arXiv subjects

the LSST Dark Energy Science Collaboration

Publications and source records attributed to the LSST Dark Energy Science Collaboration.

At least 19 recordsLinked to original sources

arXiv2026

An LSST-DESC Precursor Project: Hyper Suprime-Cam Year 1 $3\times2$pt in Harmonic Space

We present the first fully photometric joint analysis of weak gravitational lensing and galaxy clustering (3$\times$2pt) in harmonic space with LSST-DESC analysis pipelines applied to Hyper Suprime-Cam Year 1 (HSC Y1) data. The HSC Y1 dataset, with imaging depth and galaxy number density similar to those expected for the first year of LSST observations, is an ideal testbed for validating DESC measurement and inference tools. We measure the full set of angular power spectra - cosmic shear, galaxy clustering, and galaxy-galaxy lensing - perform null tests, and correct for the impact of observing conditions on both the galaxy density and shear fields via mode deprojection. We validate our likelihood pipeline by reproducing the official HSC Y1 cosmic shear cosmological constraints with the DESC inference code. Jointly analysing all three two-point functions, we constrain $Λ$CDM and $w$CDM cosmologies, reporting results for cosmic shear, the combination of galaxy clustering and galaxy-galaxy lensing (2$\times$2pt), and the full 3$\times$2pt. From the joint 3$\times$2pt $Λ$CDM analysis we find $Ω_m = 0.249^{+0.061}_{-0.051}$, $σ_8 = 0.904^{+0.098}_{-0.091}$, and $S_8 \equiv σ_8\sqrt{Ω_m/0.3} = 0.821^{+0.018}_{-0.021}$. This analysis validates the complete DESC 3x2pt infrastructure on survey data, providing the foundation for the forthcoming LSST Year 1 cosmological analysis.

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arXiv2026

DeepDISC on LSST Data Preview 1: Scene-Level Detection, Deblending, and Star/Galaxy Classification in the ECDFS and EDFS Fields

We present the first application of DeepDISC, a scene-level deep-learning framework, to real LSST data, using the Data Preview 1 (DP1) coadded images of the Extended Chandra Deep Field-South and the Euclid Deep Field-South fields. In a single pass, DeepDISC jointly detects, segments, and classifies sources as stars or galaxies, rather than treating these tasks as separate steps as in traditional pipelines. We warm-start from a model pretrained on LSST simulations and fine-tune it on DP1 six-band images, using the DP1 catalog's extendedness flag (point-like versus extended) as provisional class labels. The classification part of the network is then fine-tuned alone on a curated sample of stars and galaxies with spectroscopic, Gaia, and photometric identifications, so the final output is a star/galaxy classification. Fine-tuning on real DP1 observations substantially improves source recovery relative to the un-fine-tuned model, raising galaxy and star completeness from 40.2% and 13% to 84.2% and 49.1% under strict class-aware matching; under class-agnostic matching the fine-tuned model reaches 84.9% and 74.3%. On a common sample of sources that neither classifier saw in training, DeepDISC classifies to within about one percentage point of a published photometry-based Random Forest for the same field (97.4% vs. 98.5%); at fainter magnitudes DeepDISC labels more sources as stars than the Random Forest, and independent labels favor the Random Forest in most of these disagreements. Cross-matching to deeper HST/CANDELS imaging on a held-out footprint gives an unrecognized-blend fraction for DeepDISC of 14.9% (95% CI 12.5-17.8%), consistent with or modestly below the 18.0% we measure for the production DP1 catalog. This work establishes a baseline for DeepDISC on real LSST data and a foundation for self-supervised pretraining and for LSST Data Preview 2.

