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Uroš Seljak

Publications and source records attributed to Uroš Seljak.

At least 19 recordsLinked to original sources
arXiv2026

Model-Independent Measurement of Baryon Gas Fractions through Galaxy-Galaxy Lensing and the Kinematic Sunyaev-Zel'dovich Effect

Baryon feedback is a leading source of systematic uncertainty for cosmology from weak lensing, but measurements of the gas distribution around galaxies have largely relied on parametric profile models or simulation-calibrated frameworks. We present model independent measurements of the radial gas fraction profile around galaxies, combining galaxy-galaxy lensing and kinematic Sunyaev-Zel'dovich (kSZ) effect. We introduce a method that applies the same radial $ΔΣ$ aperture filter to both the galaxy-galaxy lensing shear field and the velocity-weighted kSZ temperature maps; their ratio directly yields $ΔΣ$ filtered gas fraction $f_{\rm gas}(R)$, the ratio of ionized gas to total matter as a function of projected radius. We apply this approach to DESI DR2 Bright Galaxy Survey (BGS, $\bar{z} \approx 0.26$) and Luminous Red Galaxy (LRG, $0.4 < z < 1.1$) samples, using ACT DR6 component-separated CMB maps for kSZ, and the HSC Year 3 shear catalog for weak lensing. After correcting for ACT beam suppression using a simulation-calibrated compensation factor, we detect baryon depletion relative to the cosmic mean baryon fraction at SNR = 17.2 (BGS) and SNR = 13.8 (LRG bin 1). Comparison with six hydrodynamical simulations from the Illustris, IllustrisTNG, SIMBA, and FLAMINGO suites shows that the fiducial FLAMINGO run most closely tracks the observed radial gas distribution in both samples, with Illustris-1 and TNG300-1 bracketing the data from below and above at small radii. We find a mild discrepancy between the kSZ measurements in the South and North Galactic Caps in one LRG redshift bin, which may indicate residual systematics. We show that the ratio is robust to splits by stellar mass and to the removal of close pairs. These results establish $ΔΣ$ filtering of kSZ versus weak lensing signal as a model-independent probe of the baryon distribution around galaxies.

astro-ph.CO↗
arXiv2026

Field-Level Baryon Acoustic Oscillation Reconstruction of the DESI DR1 Luminous Red Galaxies with Linear Field Transformer (LiFT)

We present the first application of neural field-level baryon-acoustic oscillation (BAO) reconstruction to real spectroscopic survey data. We develop Linear Field Transformer (LiFT), a 3D vision transformer that takes as input the observed galaxy field, its standard reconstruction, and a set of context channels encoding local line of sight, survey coverage, and redshift, and learns to correct standard reconstruction toward the linear density field. We construct a forward-modeling pipeline to produce mock lightcones similar to the DESI Data Release 1 (DR1) luminous red galaxy (LRG) sample, and train LiFT on these. We validate LiFT on held-out simulations, as well as on additional mocks which differ in gravity solver, halo finder, HOD, cosmology, and fiber-assignment history, as well as on mocks analyzed with a distorted distance-redshift relation; ultimately, we find unbiased dilation parameters with consistently tighter constraints than standard reconstruction. Applied to the DESI DR1 LRGs, LiFT improves errors on $α_{\rm iso}$ by 13%, 24%, and 30% and on $α_{\rm AP}$ by 5%, 22%, and 30% relative to the DESI DR1 standard reconstruction analysis in the LRG1, LRG2, and LRG3 bins respectively; meanwhile, our DR1 central values remain consistent with DR1 and DR2. This equates to a factor of 1.2, 1.7 and 2.0 increase in Figure of Merit (or effective survey volume) if one were to only use standard reconstruction. Ultimately, these results establish LiFT as a validated, survey-ready tool for current and upcoming galaxy surveys.

astro-ph.CO↗
arXiv2026

Radiological mapping and uncertainty quantification by a fast Microcanonical Langevin Monte Carlo sampler

