arXiv subjects

C. Danielle Leonard

Publications and source records attributed to C. Danielle Leonard.

At least 19 recordsLinked to original sources

arXiv2026

Fisher Forecasting for the DESC with $\texttt{Augur}$

The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) has begun its ten-year survey of the entire visible southern hemisphere. To ensure robust cosmological measurements, computationally inexpensive investigations of modeling choices must be made to gauge the performance of proposed cosmological analyses. In this paper, we introduce the $\texttt{Augur}$ tool of the Dark Energy Science Collaboration (DESC), which provides Fisher forecasts for cosmological inference for the LSST using software frameworks designed for DESC science. We test the pipeline by comparing it to forecasts produced by external code and direct sampling of the posterior via nested sampling methods, finding good agreement between all methods. We additionally investigate a range of modeling and hyperparameter choices for a 3$\times$2pt investigation in harmonic space, providing users with diagnostics to obtain reliable forecasts. $\texttt{Augur}$ will be continually updated to be compatible with the other tools in the DESC software ecosystem as additional probes and functionality become available.

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arXiv2026

Smokescreen: A Python package for data vector blinding and encryption in cosmological analyses

Smokescreen is an open-source Python library for data-vector concealment (blinding) in cosmological analyses. Data-vector blinding works by applying cosmology-dependent shifts to the observed data vector, moving it away from the true cosmological signal without affecting its statistical properties, so that analysts cannot infer the true result until the analysis is frozen and the blinding is lifted. The package computes these shifts using Firecrown likelihoods applied to data vectors stored in the SACC format, ensuring that the theoretical model used for blinding is identical to that used for inference whilst remaining agnostic to the specific observable being blinded. To prevent accidental unblinding, the original SACC file, containing the true cosmology, is encrypted. Although developed for the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), Smokescreen is applicable to any experiment using Firecrown likelihoods and the SACC data format.

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arXiv2026

Meta-learning for cosmological emulation: Rapid adaptation to new lensing kernels

Theoretical computation of cosmological observables is an intensive process, restricting the speed at which cosmological data can be analysed and cosmological models constrained, and therefore limiting research access to those with high performance computing infrastructure. Whilst the use of machine learning to emulate these computations has been studied, most existing emulators are specialised and not suitable for emulating a wide range of observables with changing physical models. Here, we investigate the Model-Agnostic Meta-Learning algorithm (MAML) for training a cosmological emulator. MAML attempts to train a set of network parameters for rapid fine-tuning to new tasks within some distribution of tasks. Specifically, we consider a simple case where the galaxy sample changes, resulting in a different redshift distribution and lensing kernel. Using MAML, we train a cosmic-shear angular power spectrum emulator for rapid adaptation to new redshift distributions with only $O(100)$ fine-tuning samples, whilst not requiring any parametrisation of the redshift distributions. We compare the performance of the MAML emulator to two standard emulators, one pre-trained on a single redshift distribution and the other with no pre-training, both in terms of accuracy on test data, and the constraints produced when using the emulators for cosmological inference. We observe that within an MCMC analysis, the MAML emulator is able to better reproduce the fully-theoretical posterior, achieving a Battacharrya distance from the fully-theoretical posterior in the $S_8$ -- $Ω_m$ plane of 0.008, compared to 0.038 from the single-task pre-trained emulator and 0.243 for the emulator with no pre-training.

