters up to cubic order. We also compute the non-Gaussian covariance of the angular matter power spectrum due to the connected four-point function, beyond the Limber approximation. We demonstrate that, in contrast to the standard lore, the Limber approximation can fail for the non-Gaussian covariance computation even for large multipoles.

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arXiv2020

A Novel CMB Component Separation Method: Hierarchical Generalized Morphological Component Analysis

We present a novel technique for Cosmic Microwave Background (CMB) foreground subtraction based on the framework of blind source separation. Inspired by previous work incorporating local variation to Generalized Morphological Component Analysis (GMCA), we introduce Hierarchical GMCA (HGMCA), a Bayesian hierarchical graphical model for source separation. We test our method on $N_{\rm side}=256$ simulated sky maps that include dust, synchrotron, free-free and anomalous microwave emission, and show that HGMCA reduces foreground contamination by $25\%$ over GMCA in both the regions included and excluded by the Planck UT78 mask, decreases the error in the measurement of the CMB temperature power spectrum to the $0.02-0.03\%$ level at $\ell>200$ (and $<0.26\%$ for all $\ell$), and reduces correlation to all the foregrounds. We find equivalent or improved performance when compared to state-of-the-art Internal Linear Combination (ILC)-type algorithms on these simulations, suggesting that HGMCA may be a competitive alternative to foreground separation techniques previously applied to observed CMB data. Additionally, we show that our performance does not suffer when we perturb model parameters or alter the CMB realization, which suggests that our algorithm generalizes well beyond our simplified simulations. Our results open a new avenue for constructing CMB maps through Bayesian hierarchical analysis.

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arXiv2020

Probing the Small-Scale Matter Power Spectrum with Large-Scale 21-cm Data

The distribution of matter fluctuations in our universe is key for understanding the nature of dark matter and the physics of the early cosmos. Different observables have been able to map this distribution at large scales, corresponding to wavenumbers $k\lesssim 10$ Mpc$^{-1}$, but smaller scales remain much less constrained. The 21-cm line is a promising tracer of early stellar formation, which took place in small haloes (with masses $M\sim 10^6-10^8M_\odot$), formed out of matter overdensities with wavenumbers as large as $k\approx100$ Mpc$^{-1}$. Here we forecast how well both the 21-cm global signal, and its fluctuations, could probe the matter power spectrum during cosmic dawn ($z=12-25$). We find that the long-wavelength modes (with $k\lesssim40$ Mpc$^{-1}$) are highly degenerate with astrophysical parameters, whereas the modes with $k= (40-80)$ Mpc$^{-1}$ are more readily observable. This is further illustrated in terms of the principal components of the matter power spectrum, which peak at $k\sim 50$ Mpc$^{-1}$ both for a typical experiment measuring the 21-cm global signal and its fluctuations. We find that, imposing broad priors on astrophysical parameters, a global-signal experiment can measure the amplitude of the matter power spectrum integrated over $k= (40-80)$ Mpc$^{-1}$ with a precision of tens of percent. A fluctuation experiment, on the other hand, can constrain the power spectrum to a similar accuracy over both the $k=(40-60)$ Mpc$^{-1}$ and $(60-80)$ Mpc$^{-1}$ ranges even without astrophysical priors. The constraints outlined in this work would be able to test the behavior of dark matter at the smallest scales yet measured, for instance probing warm-dark matter masses up to $m_{\rm WDM}=8$ keV for the global signal and $14$ keV for the 21-cm fluctuations. This could shed light on the nature of dark matter beyond the reach of other cosmic probes.

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arXiv2020

Direct Detection of Dark Matter Substructure in Strong Lens Images with Convolutional Neural Networks

Strong gravitational lensing is a promising way of uncovering the nature of dark matter, by finding perturbations to images that cannot be well accounted for by modeling the lens galaxy without additional structure, be it subhalos (smaller halos within the smooth lens) or line-of-sight (LOS) halos. We present results attempting to infer the presence of substructure from images without requiring an intermediate step in which a smooth model has to be subtracted, using a simple convolutional neural network (CNN). We find that the network is only able to infer the presence of subhalos with $>75\%$ accuracy when they have masses of $\geq 5 \times 10^9$M$_{\odot}$ if they lie within the main lens galaxy. Since less massive foreground LOS halos can have the same effect as higher mass subhalos, the CNN can probe lower masses in the halo mass function. The accuracy does not improve significantly if we add a population of less massive subhalos. With the expectation of experiments such as HST and Euclid yielding thousands of high-quality strong lensing images in the next years, having a way of analyzing images quickly to identify candidates that merit further analysis to determine individual subhalo properties while preventing extensive resources being used for images that would yield null detections could be very useful. By understanding the sensitivity as a function of substructure mass, non-detections could be combined with the information from images with substructure to constrain the cold dark matter scenario, in particular if the sensitivity can be pushed to lower masses.

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arXiv2019

Capturing Non-Gaussianity of the Large-Scale Structure with Weighted Skew-Spectra

The forthcoming generation of wide-field galaxy surveys will probe larger volumes and galaxy densities, thus allowing for a much larger signal-to-noise ratio for higher-order clustering statistics, in particular the galaxy bispectrum. Extracting this information, however, is more challenging than using the power spectrum due to more complex theoretical modeling, as well as significant computational cost of evaluating the bispectrum signal and the error budget. To overcome these challenges, several proxy statistics have been proposed in the literature, which partially or fully capture the information in the bispectrum, while being computationally less expensive than the bispectrum. One such statistics are {\it weighted skew-spectra}, which are cross-spectra of the density field and appropriately weighted quadratic fields. Using Fisher forecasts, we show that the information in these skew-spectra is equivalent to that in the bispectrum for parameters that appear as amplitudes in the bispectrum model, such as galaxy bias parameters or the amplitude of primordial non-Gaussianity. We consider three shapes of the primordial bispectrum: local, equilateral and that due to massive particles with spin two during inflation. To obtain constraints that match those from a measurement of the full bispectrum, we find that it is crucial to account for the full covariance matrix of the skew-spectra.

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arXiv2019

CMB-S4 Decadal Survey APC White Paper

We provide an overview of the science case, instrument configuration and project plan for the next-generation ground-based cosmic microwave background experiment CMB-S4, for consideration by the 2020 Decadal Survey.

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arXiv2019

Probing the Fundamental Nature of Dark Matter with the Large Synoptic Survey Telescope

Astrophysical and cosmological observations currently provide the only robust, empirical measurements of dark matter. Future observations with Large Synoptic Survey Telescope (LSST) will provide necessary guidance for the experimental dark matter program. This white paper represents a community effort to summarize the science case for studying the fundamental physics of dark matter with LSST. We discuss how LSST will inform our understanding of the fundamental properties of dark matter, such as particle mass, self-interaction strength, non-gravitational couplings to the Standard Model, and compact object abundances. Additionally, we discuss the ways that LSST will complement other experiments to strengthen our understanding of the fundamental characteristics of dark matter. More information on the LSST dark matter effort can be found at https://lsstdarkmatter.github.io/ .

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