Radiological mapping plays a critical role in nuclear emergency response and environmental management activities. A radiation image, representing the spatial and intensity distribution of the radioactivity, is reconstructed from the radiation data and the associated contextual information. Typical image reconstruction methods, such as Maximum Likelihood Expectation-Maximization (ML-EM), only provide point estimates of the pixel or voxel activities without associated uncertainties. Here, we apply a new Microcanonical Langevin Monte Carlo (MCLMC) sampler for radiation image reconstruction and uncertainty quantification. The MCLMC sampler properties are first tested with synthetic radiation images. Methods to obtain the radiation distribution estimate and the associated uncertainty from the samples drawn by MCLMC are discussed. Given sufficient measurement statistics, the radiation distribution estimate obtained from MCLMC results closely resembles the ground truth with less risk of over- or under-fitting compared to ML-EM. When MCLMC is run in parallel on a GPU, the samples can converge to the posterior distribution in about 10 seconds for an image with $10^3$--$10^4$ pixels, which is significantly faster than other comparable Markov Chain Monte Carlo (MCMC) samplers. We also tested MCLMC on a dataset from a real distributed source radiological mapping campaign, and the reconstructed results agree well with the expected activity map. The fast MCLMC sampler therefore enables improved imaging accuracy and prompt uncertainty quantification for reconstructed radiation images, which can better inform decision-making in response to radiological events.

physics.med-ph↗
arXiv2026

It's All About the Environment: Local $f_{\rm NL}$ from a Dark Matter Conditioned Multitracer Analysis

Primordial non-Gaussianity (PNG) offers a powerful test of inflationary physics beyond the simplest single-field scenarios. In large-scale structure, biased tracers respond to local-type PNG with a distinctive scale-dependent signature that is strongest on large scales, but single-tracer measurements are fundamentally limited by cosmic variance. Multitracer methods can mitigate this limitation; however, many existing proposals require splitting tracers using a wide range of halo masses which are often not available, or using secondary halo properties that are not directly observable. Here we develop and validate an alternative strategy based on splitting a tracer sample by its large-scale dark matter environment. We derive a theoretical framework for the $f_{\rm NL}$ response of environmentally selected tracers and test it in $N$-body simulations, confirming consistency between separate-universe predictions and direct power-spectrum inference. To enable observational implementation, we show that a simple linear-theory reconstruction of the dark matter environment from halos yields unbiased $f_{\rm NL}$ constraints in $\mathtt{Quijote}$ simulations. Forecasts for the DESI LRG sample indicate that environmental multitracing can improve constraints on local PNG by a factor of 2-3 relative to conventional single-tracer analyses for high density tracers, comparable to gains achieved when halo formation time is assumed to be perfectly known. We develop an analytic model explaining why the gains are smaller for a halo environment split and why separate-universe predictions fail in this case. Unlike approaches that require modeling assembly bias or inferring secondary halo properties, our method relies only on linear theory. These results establish environmental multitracer techniques as a promising route toward $σ(f_{\rm NL})\sim 1$ with upcoming galaxy surveys.

astro-ph.CO↗
arXiv2026

A Multimodal Approach to Star--Galaxy Separation using SPHEREx Spectrophotometry and DESI Legacy Survey Imaging

Stellar contamination is a critical systematic for increasingly precise large-scale structure analyses from ongoing and next-generation surveys. Experiments targeting constraints on local primordial non-Gaussianity with $σ(f_{\rm NL}^{\rm loc}) \sim \mathcal{O}(1)$ demand sub-percent stellar contamination rates to avoid misidentifying spurious large-scale power induced by Galactic structure as true cosmological signal. In this work, we explore the use of multimodal models for star--galaxy separation, harnessing the information from both optical broad-band imaging data and SPHEREx near-infrared low-resolution spectrophotometry. The two modalities are integrated using contrastive learning, which projects image- and spectrum-based embeddings into a shared latent space. We find that classifiers trained on these transformed representations outperform those trained on the original embeddings and show less performance degradation when simpler classifiers are used. These results suggest that multimodal alignment organizes the embedding space along dimensions that are better suited to source classification. The improvement is particularly strong for image-based classification, which we connect to increased predictability of highly-discriminative infrared spectral features from the transformed image embeddings. Applying redshift error-based selections and extrapolating to the full SPHEREx footprint, we demonstrate that stellar contamination can be controlled at the sub-percent level across most of the extragalactic sky, with completeness tradeoffs largely confined to low redshift. Our work highlights the utility of multimodal methods for modern galaxy surveys such as SPHEREx and $\textit{Rubin}$ LSST.

