arXiv subjects

Cora Dvorkin

Publications and source records attributed to Cora Dvorkin.

At least 19 recordsLinked to original sources

arXiv2026

Statistical imprints of wave-like dark matter on multiply-imaged galaxies in strong cluster lenses from JWST

Wave-like dark matter ($ψ$DM) is an elusive dark matter (DM) candidate. The model, often also called fuzzy or ultralight DM, proposes that DM is an extremely light ($m\sim10^{-22}$ eV) boson and thereby has a kpc-scale de Broglie wavelength. Hence, interference of DM gives rise to sub-galactic density fluctuations that can be studied with strong gravitational lensing. In this paper, we use the residual power spectrum, $\mathrm{P}_δ(k)$, as a probe of $ψ$DM, which quantifies deviations from smooth lensing predictions, measured from multiply-imaged galaxies in strong cluster lenses. The key idea is that imprinted in these deviations are lensing distortions from DM substructure, which can be harnessed statistically to distinguish among DM theories. We simulate JWST-quality mock observations of strong gravitational lensing in galaxy clusters, modeling line-of-sight DM substructure within $ψ$DM and the standard cold dark matter (CDM) paradigms. Using mock deep observations ($\sim$ 20 hours), we find that $\mathrm{P}_δ(k)$ is sensitive to both $ψ$DM particle mass and fluctuation amplitude, and can distinguish $ψ$DM fluctuations from CDM subhalos. We demonstrate that $\mathrm{P}_δ(k)$ can be measured directly from data by modeling the smooth lensing with a local Curved Arc Basis formalism. With realistic modeling systematics, we find a statistically significant separation between $ψ$DM and CDM across $1 \lesssim k \lesssim 11\,\mathrm{kpc}^{-1}$ -- offering an independent probe of the wave-like nature of DM complementary to existing constraints.

astro-ph.CO↗

arXiv2026

Robustness of Neural Networks for CMB Polarization Foreground Removal

The detection of Cosmic Microwave Background primordial $B$-mode polarization would constitute a ``smoking gun" signal of primordial gravitational waves. However, this measurement requires accurate removal of polarized Galactic foregrounds to avoid systematic biases when estimating the tensor-to-scalar ratio. Methods based on Machine Learning techniques (ML), such as Convolutional Neural Networks (CNNs), have recently been proposed as alternative foreground cleaning techniques, but their applicability to real data relies on their ability to generalize beyond the models assumed during training. In this work, we focus on a variety of foreground models (FMs) used for training and conduct a systematic study of the generalization properties of a CNN-based method. We train various CNN architectures on simulations generated from different Galactic FMs, and test their performance on models not used during the training. By characterizing the statistical properties of the FMs using variance, skewness, and Shannon entropy, we define a statistical complexity hierarchy among them. We show that training on the more complex FMs reduces bias and improves precision when testing on unseen FMs, whereas training on the simplest model could introduce systematic errors. These results evidence that a lack of generalization is a relevant source of systematic uncertainty, and emphasize the importance of understanding the impact of the models assumed during training in ML-based methods before applying them to real data.

astro-ph.CO↗

arXiv2026

The Future of Artificial Intelligence and the Mathematical and Physical Sciences (AI+MPS)

This community paper developed out of the NSF Workshop on the Future of Artificial Intelligence (AI) and the Mathematical and Physics Sciences (MPS), which was held in March 2025 with the goal of understanding how the MPS domains (Astronomy, Chemistry, Materials Research, Mathematical Sciences, and Physics) can best capitalize on, and contribute to, the future of AI. We present here a summary and snapshot of the MPS community's perspective, as of Spring/Summer 2025, in a rapidly developing field. The link between AI and MPS is becoming increasingly inextricable; now is a crucial moment to strengthen the link between AI and Science by pursuing a strategy that proactively and thoughtfully leverages the potential of AI for scientific discovery and optimizes opportunities to impact the development of AI by applying concepts from fundamental science. To achieve this, we propose activities and strategic priorities that: (1) enable AI+MPS research in both directions; (2) build up an interdisciplinary community of AI+MPS researchers; and (3) foster education and workforce development in AI for MPS researchers and students. We conclude with a summary of suggested priorities for funding agencies, educational institutions, and individual researchers to help position the MPS community to be a leader in, and take full advantage of, the transformative potential of AI+MPS.