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arXiv2026

Balancing bias, baryons, and scale cuts in LSST 3x2pt analysis

Stage IV surveys such as LSST will probe deeply into the nonlinear regime, where systematic effects from galaxy bias and baryonic feedback become dominant and poorly constrained nuisance parameters can lead to degeneracies. In this work we present a 3x2pt analysis for LSST Y1 and Y10 data using the BACCO emulator for modelling both the hybrid-effective field theory (HEFT) for nonlinear galaxy bias and the baryonic feedback using the baryonification mechanism. We aim to find a balance between model complexity and scale cuts, with particular attention to parameter degeneracies and baryonic feedback effects on the galaxy-matter and galaxy-galaxy power spectra. First, we find that a linear bias model delivers percent-level unbiased constraints on $Ω_{\rm m}$ and $σ_8$ only up to $k_{\rm max}=0.1 h/$Mpc, but pushing to smaller scales requires a perturbative approach. Second, we compare HEFT with a minimal bias variant with fixed higher-order terms, and find that the latter is unbiased in $Λ$CDM even at $k_{\rm max}=0.7 h/$Mpc. We show that higher-order bias can mimic baryonic suppression, but baryons cannot reproduce the full range of higher-order bias behaviour within the parameter range allowed by the BACCO baryonification model. Third, we find that a detection of the total neutrino mass $M_ν$ is possible for both Y1 and Y10 for $k \geq 0.5 h/$Mpc, at least when photo-$z$ uncertainties and related nuisance parameters are precisely known. However, the specific measured value is not robust across equally plausible mock scenarios: the inferred $M_ν$ can be significantly biased by adopting the minimal bias model. The entire analysis is conducted with a new independent, open source pipeline MGL that we present for the first time in this work.

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arXiv2026

Weak-Lensing Shear Response for Photometric Redshift-Based Tomographic Binning

Dividing source galaxies into tomographic redshift bins is a cornerstone of modern weak gravitational lensing analyses, enabling measurements of the growth of cosmic structure and the nature of dark energy. In practice, these tomographic bins are defined using photometric redshift (photo-$z$) estimates. However, correlations between photo-$z$ estimates and weak lensing shear can introduce redshift-dependent selection biases in the measured shear signal; if left uncorrected, these biases distort the inferred amplitude and redshift evolution of the lensing signal, and in turn bias the measurement of the growth of cosmic structure across cosmic time. In this paper, we extend the analytical self-calibration for shear measurement (AnaCal) framework to account for photo-$z$-based selection biases in tomographic weak lensing analyses by propagating shear responses through the selection process. This approach eliminates the need for external image simulations to calibrate this correction. As a first sanity check on real data, we validate the photo-$z$ estimates derived from AnaCal fluxes on the Rubin Observatory Data Preview 1 dataset, and find that they reach photo-$z$ quality comparable to, and at high redshift slightly better than, the standard LSST estimates. We then validate the shear calibration on LSST-like image simulations with blending at the expected LSST Y10 depth, with two representative photo-$z$ algorithms -- a template-fitting method and a machine-learning method -- and show that the multiplicative shear bias induced by photo-$z$ selection remains within the LSST ten-year requirement $|m| < 3\times 10^{-3}$ across all five tomographic bins for both algorithms. These results establish AnaCal as a self-consistent pipeline for tomographic weak lensing science in upcoming LSST analyses.

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arXiv2026

Lens Model Accuracy in the Expected LSST Lensed AGN Sample

Strong gravitational lensing of active galactic nuclei (AGN) enables measurements of cosmological parameters through time-delay cosmography (TDC). With data from the upcoming LSST survey, we anticipate using a sample of O(1000) lensed AGN for TDC. To prepare for this dataset and enable this measurement, we construct and analyze a realistic mock sample of 1300 systems drawn from the OM10 (Oguri & Marshall 2010) catalog of simulated lenses with AGN sources at $z<3.1$ in order to test a key aspect of the analysis pipeline, that of the lens modeling. We realize the lenses as power law elliptical mass distributions and simulate 5-year LSST i-band coadd images. From every image, we infer the lens mass model parameters using neural posterior estimation (NPE). Focusing on the key model parameters, $θ_E$ (the Einstein Radius) and $γ_{lens}$ (the projected mass density profile slope), with consistent mass-light ellipticity correlations in test and training data, we recover $θ_E$ with less than 1% bias per lens, 6.5% precision per lens and $γ_{lens}$ with less than 3% bias per lens, 8% precision per lens. We find that lens light subtraction prior to modeling is only useful when applied to data sampled from the training prior. If emulated deconvolution is applied to the data prior to modeling, precision improves across all parameters by a factor of 2. Finally, we combine the inferred lens mass models using Bayesian Hierarchical Inference to recover the global properties of the lens sample with less than 1% bias.