astro-ph.CO↗
arXiv2026

Remembering George Smoot

George Smoot (1945-2025) was a major figure in observational cosmology, particularly through his role in the precision measurement of the CMB dipole and the discovery of the primordial CMB temperature anisotropy as part of the COBE team. This collection of 33 essays from collaborators, colleagues, students, and friends offers varied insights into the work, career, and life of George Smoot.

astro-ph.CO↗
arXiv2026

Practical and Scalable Hamiltonian Monte Carlo Without the Metropolis Test

Hamiltonian Monte Carlo and underdamped Langevin Monte Carlo are leading methods for sampling from high-dimensional distributions with differentiable densities. Both rely on numerical integration, which introduces asymptotic bias in expectation estimates. This bias can be removed by adjusting the numerical integration with a Metropolis Hastings (MH) step, at a cost of slower mixing and larger variance. Alternatively, we can trade bias for lower variance if we avoid the MH step and use an appropriate step size of integration. These unadjusted schemes have strong performance, especially in high-dimensional problems, but are rarely used due to the lack of automated step size selection. We propose an automatic tuning scheme that selects a step size to meet a user-specified asymptotic bias tolerance. The method is based on a relationship between energy error and bias which we establish. We rigorously analyze the method in the Gaussian setting and numerically extend the analysis to several non Gaussian problems. Experiments on Bayesian inference and large scale statistical physics models (with over one million parameters) show that, with our tuning, unadjusted methods consistently and significantly outperform adjusted counterparts.

stat.CO↗
arXiv2026

Microcanonical Hamiltonian Monte Carlo

We develop Microcanonical Hamiltonian Monte Carlo (MCHMC), a class of models which follow a fixed energy Hamiltonian dynamics, in contrast to Hamiltonian Monte Carlo (HMC), which follows canonical distribution with different energy levels. MCHMC tunes the Hamiltonian function such that the marginal of the uniform distribution on the constant-energy-surface over the momentum variables gives the desired target distribution. We show that MCHMC requires occasional energy conserving billiard-like momentum bounces for ergodicity, analogous to momentum resampling in HMC. We generalize the concept of bounces to a continuous version with partial direction preserving bounces at every step, which gives an energy conserving underdamped Langevin-like dynamics with non-Gaussian noise (MCLMC). MCHMC and MCLMC exhibit favorable scalings with condition number and dimensionality. We develop an efficient hyperparameter tuning scheme that achieves high performance and consistently outperforms NUTS HMC on several standard benchmark problems, in some cases by more than an order of magnitude.

stat.CO↗
arXiv2026

FAIR Universe Weak Lensing ML Uncertainty Challenge: Handling Uncertainties and Distribution Shifts for Precision Cosmology

Weak gravitational lensing, the correlated distortion of background galaxy shapes by foreground structures, is a powerful probe of the matter distribution in our universe and allows accurate constraints on the cosmological model. In recent years, high-order statistics and machine learning (ML) techniques have been applied to weak lensing data to extract the nonlinear information beyond traditional two-point analysis. However, these methods typically rely on cosmological simulations, which poses several challenges: simulations are computationally expensive, limiting most realistic setups to a low training data regime; inaccurate modeling of systematics in the simulations create distribution shifts that can bias cosmological parameter constraints; and varying simulation setups across studies make method comparison difficult. To address these difficulties, we present the first weak lensing benchmark dataset with several realistic systematics and launch the FAIR Universe Weak Lensing Machine Learning Uncertainty Challenge. The challenge focuses on measuring the fundamental properties of the universe from weak lensing data with limited training set and potential distribution shifts, while providing a standardized benchmark for rigorous comparison across methods. Organized in two phases, the challenge will bring together the physics and ML communities to advance the methodologies for handling systematic uncertainties, data efficiency, and distribution shifts in weak lensing analysis with ML, ultimately facilitating the deployment of ML approaches into upcoming weak lensing survey analysis.