cs.AI↗

arXiv2026

Fast Fourier Transform evaluation of the Fresnel integral for gravitational-wave lensing

Gravitational waves (GWs) exhibit wave-optics effects when their wavelength is comparable to the scale of the gravitational lens. This may occur in lensing from galactic subhalos in GWs emitted by binary black-hole mergers, and is gaining interest as a novel probe of dark matter. Predictions for observables in these cases ultimately rely on evaluating a Fresnel integral that quantifies the effect of lensing on the amplitude of a GW at a given frequency. However, numerical evaluation of this Fresnel integral is tricky, and several algorithms and publicly available codes that implement it have been developed. Here, we show that the dependence of this integral on the lens position can be written as a two-dimensional Fourier transform. Modern FFT techniques then enable rapid evaluation at all-sky positions simultaneously for general lenses without symmetry. Vectorization of FFT routines allows for derivatives with respect to model parameters to be obtained with only incremental additional computational cost. If the lens is axisymmetric, further speedups can be achieved with recently developed techniques for non-uniform fast Hankel transforms. To demonstrate, we make available Fresnel Integral Optimization with Non-uniform trAnsforms (FIONA), an efficient and accurate code that is significantly faster than current methods for dense source grids, reaching 2-3 orders of magnitude speedups for $\sim 10^6$ GW-emitting points. As part of FIONA, we developed code that provides vectorized non-uniform fast Hankel transforms that may have other uses (e.g., calculation of cosmological two-point correlation functions) beyond those considered here.

astro-ph.CO↗

arXiv2026

Constraining Power of Wavelet vs. Power Spectrum Statistics for CMB Lensing and Weak Lensing with Learned Binning

We present forecasts for constraints on the matter density ($Ω_m$) and the amplitude of matter density fluctuations at 8h$^{-1}$Mpc ($σ_8$) from CMB lensing convergence maps and galaxy weak lensing convergence maps. For CMB lensing convergence auto statistics, we compare the angular power spectra ($C_\ell$'s) to the wavelet scattering transform (WST) coefficients. For CMB lensing convergence $\times$ galaxy weak lensing convergence statistics, we compare the cross angular power spectra to wavelet phase harmonics (WPH). This work also serves as the first application of WST and WPH to these probes. For CMB lensing convergence, we find that WST and $C_\ell$'s yield similar constraints in forecasts for all surveys considered in this work. When CMB lensing convergence is crossed with galaxy weak lensing convergence projected from $\textit{Euclid}$ Data Release 2 (DR2), we find that WPH outperforms cross-$C_\ell$'s by factors between $2.2$ and $3.4$ for individual parameter constraints. To compare these different summary statistics, we develop a novel learned binning approach. This method compresses summary statistics while maintaining interpretability. We find this leads to improved constraints compared to more naive binning schemes for our wavelet-based statistics, but not for $C_\ell$'s. By learning the binning and measuring constraints on distinct data sets, our method is robust to overfitting by construction.