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arXiv2026

Lens Modeling and Cosmological Inference from an Impure Sample of Galaxy-Galaxy Strong Lenses

The start of the Legacy Survey of Space and Time marks a new era for strong lensing science, where the number of strong lenses identified is expected to increase to $\mathcal{O}(10^5)$. In this paper we use a neural network to determine the precision with which lens parameters can be determined, using realistic simulated LSST lensed systems. We find that the Einstein radius can be measured with a mean precision of $3.7\%$ with calibrated uncertainties accurately reflecting the corresponding measurement error. Based on the performance of current strong lens classifiers, the $\sim 100,000$ detectable strong lenses are expected to be accompanied by a similar or larger number of false positives (non-lenses). In readiness for this we introduce a formalism, termed `COSMIC-BEAMS', to infer cosmological parameters while accounting for contamination by false positives. As a proof-of-concept, using simulated LSST measurements of the Einstein radii of a realistic and impure sample of photometric lens systems, i.e. those without spectroscopic confirmation, we find that the cosmological parameters $Ω_m$, $Ω_Λ$, and $w$ can be measured to a precision of $0.1$, $0.03$ and $0.15$ respectively for a $w$CDM cosmology. We demonstrate that unbiased cosmological parameters can be inferred even in strong lens samples contaminated by $50\%$ false positives, and that the photometric dataset of $100\,000$ strong lenses will provide equivalent $w$-precision to $2500-3500$ spectroscopic systems.

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arXiv2026

Probabilistic characterization of blending with LSST and application to cluster lensing cosmology

Next-generation galaxy surveys, like the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST), will deliver unprecedented depth and sky coverage, enabling precise measurements of cosmic probes such as weak lensing and galaxy clustering. However, increased imaging depth leads to significant blending of galaxy images, particularly in dense fields like galaxy clusters. This blending, exacerbated by atmospheric blurring in ground-based observations, contaminates galaxy property measurements and causes source confusion. To simultaneously capture these effects, we develop a probabilistic framework introducing the blending entropy, a metric quantifying the ambiguity in matching detected objects to true galaxies or external reference sources. Using simulated data from the DESC Data Challenge 2 (DC2), we characterize blending in LSST data and quantify its impact on cluster lensing cosmology around cosmoDC2 halos. We demonstrate that imposing a blending entropy threshold of $S_b<0.2$ effectively filters out highly blended objects (around 25%), which are especially prevalent near the survey's magnitude limit and are associated with higher errors in shape measurements and photometric redshifts. Applying this cut substantially reduces blending-induced biases in cluster lensing profiles and mass estimates, thereby mitigating systematic errors in cosmological parameters---most notably reducing tension in $σ_8$ estimates. Our method is readily generalizable to other static probes and offers a practical path forward for real data analyses, particularly when leveraging overlapping high-resolution datasets from spaced-based missions such as Euclid or the Roman Space Telescope, where these external datasets can act as reference catalogs to improve the identification of blended sources in LSST data.

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arXiv2026

Neural Posterior Estimation for Tomographic Weak Lensing Mass Mapping

Weak gravitational lensing shear and convergence trace the distribution of baryonic and dark matter across space, making them a powerful probe of cosmic structure. Inferring shear and convergence from images is a challenging inverse problem. The prevailing approach to this task estimates shear from weighted averages of galaxy ellipticities, calibrates these estimates to account for systematic biases, and transforms them to reconstruct convergence, a multistage procedure that requires substantial computational resources and meticulous handling of statistical uncertainties. As an alternative, we propose a probabilistic approach to field-level weak lensing inference in which we train a deep neural network to directly map a multiband image to a variational distribution over the underlying tomographic shear and convergence fields. This neural posterior estimation (NPE) procedure implicitly marginalizes over nuisance variables in the cosmological forward model and does not require evaluating the likelihood function. It is also amortized, so it enables rapid posterior inference for astronomical surveys once the neural network is trained. When evaluated on synthetic images from the LSST-DESC DC2 Simulated Sky Survey, NPE produces well-calibrated variational distributions for shear and convergence that are consistent with the ground truth. We describe how maps sampled from these variational distributions could be used in a subsequent simulation-based inference procedure to approximate the posterior distribution over cosmological parameters.