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arXiv2026

Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration

The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will produce unprecedented volumes of heterogeneous astronomical data (images, catalogs, and alerts) that challenge traditional analysis pipelines. The LSST Dark Energy Science Collaboration (DESC) aims to derive robust constraints on dark energy and dark matter from these data, requiring methods that are statistically powerful, scalable, and operationally reliable. Artificial intelligence and machine learning (AI/ML) are already embedded across DESC science workflows, from photometric redshifts and transient classification to weak lensing inference and cosmological simulations. Yet their utility for precision cosmology hinges on trustworthy uncertainty quantification, robustness to covariate shift and model misspecification, and reproducible integration within scientific pipelines. This white paper surveys the current landscape of AI/ML across DESC's primary cosmological probes and cross-cutting analyses, revealing that the same core methodologies and fundamental challenges recur across disparate science cases. Since progress on these cross-cutting challenges would benefit multiple probes simultaneously, we identify key methodological research priorities, including Bayesian inference at scale, physics-informed methods, validation frameworks, and active learning for discovery. With an eye on emerging techniques, we also explore the potential of the latest foundation model methodologies and LLM-driven agentic AI systems to reshape DESC workflows, provided their deployment is coupled with rigorous evaluation and governance. Finally, we discuss critical software, computing, data infrastructure, and human capital requirements for the successful deployment of these new methodologies, and consider associated risks and opportunities for broader coordination with external actors.

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arXiv2026

Extracting intrinsic alignments in the Dark Energy Survey's year 1 data, using the self-calibration method and LSST-DESC tools

We present the implementation of a Self-Calibration of Intrinsic Alignments of galaxies as an extension to the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) Dark Energy Science Collaboration (DESC)'s weak lensing 3x2pt pipeline (TXPipe). As a demonstration, we have run this pipeline on the Dark Energy Survey (DES) year one data set. We find indications of a non-zero intrinsic alignment signal in the higher redshift bins, while in the lower bins our results look more uncertain. We believe this is caused by known issues with the individual galaxies photo-z estimation. This effect is particularly harmful for the self-calibration method, since it has high requirements for reliable estimation of the photo-$z$s, and the need for individual galaxy point estimates and tomographic binning to match. We show how different methods of recreating the redshift probability distribution can affect the detection of intrinsic alignment.

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arXiv2025

Taking the Weight Off: Mitigating Parameter Bias from Catastrophic Outliers in 3$\times$2pt Analysis

Stage IV cosmological surveys will map the universe with unprecedented precision, reducing statistical uncertainties to levels where unmodelled systematics can significantly bias inference. In particular, photometric redshift (photo-z) errors and intrinsic alignments (IA) must be robustly accounted for to ensure accurate inference of cosmological parameters. The increasing depth of Stage IV surveys exacerbates these challenges by producing low signal-to-noise galaxy populations prone to inaccurate photo-z measurements. Catastrophically misidentified redshifts are especially problematic for 3$\times$2pt inferences that combine weak lensing and galaxy clustering information. We demonstrate that even modest outlier fractions (e.g. 5%) can lead to substantial biases in cosmological parameter estimates: up to 1.8$σ$ in $Ω_M$ and $σ_8$, and over 8$σ$ in the IA redshift evolution parameter $η$. To address this, we introduce a flexible weighting scheme at the likelihood level that down-weights the most contamination-sensitive elements of the data vector during inference. This method mitigates biases without inflating the parameter space, reducing cosmological parameter biases to below 1$σ$ without substantially degrading constraining power. Our approach offers a practical solution for future analyses, enabling robust cosmological inference in the presence of catastrophic redshift errors.

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arXiv2025

Forecasting the Impact of Source Galaxy Photometric Redshift Uncertainties on the LSST $3\times2$pt Analysis

Photometric redshifts of the source galaxies are a key source of systematic uncertainty in the Rubin Observatory Legacy Survey of Space and Time (LSST)'s galaxy clustering and weak lensing analysis, i.e., the $3\times 2$pt analysis. This paper introduces a Fisher forecast code FisherA2Z for the LSST Year 10 (Y10) $3 \times 2$pt and cosmic shear analyses, utilizing a 15-parameter redshift distribution model, with one redshift bias, variance, and outlier rate per tomographic bin. FisherA2Z employs the Core Cosmology Library CCL to compute the large-scale structure power spectrum and incorporates a four-parameter nonlinear alignment model for intrinsic alignments. We evaluate the impact of marginalizing over redshift distribution parameters on weak lensing, forecast biases in cosmological parameters due to redshift errors, and assess cosmological parameter sensitivity to redshift systematic parameters using decision trees. The sensitivity study reveals that for LSST $3\times2$pt analysis, $S_8$ is most sensitive to the mean redshift of the fourth out of the five source tomographic bins, while other cosmological parameters possess different sensitivities. Additionally, we provide cosmological analysis forecasts based on different scenarios of spectroscopic training datasets. We find that the figures-of-merit for the cosmological results increase with the number of spectroscopic training galaxies, and with the completeness of the training set above $z=1.6$, assuming the redshift information comes solely from the training set galaxies without other external constraints.