astro-ph.CO↗
arXiv2026

A Probabilistic Autoencoder for Galaxy SED Reconstruction and Redshift Estimation: Application to Mock SPHEREx Spectrophotometry

We present a probabilistic autoencoder (PAE) framework for galaxy spectral energy distribution (SED) modeling and redshift estimation, applied to synthetic SPHEREx 102-band spectrophotometry. Our PAE learns a compact latent representation of rest-frame galaxy SEDs transformed to a simple Gaussian base density using a normalizing flow, combined with an explicit forward model enabling joint Bayesian inference over intrinsic SED parameters and redshift with well-defined priors. In controlled tests on simulated SPHEREx spectra, our PAE improves on template fitting (TF) in source recovery, outlier rate, and posterior calibration, with trade-offs in redshift performance that depend on the assumed priors. A simple cut on the ratio of PAE and TF uncertainties identifies sources that are overwhelmingly TF outliers, which can be used to clean existing TF samples while retaining the vast majority of well-recovered sources. By directly profiling over PAE latent variables, we show these cases correspond to shallow likelihood surfaces where the PAE's continuous SED manifold produces broader likelihoods that more faithfully reflect the lack of constraining power in the data, whereas the TF discrete model grid yields artificially confident but incorrect redshift estimates. Lastly, we present an alternative, simulation-based inference approach using a Transformer encoder and conditional normalizing flow, which provides similar redshift performance to the PAE but with $\sim200\times$ faster inference throughput. Our implementation, \texttt{PAESpec}, is publicly available and provides a foundation for principled redshift estimation in modern photometric surveys.

astro-ph.IM↗
arXiv2026

The Age of the Universe with Globular Clusters IV: Multiple Stellar Populations

We revisit the determination of the age of the Universe from galactic globular clusters, extending previous analyses by explicitly accounting for the presence of multiple stellar populations within each cluster. Using high--quality \textit{Hubble Space Telescope} color--magnitude diagrams for 69 globular clusters, we relax the standard single--population assumption, and model two stellar populations with independent ages, metallicities, helium abundances, and population fractions. The inference is performed using the full color--magnitude diagram morphology, an explicit treatment of field contamination, and a hierarchical framework that propagates non--Gaussian age posteriors. Allowing for multiple stellar populations has a negligible impact on globular cluster age estimates. The ages of the oldest populations remain fully consistent with those obtained under the single--population assumption, with differences at the $0.6σ$ level. Restricting to the metal--poor subsample ([Fe/H] $< -1.5$), we infer a dominant old component with mean age $t_{\rm GC}=13.61\pm0.25\,\mathrm{(stat)}\,\pm0.23 \mathrm{(sys)}\,\mathrm{Gyr}$. Adopting a conservative delay between the Big Bang and the formation of the first globular clusters, we obtain an age of the Universe of $t_{\rm U}=13.81\pm0.25\,\mathrm{(stat)}\,\pm0.23 \mathrm{(sys)}\,\mathrm{Gyr}$. In addition to age constraints, our analysis yields simultaneous measurements of metallicity and helium content for the different populations, including constraints on helium enrichment and population fractions which are consistent with independent determinations from the literature. These results demonstrate that globular--cluster--based cosmic chronometry is robust to stellar population complexity, reinforcing its role as a precise and largely cosmological model--independent probe of the age of the Universe.

astro-ph.GA↗
arXiv2026

Beyond Gaussian Assumptions: A new robust statistical framework for gravitational-wave data analysis