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arXiv2026

Simulation Tests of PSF Modeling for Cosmic Shear with the Vera C. Rubin Observatory

Exceptional control of systematic effects is required in order to achieve unbiased cosmic shear two-point correlation function measurements with the next generation of galaxy imaging surveys, such as the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). One critical challenge is accurately modeling the point-spread function (PSF), as errors in PSF estimation can introduce spatially correlated biases in galaxy shape measurements. The LSST Science Pipelines, which will be used to process Rubin data, include an implementation of the PIFF (PSFs in the Full Field of View) package originally developed for, and demonstrated to perform well on, DES-Y3 and Y6 data. In this work we use semi-realistic image simulations, mimicking LSST observing conditions in $i$-band over 100 square degrees, to perform an end-to-end test of the Rubin PSF modeling pipeline. PSF model residuals are quantified using both second- and fourth-order moment parameters and performance is evaluated, for both LSST year 1 and year 10 depth, with a series of diagnostic tests. We find that the additive bias contribution from PSF modeling errors to the non-tomographic cosmic shear data vector is well below 30% of the cosmic shear uncertainty estimated from our analytic covariance matrix. Though our simulations exclude several known effects that may further challenge PSF modeling, these results demonstrate promising performance from PIFF on LSST-like data and provide an early benchmark for ongoing PSF validation efforts for LSST weak lensing analyses.

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arXiv2026

Differentiable Forward Modeling for Efficient and Accurate Shear Inference

Forthcoming Stage-IV dark energy optical surveys, such as LSST, have the ambitious goal of measuring cosmological parameters at sub-percent precision. Realizing their full scientific potential requires very precise measurement of the cosmic shear signal and control of corresponding systematics. In this work, we present a modern implementation of the Bayesian shear inference framework in Schneider et al. (2015), in the case that the PSF and sky background are known. This framework automatically propagates the pixel-noise measurement error from each galaxy into the final shear estimate, and thus requires no external calibration to handle noise bias. As a first application of this new implementation, we infer the cosmic shear posterior from simulated images consisting of isolated exponential galaxies with semi-realistic noise levels. In this simplified scenario, we estimate the absolute multiplicative bias $|m|$ of our approach to be below $0.9 \times 10^{-3}\,[3σ]$ when the intrinsic distribution of galaxy properties is known, and below $1.3 \times 10^{-3}\,[3σ]$ when these distributions are inferred alongside shear. Additionally, we make progress towards the algorithm's computational feasibility in the context of modern wide-field surveys, where billions of galaxies must be processed, by leveraging differentiable forward models of galaxies, gradient-based samplers, and GPUs. Our final galaxy-fitting MCMC produces $300$ effective samples of galaxy properties in $0.45$ seconds per galaxy using a single A100 GPU. In the future, we seek to generalize our algorithm to handle selection, detection, and model shear biases so it can be applied to real survey data.