astro-ph.CO↗

arXiv2025

Astrophysical Tests of Dark Matter Self-Interactions

Self-interacting dark matter (SIDM) arises generically in scenarios for physics beyond the Standard Model that have dark sectors with light mediators or strong dynamics. The self-interactions allow energy and momentum transport through halos, altering their structure and dynamics relative to those produced by collisionless dark matter. SIDM models provide a promising way to explain the diversity of galactic rotation curves, and they form a predictive and versatile framework for interpreting astrophysical phenomena related to dark matter. This review provides a comprehensive explanation of the physical effects of dark matter self-interactions in objects ranging from galactic satellites (dark and luminous) to clusters of galaxies and the large-scale structure. The second major part describes the methods used to constrain SIDM models including current constraints, with the aim of advancing tests with upcoming galaxy surveys. This part also provides a detailed review of the unresolved small-scale structure formation issues and concrete ways to test simple SIDM models. The review is rounded off by a discussion of the theoretical motivation for self-interactions, degeneracies with baryonic and gravitational effects, extensions to the single-component elastic-interactions SIDM framework, and future observational and theoretical prospects.

astro-ph.CO↗

arXiv2025

Field-level Reconstruction from Foreground-Contaminated 21-cm Maps

Current and upcoming 21-cm experiments will soon be able to map 21-cm spatial fluctuations in three dimensions for a wide range of redshifts. However, bright foreground contamination and the nature of radio interferometry create significant challenges, making it difficult to access rich cosmological information from the Fourier modes that lie within the "foreground wedge". In this work, we introduce two approaches aiming to reconstruct the full 21-cm density field, including the missing modes in the wedge: (a) a field-level inference under an effective field theory (EFT) framework; (b) a diffusion-based deep generative model trained on simulations. Under the EFT framework, we implement a fully differentiable forward model that maps the initial conditions of matter fluctuations to the observed, foreground-filtered 21-cm maps. This enables a gradient-based sampler to simultaneously sample the initial conditions and bias parameters, allowing a physically motivated mode reconstruction. Alternatively, we apply a variational diffusion model to perform 21-cm density reconstruction at the map level. Our model is trained on semi-numerical simulations over a wide range of astrophysical parameters. Our results from both approaches should provide improved cosmological constraints from the field level and also enable cross-correlation between experiments that have little or no overlapping modes.

astro-ph.CO↗

arXiv2025

Dark Matter Substructure or Source Model Systematics? A Case Study of Cluster Lens Abell S1063

Mapping the small-scale structure of the universe through gravitational lensing is a promising tool for probing the particle nature of dark matter. Curved Arc Basis (CAB) has been proposed as a local lensing formalism in galaxy clusters, with the potential to detect low-mass dark matter substructure. In this work, we analyze the cluster lens Abell S1063 in search of dark matter substructure with the CAB formalism, using multi-band imaging data from JWST. We use two different source modeling methods: shapelets and pixel-based source reconstruction based on Delaunay triangulation. We find that source modeling systematics from shapelets result in a disagreement between Curved Arc Basis parameters measured from different filters. Source modeling with Delaunay significantly alleviates this systematic, as seen in the improvement in agreement across filters. We also find that inadequate complexity in source modeling can result in convincing spurious detections of dark matter substructure from strong gravitational lenses, as seen by our $Δ\text{BIC} > 20$ measurement of a $M \sim 10^{10}$ $M_{\odot}$ subhalo with shapelets, a spurious detection that is not reproduced with Delaunay source modeling. We demonstrate that multi-band analysis with different JWST filters is key for disentangling source and lens model systematics from dark matter substructure detections.

astro-ph.CO↗

arXiv2025

The Roman View of Strong Gravitational Lenses

Galaxy-galaxy strong gravitational lenses can constrain dark matter models and the Lambda Cold Dark Matter cosmological paradigm at sub-galactic scales. Currently, there is a dearth of images of these rare systems with high signal-to-noise and angular resolution. The Nancy Grace Roman Space Telescope (hereafter, Roman), scheduled for launch in late 2026, will play a transformative role in strong lensing science with its planned wide-field surveys. With its remarkable 0.281 square degree field of view and diffraction-limited angular resolution of ~0.1 arcsec, Roman is uniquely suited to characterizing dark matter substructure from a robust population of strong lenses. We present a yield simulation of detectable strong lenses in Roman's planned High Latitude Wide Area Survey (HLWAS). We simulate a population of galaxy-galaxy strong lenses across cosmic time with Cold Dark Matter subhalo populations, select those detectable in the HLWAS, and generate simulated images accounting for realistic Wide Field Instrument detector effects. For a fiducial case of single 146-second exposures, we predict around 160,000 detectable strong lenses in the HLWAS, of which about 500 will have sufficient signal-to-noise to be amenable to detailed substructure characterization. We investigate the effect of the variation of the point-spread function across Roman's field of view on detecting individual subhalos and the suppression of the subhalo mass function at low masses. Our simulation products are available to support strong lens science with Roman, such as training neural networks and validating dark matter substructure analysis pipelines.