Many traditional algorithms applied in gravitational-wave astronomy rely on the assumption of Gaussian noise, a condition not always met. To meet this need, this study extends a robust statistical framework, advancing previous work on heavy-tailed likelihoods, that adapts the hyperbolic likelihood method for full frequency domain applications. The framework is designed to maintain high performance under ideal conditions while improving robustness against non-Gaussian noise and outliers in real-world data. We demonstrate the efficacy of this approach through two key case studies. The first case study analyzes a massive black hole binary merger in simulated Laser Interferometer Space Antenna (LISA) data with Gaussian noise, showing that the extended hyperbolic likelihood method performs comparably to the more commonly used Whittle likelihood. The second case study examines a stellar-mass black hole binary merger using real ground-based gravitational-wave data containing non-Gaussian noise or overlapping signals, where our framework exhibits increased robustness and yields more accurate parameter estimations. Our results show that the hyperbolic likelihood better captures the true noise distribution, providing a flexible and physically motivated alternative for GW data analysis across current and future detectors.

gr-qc↗
arXiv2026

Counterdiabatic Hamiltonian Monte Carlo

Hamiltonian Monte Carlo (HMC) is a state of the art method for sampling from distributions with differentiable densities, but can converge slowly when applied to challenging multimodal problems. Running HMC with a time varying Hamiltonian, in order to interpolate from an initial tractable distribution to the target of interest, can address this problem. In conjunction with a weighting scheme to eliminate bias, this can be viewed as a special case of Sequential Monte Carlo (SMC) sampling \cite{doucet2001introduction}. However, this approach can be inefficient, since it requires slow change between the initial and final distribution. Inspired by \cite{sels2017minimizing}, where a learned \emph{counterdiabatic} term added to the Hamiltonian allows for efficient quantum state preparation, we propose \emph{Counterdiabatic Hamiltonian Monte Carlo} (CHMC), which can be viewed as an SMC sampler with a more efficient kernel. We establish its relationship to recent proposals for accelerating gradient-based sampling with learned drift terms, and demonstrate on simple benchmark problems.

stat.ML↗
arXiv2026

Exoplanet transit search at the detection limit: detection and false alarm vetting pipeline

One of the primary mission goals of the Kepler space telescope was to detect Earth-like terrestrial planets in the habitable zone around Sun-like stars. These planets are at the detection limit, where the Kepler detection and vetting pipeline produced unreliable planet candidates. We present a novel pipeline that improves the removal of localized defects prior to the planet search, improves vetting at the level of individual transits and introduces a Bayes factor test statistic and an algorithm for extracting multiple candidates from a single detection run. We show with injections in the Kepler data that the introduced novelties improve pipeline's completeness at a fixed false alarm rate. We apply the pipeline to the stars with previously identified planet candidates and show that our pipeline successfully recovers the previously confirmed candidates, but flags a considerable portion of unconfirmed candidates as likely false alarms, especially in the long period, low signal-to-noise ratio regime. In particular, several known Earth-like candidates in the habitable zone, such as KOI 8063.01, 8107.01 and 8242.01, are identified as false alarms, which could have a significant impact on the estimates of $η_{\oplus}$, i.e., the occurrence of Earth-like planets in the habitable zone.

astro-ph.EP↗
arXiv2026

Faster parallel MCMC: Metropolis adjustment is best served warm

Despite the enormous success of Hamiltonian Monte Carlo and related Markov Chain Monte Carlo (MCMC) methods, sampling often still represents the computational bottleneck in scientific applications. Availability of parallel resources can significantly speed up MCMC inference by running a large number of chains in parallel, each collecting a single sample. However, the parallel approach converges slowly if the chains are not initialized close to the target distribution (cold start). Theoretically this can be resolved by initially running MCMC without Metropolis-Hastings adjustment to quickly converge to the vicinity of the target distribution and then turn on adjustment to achieve fine convergence. However, no practical scheme uses this strategy, due to the difficulty of automatically selecting the step size during the unadjusted phase. We here develop Late Adjusted Parallel Sampler (LAPS), which is precisely such a scheme and is applicable out of the box, all the hyperparameters are selected automatically. LAPS takes advantage of ensemble-based hyperparameter adaptation to estimate the bias at each iteration and converts it to the appropriate step size. We show that LAPS consistently and significantly outperforms ensemble adjusted methods such as MEADS or ChESS and the optimization-based initializer Pathfinder on a variety of standard benchmark problems. LAPS typically achieves two orders of magnitude lower wall-clock time than the corresponding sequential algorithms such as NUTS.