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arXiv2026

DB-Bench: Benchmarking Deblenders for LSST DESC Using the Blending ToolKit

Blending will be a major source of systematic uncertainty in downstream science analyses of LSST data. We benchmark the performance of several deblenders, leveraging the Blending ToolKit (BTK) to perform rigorous, end-to-end testing. This benchmark incorporates key deblending algorithms, including SourceExtractor, SCARLET, and DeepDISC, with the goal of comparing their effectiveness in handling blended galaxy images from LSST/Rubin simulations. A key focus is characterizing algorithm performance in the regime of unrecognized blends, where multiple galaxies are misidentified as a single object, as these cases introduce systematic biases that propagate into downstream cosmological analyses for galaxy surveys. By utilizing BTK's ability to create customized, reproducible blends, we systematically test these deblenders against different blending conditions, such as source separation and brightness. The toolkit's standardized evaluation metrics, including detection precision, segmentation accuracy, and source reconstruction, are comprehensive assessments of each algorithm's strengths and limitations. Each deblender has performance caveats that may impact their true performance in real survey conditions. We find that SCARLET has high segmentation and reconstruction performance, whereas DeepDISC has strong detection recall for faint and low-SNR sources, and SourceExtractor has accurate peak finding abilities but low segmentation and reconstruction performance. This benchmark provides valuable insights into the performance of existing deblenders and highlights areas for future development.

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arXiv2026

Neural Posterior Estimation for Inferring Weak Lensing Shear

The prevailing approach to inferring weak gravitational lensing shear from images involves detecting galaxies, estimating their ellipticities, and calibrating these estimates to correct for image noise, selection bias, and model misspecification. Characterizing the statistical model and assumptions underlying this pipeline is challenging, which makes it difficult to propagate uncertainty through its various stages. As an alternative, we propose to infer shear using neural posterior estimation (NPE), a type of simulation-based inference. We train a deep neural network to map a simulated multiband image to a variational distribution over the underlying shear field, thereby folding galaxy detection, deblending, measurement, and calibration into a single implicit inference step. Once trained, the network accounts for all features present in the simulated images, including potential sources of bias. In experiments on simulated constant-shear images with increasingly complex observational effects, NPE produces accurate and well-calibrated posterior approximations for both shear components in the presence of blended galaxies, spatially varying point spread functions, stars, and detector artifacts. These results demonstrate that NPE can be a viable shear estimation method in settings where all anticipated features and artifacts can be simulated, a requirement that will become increasingly feasible as simulation fidelity improves in the coming decades.

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arXiv2026

Propagating data-driven galaxy redshift distribution uncertainties in 3$\times$2-pt analyses

Uncertainties in the radial distribution of galaxies, $\boldsymbol{n}(\boldsymbol{z})$, are one of the major contributions to the error budget of early Stage-IV galaxy survey analyses of weak gravitational lensing, galaxy clustering and galaxy-galaxy lensing (3$\times$2-pt). Based on ensembles of simulated $\boldsymbol{n}(\boldsymbol{z})$ including stochastic and systematic variations, we study the impact of four different $\boldsymbol{n}(\boldsymbol{z})$ uncertainty models: shifts, shifts & stretches, Gaussian processes (GP) and principal component analysis (PCA). Due to the high dimensionality of the latter models, we make use of state-of-the-art gradient-based inference methods as well as approximate analytical marginalisation schemes. Our results show that Stage-IV 3$\times$2-pt analyses must go beyond simple shift & stretch models. In particular, we advocate for the adoption of PCA models even in early Stage-IV surveys. Our results show that considering a five-parameters PCA model only degrades the constraint on the $S_{\rm 8}$ parameter by $5$ per cent with respect to the case when only a shift and a stretch parameter are included, while incurring half the bias in its constituents parameters, $Ω_{\rm m}$ and $σ_{\rm 8}$. We demonstrate that all models considered can be safely marginalised analytically, with speed-ups of up to a factor of 25 depending on the dimensionality of the model. This will allow Stage-IV analyses to safely include higher-dimensional $\boldsymbol{n}(\boldsymbol{z})$ uncertainty models in their analysis at negligible additional computational cost.