astro-ph.CO↗

arXiv2025

Precise Cosmological Constraints from BOSS Galaxy Clustering with a Simulation-Based Emulator of the Wavelet Scattering Transform

We perform a reanalysis of the BOSS CMASS DR12 galaxy dataset using a simulation-based emulator for the Wavelet Scattering Transform (WST) coefficients. Moving beyond our previous works, which laid the foundation for the first galaxy clustering application of this estimator, we construct a neural net-based emulator for the cosmological dependence of the WST coefficients and the 2-point correlation function multipoles, trained from the state-of-the-art suite of \textsc{AbacusSummit} simulations combined with a flexible Halo Occupation Distribution (HOD) galaxy model. In order to confirm the accuracy of our pipeline, we subject it to a series of thorough internal and external mock parameter recovery tests, before applying it to reanalyze the CMASS observations in the redshift range $0.46<z<0.57$. We find that a joint WST + 2-point correlation function likelihood analysis allows us to obtain marginalized 1$σ$ errors on the $Λ$CDM parameters that are tighter by a factor of $2.5-6$, compared to the 2-point correlation function, and by a factor of $1.4-2.5$ compared to the WST-only results. This corresponds to a competitive $0.9\%$, $2.3\%$ and $1\%$ level of determination for parameters $ω_c$, $σ_8$ $\&$ $n_s$, respectively, and also to a $0.7\%$ $\&$ $2.5 \%$ constraint on derived parameters h and $f(z)σ_8(z)$, in agreement with the \textit{Planck} 2018 results. Our results reaffirm the constraining power of the WST and highlight the exciting prospect of employing higher-order statistics in order to fully exploit the power of upcoming Stage-IV spectroscopic observations.

astro-ph.CO↗

arXiv2025

The Primordial Inflation Explorer (PIXIE): Mission Design and Science Goals

The Primordial Inflation Explorer (PIXIE) is an Explorer-class mission concept to measure the energy spectrum and linear polarization of the cosmic microwave background (CMB). A single cryogenic Fourier transform spectrometer compares the sky to an external blackbody calibration target, measuring the Stokes I, Q, U parameters to levels ~200 Jy/sr in each 2.65 degree diameter beam over the full sky, in each of 300 frequency channels from 28 GHz to 6 THz. With sensitivity over 1000 times greater than COBE/FIRAS, PIXIE opens a broad discovery space for the origin, contents, and evolution of the universe. Measurements of small distortions from a CMB blackbody spectrum provide a robust determination of the mean electron pressure and temperature in the universe while constraining processes including dissipation of primordial density perturbations, black holes, and the decay or annihilation of dark matter. Full-sky maps of linear polarization measure the optical depth to reionization at nearly the cosmic variance limit and constrain models of primordial inflation. Spectra with sub-percent absolute calibration spanning microwave to far-IR wavelengths provide a legacy data set for analyses including line intensity mapping of extragalactic emission and the cosmic infrared background amplitude and anisotropy. We describe the PIXIE instrument sensitivity, foreground subtraction, and anticipated science return from both the baseline 2-year mission and a potential extended mission.