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arXiv2025

Joint Modelling of Astrophysical Systematics for Cosmology with LSST Cosmic Shear

We present a novel framework for jointly modelling the weak lensing source galaxy redshift distribution and the intrinsic alignment of galaxies via a shared luminosity function (LF). Considering this framework within the context of a Rubin Observatory's Legacy Survey of Space and Time (LSST) Year 1 and Year 10 cosmic shear analysis, we first demonstrate the substantial impact of the LF on both source galaxy redshift distributions and the intrinsic alignment contamination. We establish how the individual parameters of a Schechter LF model impact the redshift distribution of a magnitude-limited sample, and we demonstrate the effect of marginalising over the LF parameters as incorporated in the intrinsic alignment modelling of a standard cosmic shear analysis set-up. We forecast the impact of our joint modelling framework on cosmological parameter constraints. Our preliminary results are promising, indicating that this framework can yield cosmological constraints consistent with those expected from standard analyses, enhanced by the flexibility of not fixing LF parameters. We plan to further validate these findings with comprehensive Markov chain Monte Carlo simulations to robustly quantify bias avoidance and underscore the framework's efficacy. Taking advantage of our forecasting results and the parameter degeneracies, we identify the specific impact of the shape of the LF of source galaxies on the cosmic shear data vector. We also discuss the potential of this method in providing a way to model generic selection functions in redshift distribution estimation, as well as its possibilities for extension to a 3x2pt analysis, particularly with respect to incorporating galaxy bias in this luminosity-function-based framework. Although we consider the context of LSST cosmic shear in this work, the proposed joint modelling framework is generically applicable to weak lensing surveys.

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arXiv2024

An Empirical Model For Intrinsic Alignments: Insights From Cosmological Simulations

We extend current models of the halo occupation distribution (HOD) to include a flexible, empirical framework for the forward modeling of the intrinsic alignment (IA) of galaxies. A primary goal of this work is to produce mock galaxy catalogs for the purpose of validating existing models and methods for the mitigation of IA in weak lensing measurements. This technique can also be used to produce new, simulation-based predictions for IA and galaxy clustering. Our model is probabilistically formulated, and rests upon the assumption that the orientations of galaxies exhibit a correlation with their host dark matter (sub)halo orientation or with their position within the halo. We examine the necessary components and phenomenology of such a model by considering the alignments between (sub)halos in a cosmological dark matter only simulation. We then validate this model for a realistic galaxy population in a set of simulations in the IllustrisTNG suite. We create an HOD mock with TNG-like correlations using our method, constraining the associated IA model parameters, with the $χ^2_{\rm dof}$ between our model's correlations and those of Illustris matching as closely as 1.4 and 1.1 for orientation--position and orientation--orientation correlation functions, respectively. By modeling the misalignment between galaxies and their host halo, we show that the 3-dimensional two-point position and orientation correlation functions of simulated (sub)halos and galaxies can be accurately reproduced from quasi-linear scales down to $0.1~h^{-1}{\rm Mpc}$. We also find evidence for environmental influence on IA within a halo. Our publicly-available software provides a key component enabling efficient determination of Bayesian posteriors on IA model parameters using observational measurements of galaxy-orientation correlation functions in the highly nonlinear regime.