stat.CO↗
arXiv2025

Flinch: A Differentiable Framework for Field-Level Inference of Cosmological parameters from curved sky data

We present Flinch, a fully differentiable and high-performance framework for field-level inference on angular maps, developed to improve the flexibility and scalability of current methodologies. Flinch is integrated with differentiable cosmology tools, allowing gradients to propagate from individual map pixels directly to the underlying cosmological parameters. This architecture allows cosmological inference to be carried out directly from the map itself, bypassing the need to specify a likelihood for intermediate summary statistics. Using simulated, masked CMB temperature maps, we validate our pipeline by reconstructing both maps and angular power spectra, and we perform cosmological parameter inference with competitive precision. In comparison with the standard pseudo-$C_\ell$ approach, Flinch delivers substantially tighter constraints, with error bars reduced by up to 40%. Among the gradient-based samplers routinely employed in field-level analyses, we further show that MicroCanonical Langevin Monte Carlo provides orders-of-magnitude improvements in sampling efficiency over currently employed Hamiltonian Monte Carlo samplers, greatly reducing computational expense.

astro-ph.CO↗
arXiv2025

Reassessment of Kepler's habitable zone Earth-like exoplanets with data-driven null-signal templates

One of the primary mission goals of the Kepler space telescope is to detect Earth-like terrestrial planets in the habitable zone around Sun-like stars. Unfortunately, such planets are at the detection limit. Estimating their statistical significance via false alarm probability (FAP) is crucial for their validation, and has a large impact on the estimate of their occurrence rate, which is of central importance for future spectroscopic missions searching for life signatures. Current methods estimate FAP by light curve inverting or scrambling, but we show that both of these approaches are unsatisfactory. Here we propose to modify the planet transit template by randomly shifting the transit times by small amounts. We show that the exoplanet search with the resulting Null Signal Template (NST) has the same statistical properties as with the true periodic template, which enables assigning a reliable star-specific FAP to every candidate. We show on simulations and on the real data that the method is robust to unmodeled noise contamination. We reevaluate the statistical significance of all 47 previously proposed habitable Earth-like and super Earth Kepler candidates and assign them star-specific NST based FAP. We identify 29 candidates with FAP below 1%, 7 of whom are currently not considered confirmed. Among these are Kepler 452b with radius $1.5 R{\oplus}$, a period of 384 days, and KOI 2194.03 with radius $1.8 R{\oplus}$ and a period of 445 days, both around Sun-like G stars. Several well-known candidates should be considered marginal or likely false alarms, including Kepler 186f with 20% FAP.

astro-ph.EP↗
arXiv2025

Selecting samples of galaxies with fewer Fingers-of-God

The radial positions of galaxies inferred from their measured redshift appear distorted due to their peculiar velocities. We argue that the contribution from stochastic velocities -- which gives rise to Fingers-of-God (FoG) anisotropy in the inferred maps -- does not lend itself to perturbative modelling already on scales targeted by current experiments. To get around this limitation, we propose to remove FoG using data-driven indicators of their abundance that are local in nature and thus avoid selection biases. In particular, we show that the scale where the measured power spectrum quadrupole changes sign is tightly anti-correlated with both the satellite fraction and the velocity dispersion, and can thus be used to select galaxy samples with fewer FoG. In addition, we show that excluding galaxies in haloes more massive than a given mass threshold can help to discard many of the most problematic galaxies. Such selection could be achieved in practice using maps of the thermal Sunyaev-Zeldovich distortion of the cosmic microwave background frequency spectrum. These techniques could potentially improve reconstructions of the large-scale velocity and displacement fields from the redshift-space positions of galaxies. They may also extend the reach of perturbative models for galaxy clustering, though in practice we find only marginal gains when fitting one-loop EFTofLSS models to simulations with mitigated FoG due to the relevance of other effects entering at two-loop order.

astro-ph.CO↗