astro-ph.CO↗

arXiv2024

Substructure Detection in Realistic Strong Lensing Systems with Machine Learning

Tens of thousands of galaxy-galaxy strong lensing systems are expected to be discovered by the end of the decade. These will form a vast new dataset that can be used to probe subgalactic dark matter structures through its gravitational effects, which will in turn allow us to study the nature of dark matter at small length scales. This work shows how we can leverage machine learning to search through the data and identify which systems are most likely to contain dark matter substructure and thus can be studied in greater depth. We use a UNet, an image segmentation architecture, on a simulated strongly-lensed dataset with realistic sources (COSMOS galaxies), lenses (power-law elliptical profiles with multipoles and external shear), and noise. Our machine learning algorithm is able to quickly detect most substructure at high image resolution and subhalo concentration. At a false positive rate of $10\%$, we are able to identify systems with substructure at a true positive rate of $71\%$ for a subhalo mass range of $10^{9}\text{-}10^{9.5}\,M_\odot$. While recent detections are consistent with higher concentrations, we find that our algorithm fails at detecting subhalos with lower concentrations (expected from $Λ$CDM simulations).

astro-ph.CO↗

arXiv2024

Analysis of BOSS Galaxy Data with Weighted Skew-Spectra

We present the first application of the weighted skew-spectra to analyze non-Gaussian information in galaxy survey data. Using the tree-level galaxy skew-spectra together with the one-loop power spectrum multipoles, we analyze the Sloan Digital Sky Survey (SDSS)-III Baryon Oscillation Spectroscopic Survey (BOSS) galaxy clustering data, and target our search towards the equilateral bispectrum shape of primordial non-Gaussianity. We use the Effective Field Theory model for the galaxy power spectrum and bispectrum, and account for systematic effects, such as the survey geometry. From our likelihood analysis, we find $f_{\rm NL}^{\rm equil}=-34^{+296}_{-334}$ at $68\%$ CL, consistent with previous works, while systematic errors from our treatment of the survey geometry lead to an unreliable estimation of $f_{\rm NL}^{\rm ortho}$. We further constrain the bias and counterterm parameters, while keeping the cosmology fixed to $\textit{Planck }2018$ values. As a check, we also validate our analysis pipeline using the ${\tt Nseries}$ simulation suite.

astro-ph.CO↗

arXiv2024

Subhalo effective density slope measurements from HST strong lensing data with neural likelihood-ratio estimation

Examining the properties of subhalos with strong gravitational lensing images can shed light on the nature of dark matter. From upcoming large-scale surveys, we expect to discover orders of magnitude more strong lens systems that can be used for subhalo studies. To optimally extract information from a large number of strong lensing images, machine learning provides promising avenues for efficient analysis that is unachievable with traditional analysis methods, but application of machine learning techniques to real observations is still limited. We build upon previous work, which uses a neural likelihood-ratio estimator, to constrain the effective density slopes of subhalos and demonstrate the feasibility of this method on real strong lensing observations. To do this, we implement significant improvements to the forward simulation pipeline and undertake careful model evaluation using simulated images. Ultimately, we use our trained model to predict the effective subhalo density slope from combining a set of strong lensing images taken by the \textit{Hubble Space Telescope}. We found the subhalo slope measurement of this set of observations to be steeper than the slope predictions of cold dark matter subhalos. Our result adds to several previous works that also measured high subhalo slopes in observations. Although a possible explanation for this is that subhalos with steeper slopes are easier to detect due to selection effects and thus contribute to statistical bias, our result nevertheless points to the need for careful analysis of more strong lensing observations from future surveys.

astro-ph.CO↗

arXiv2023

Data Compression and Inference in Cosmology with Self-Supervised Machine Learning

The influx of massive amounts of data from current and upcoming cosmological surveys necessitates compression schemes that can efficiently summarize the data with minimal loss of information. We introduce a method that leverages the paradigm of self-supervised machine learning in a novel manner to construct representative summaries of massive datasets using simulation-based augmentations. Deploying the method on hydrodynamical cosmological simulations, we show that it can deliver highly informative summaries, which can be used for a variety of downstream tasks, including precise and accurate parameter inference. We demonstrate how this paradigm can be used to construct summary representations that are insensitive to prescribed systematic effects, such as the influence of baryonic physics. Our results indicate that self-supervised machine learning techniques offer a promising new approach for compression of cosmological data as well its analysis.