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arXiv2026

Strong Lensing Tomography: Double and pseudo multi-source plane strong gravitational lensing to constrain dark energy

Tomographic measurements of gravitational lensing with different lens and source redshift distributions contain crucial information about the universe's relative expansion rate, and hence dark energy. While this technique is well-established in weak lensing, its application to strong lensing has traditionally focused on Double Source Plane Lenses (DSPLs). However, DSPLs are exceedingly rare and fundamentally limited by the Mass-Sheet Degeneracy (MSD), a systematic uncertainty underexplored in previous literature. To overcome these challenges, we introduce Pseudo Double-Source Plane Lenses (PDSPLs): pairs of independent single-source plane lenses with self-similar deflectors. This generalizes the DSPL formalism to the $\sim 10^5$ galaxy-galaxy lenses expected from upcoming surveys like LSST, Euclid, and Roman. Unlike true DSPLs, PDSPLs are free from the intermediate source mass problem by construction, eliminating the associated secondary MSD and the need for multi-plane ray tracing. We incorporate the deflector galaxy's MSD into a hierarchical forecasting framework, demonstrating that this degeneracy severely degrades constraints from small DSPL samples, thus motivating our PDSPL statistical approach. We forecast constraints on the dark energy equation of state under a Flat $w_0w_a$CDM cosmology. The LSST 10-year photometric sample alone achieves $σ(w_0) \sim 0.45$, while simultaneously constraining the MSD parameter and deflector power-law slope to $\sim 2\%$. Adding a prior $\mathcal{N}(0.3, 0.05)$ on $Ω_{\rm m}$ -- simulating combination with external probes like CMB, BAO, or SNe Ia -- tightens this to $σ(w_0) \sim 0.29$, competitive with current Stage III weak lensing analyses. Notably, this massive photometric sample outperforms smaller subsets with precise spectroscopic follow-up (e.g., from 4MOST), confirming statistical volume dominates over per-pair precision.

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arXiv2026

SLSim: a strong lensing population simulation package

Gravitational lensing offers unique insights into cosmology by bending light around massive objects. Strong gravitational lensing, in particular, produces magnified and often multiple images of distant sources, crucial for precise cosmological measurements and understanding the distribution of dark matter in the universe. Current studies are limited by the number of strong gravitational lenses. From upcoming cosmological surveys, we anticipate observing a several orders of magnitude increase in the number of lenses, for both static and transient phenomena. However, detecting and analyzing these events from vast surveys like Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) presents significant challenges. To prepare for these challenges, we introduce SLSim, a versatile simulation tool tailored for the Vera C. Rubin Observatory. SLSim integrates advanced astrophysical models with computational efficiency to generate synthetic strong lens populations under realistic observational conditions. SLSim simulates static and variable lensing scenarios, essential for cosmological studies, training and testing lens search and data analysis pipelines. This paper details SLSim,'s design and implementation, emphasizing its modularity and capabilities across various astrophysical regimes. Validation against observational data and existing simulations confirms SLSim's accuracy in reproducing observed lensing phenomena. SLSim is publicly available at https://github.com/LSST-strong-lensing/slsim, and we anticipate continued development and expansion of its capabilities. Users are encouraged to check the repository for updates and to contribute to ongoing community efforts in strong lensing simulations.

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arXiv2026

Constraining Galaxy Cluster Triaxiality via Weak Lensing -- I. Preparation for the Rubin Data Beyond Leading Order

The 3D mass distributions of galaxy clusters are generally triaxial, a geometry that is difficult to constrain from projected observations. In this work, we measure the projected halo shapes of clusters from their weak lensing signatures using the triaxiality functionality in the Cluster Lensing Mass Modeling software, a tool developed by the Dark Energy Science Collaboration to analyze data from NSF-DOE Rubin Observatory's Legacy Survey of Space and Time (LSST). We measure ensemble halo ellipticity on the plane of the sky via axis-aligned stacking and multipole expansion of the weak lensing data. We study a precursor dataset -- the redMaPPer cluster catalog, the metacalibration shape catalog, and the Directional Neighborhood Fitting photometric redshift catalog from the Dark Energy Survey Year 3 public data release. We select clusters that have a high centering probability (>90%) of the identified central galaxy, and use the satellite galaxy distribution to determine the major-axis orientation for stacking. We extend the analysis to the second order of ellipticity in the monopole and quadrupole measurement. The projected ellipticity of the cluster sample is found to be $0.310^{+0.017}_{-0.016}$ (axis ratio $0.527^{+0.018}_{-0.019}$). The projected cluster ellipticity shows no statistically significant dependence on mass and redshift. We further verify the accuracy of the cluster shape measurement using mock catalogs. This analysis is applicable to datasets from upcoming wide-area cosmic surveys such as LSST, Euclid, and the Roman Space Telescope, where larger sample sizes will lead to tighter constraints on the cluster ellipticities.

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arXiv2026

Optimization of Weak Lensing Lightcone Simulations for Higher-Order Statistics in the LSST era

We present a framework for generating lightcone simulations tailored to the analysis of Stage-IV cosmic shear data using Higher-Order Statistics (HOS). We revisit key design choices from previous simulation campaigns and re-optimize several internal parameters, benchmarking accuracy through changes in $χ^2$ of cosmic shear statistics under survey conditions mimicking 10 years of observations from the Legacy Survey of Space and Time (LSST). We find that discretizing the lightcone uniformly in scale factor yields higher accuracy than commonly adopted schemes such as uniform spacing in redshift or comoving distance. While $N_{\rm part} = 1024^3$ simulation particles (corresponding to a mass resolution of $m_{\rm part} = 2.08\times10^{10}M_\odot$) is sufficient to model two-point statistics up to $\ell = 5000$, we observed significant instabilities on our full suite of HOS as the number of mass shells used in the lightcone construction, $N_{\rm shells}$, is varied. In contrast, simulations with $N_{\rm part} = 2048^3$ particles ($m_{\rm part} = 2.60\times10^{9}M_\odot$) robustly reproduce all statistics considered. In this higher-resolution configuration, $N_{\rm shells}$ can be reduced to $\sim50$ with only minor deviations, no larger than $0.1-0.3σ$ relative to our highest-resolution case ($N_{\rm shells}\sim100$). This has been explicitly verified through a comparison between our fiducial lightcone production mode based on slicing particle snapshots and an exact lightcone mode where individual particle trajectories are solved for at runtime. We further show that the particle density per pixel can be downsampled by a significant amount for $z>1.5$, saving large computational resources with no impact on the resulting statistics. These results guide the design of upcoming simulation campaigns geared towards forward-modeling and emulation-based analyses of Stage-IV data.

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arXiv2026

Probing Physics Beyond the Standard Model through Combined Analyses of Next-Generation Type Ia Supernova, CMB, and BAO Surveys

Observations of Type Ia supernovae (\sne), which probe the late Universe, together with baryon acoustic oscillations (BAO) and the cosmic microwave background (CMB), which probe the intermediate and early epochs, provide complementary constraints on the expansion history of the Universe. In this work, we forecast constraints on dark energy and other extensions to the standard cosmological model by combining the SNIa sample expected from the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST), data from current and forthcoming CMB surveys, and BAO measurements from the Dark Energy Spectroscopic Instrument (DESI). For the CMB, we use temperature, polarization, and lensing power spectra ($TT/EE/TE/ϕϕ$) from South Pole Telescope, the planned Advanced Simons Observatory, and a CMB-S4-like experiment. We derive constraints on $Λ{\rm CDM}$ and its extensions involving the dark energy equation of state parameters $(w_{0}, w_{a})$ and the sum of neutrino masses $\sum m_ν$, using a Markov Chain Monte Carlo (MCMC) sampling framework. We find that the LSST Year-3 SNIa sample can improve upon the DES Year-5 dark energy constraints by a factor of $\times2-\times2.5$, with the gains driven primarily by the significantly higher SNIa density in the LSST sample. Similarly, DESI-DR3 shows up to a $\times1.8$ improvement on dark energy parameters over DR2, driven largely by the substantial increase in low-redshift sample. Combining CMB with LSST-Y3-SNIa and DESI-DR3-BAO yields $σ(w_{0}) = 0.028$ and $σ(w_{a}) = 0.11$ for $w_{0} w_{a} {\rm CDM}$ cosmology with the results being largely independent of the CMB dataset. The constraints weaken by 10%-30% when freeing $\sum m_ν$ and spatial curvature. Moreover, the joint analysis of the three datasets can enable a $2-3σ$ detection of $\sum m_ν$.

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