astro-ph.CO↗

arXiv2023

Dark Dimension and Decaying Dark Matter Gravitons

We explore the cosmology of the Dark Dimension scenario taking into account perturbations in the linear regime. In the context of the Dark Dimension scenario, a natural candidate for dark matter in our universe is the excitations of a tower of massive spin-2 KK gravitons. These dark gravitons are produced in the early universe and decay to lighter KK gravitons during the course of cosmological evolution. The decay causes the average dark matter mass to decrease as the universe evolves. In addition, the kinetic energy liberated in each decay leads to a kick velocity for the dark matter particles leading to a suppression of structure formation. Using current CMB (Planck), BAO and cosmic shear (KiDS-1000) data, we put a bound on the dark matter kick velocity today $v_\mathrm{today} \leq 2.2 \times 10^{-4} c$ at 95\% CL. This leads to rather specific regions of parameter space for the dark dimension scenario. The combination of the experimental bounds from cosmology, astrophysics and table-top experiments lead to the range $l_5\sim 1- 10 \, μm$ for the size of the Dark Dimension. The Dark Dimension scenario is found to be remarkably consistent with current observations and provides signatures that are within reach of near-future experiments.

astro-ph.CO↗

arXiv2023

Detecting Low-Mass Perturbers in Cluster Lenses using Curved Arc Bases

Strong gravitationally lensed arcs produced by galaxy clusters have been observationally detected for several decades now. These strong lensing constraints provided high-fidelity mass models for cluster lenses that include substructure down to $10^{9-10}\,\mathrm{M}_\odot$. Optimizing lens models, where the cluster mass distribution is modeled by a smooth component and subhalos associated with the locations of individual cluster galaxies, has enabled deriving the subhalo mass function, providing important constraints on the nature and granularity of dark matter. In this work, we explore and present a novel method to detect and measure individual perturbers (subhalos, line-of-sight halos, and wandering supermassive black holes) by exploiting their proximity to highly distorted lensed arcs in galaxy clusters, and by modeling the local lensing distortions with curved arc bases. This method offers the possibility of detecting individual low-mass perturber subhalos in clusters and halos along the line-of-sight down to a mass resolution of $10^8\,\mathrm{M}_\odot$. We quantify our sensitivity to low-mass perturbers ($M\sim 10^{7-9}\,\mathrm{M}_\odot$) in clusters ($M\sim 10^{14-15}\mathrm{M}_\odot$), by creating realistic mock data. Using three lensed images of a background galaxy in the cluster SMACS J0723, taken by the $\textit{James Webb Space Telescope}$, we study the retrieval of the properties of potential perturbers with masses $M=10^{7-9}\,\mathrm{M}_\odot$. From the derived posterior probability distributions for the perturber, we constrain its concentration, redshift, and ellipticity. By allowing us to probe lower-mass substructures, the use of curved arc bases can lead to powerful constraints on the nature of dark matter as discrimination between dark matter models appears on smaller scales.

astro-ph.GA↗

arXiv2023

Snowmass2021 Cosmic Frontier White Paper: Dark Matter Physics from Halo Measurements

The non-linear process of cosmic structure formation produces gravitationally bound overdensities of dark matter known as halos. The abundances, density profiles, ellipticities, and spins of these halos can be tied to the underlying fundamental particle physics that governs dark matter at microscopic scales. Thus, macroscopic measurements of dark matter halos offer a unique opportunity to determine the underlying properties of dark matter across the vast landscape of dark matter theories. This white paper summarizes the ongoing rapid development of theoretical and experimental methods, as well as new opportunities, to use dark matter halo measurements as a pillar of dark matter physics.

hep-ph↗