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arXiv2024

Photometric redshifts and intrinsic alignments: degeneracies and biases in 3$\times$2pt analysis

We present a systematic study of cosmological parameter bias in weak lensing and large-scale structure analyses for upcoming imaging surveys induced by the interplay of intrinsic alignments (IA) and photometric redshift (photo-z) model mis-specification error. We first examine the degeneracies between the parameters of the Tidal Alignment - Tidal Torquing (TATT) model for IA and of a photo-z model including a mean shift ($Δ\bar{z}$) and variance ($σ_{z}$) for each tomographic bin of lenses and sources, under a variety of underlying true IA behaviors. We identify strong degeneracies between: (1) the redshift scaling of the tidal alignment amplitude and the mean shift and variances of source bins, (2) the redshift scaling of the tidal torquing amplitude and the variance of the lowest-$z$ source bin, and (3) the IA source density weighting and the mean shift and variance of several source bins. We then use this information to guide our exploration of the level of cosmological parameter bias which can be induced given incorrect modelling of IA, photo-z, or both. We find that marginalizing over all the parameters of TATT is generally sufficient to preclude cosmological parameter bias in the scenarios we consider. However, this does not necessarily mean that IA and photo-z parameters are themselves unbiased, nor does it mean that the best-fit model is a good fit to the data. We also find scenarios where the inferred parameters produce $χ^2_{\rm DOF}$ values indicative of a good fit but cosmological parameter bias is significant, particularly when the IA source density weighting parameter is not marginalized over.

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arXiv2024

Accuracy requirements on intrinsic alignments for Stage-IV cosmic shear

In the context of cosmological weak lensing studies, intrinsic alignments (IAs) are one the most complicated astrophysical systematic to model, given the poor understanding of the physical processes that cause them. A number of modelling frameworks for IAs have been proposed in the literature, both purely phenomenological or grounded on a perturbative treatment of symmetry-based arguments. However, the accuracy with which any of these approaches is able to describe the impact of IAs on cosmic shear data, particularly on the comparatively small scales ($k\simeq 1\,{\rm Mpc}^{-1}$) to which this observable is sensitive, is not clear. Here we quantify the level of disagreement between the true underlying intrinsic alignments and the theoretical model used to describe them that can be allowed in the context of cosmic shear analyses with future Stage-IV surveys. We consider various models describing this "IA residual", covering both physics-based approaches, as well as completely agnostic prescriptions. The same qualitative results are recovered in all cases explored: for a Stage-IV cosmic shear survey, a mis-modelling of the IA contribution at the $\sim10\%$ level produces shifts of $\lesssim0.5σ$ on the final cosmological parameter constraints. Current and future IA models should therefore aim to achieve this level of accuracy, a prospect that is not unfeasible for models with sufficient flexibility.

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arXiv2024

Intrinsic alignment from multiple shear estimates: A first application to data and forecasts for Stage IV

Without mitigation, the intrinsic alignment (IA) of galaxies poses a significant threat to achieving unbiased cosmological parameter constraints from precision weak lensing surveys. Here, we apply for the first time to data a method to extract the scale dependence of the IA contribution to galaxy-galaxy lensing, which takes advantage of the difference in alignment signal as measured by shear estimators with different sensitivities to galactic radii. Using data from Year 1 of the Dark Energy Survey, with shear estimators METACALIBRATION and IM3SHAPE, we investigate and address method systematics including non-trivial selection functions, differences in weighting between estimators, and multiplicative bias. We obtain a null detection of IA, which appears qualitatively consistent with existing work. We then forecast the application of this method to Rubin Observatory Legacy Survey of Space and Time (LSST) data and place requirements on a pair of shear estimators for detecting IA and constraining its 1-halo scale dependence. We find that for LSST Year 1, shear estimators should have at least a $40\%$ difference in IA amplitude, and the Pearson correlation coefficient of their shape noise should be at least $ρ=0.50$, to ensure a $1σ$ detection of IA and a constraint on its 1-halo scale dependence with a signal-to-noise ratio greater than $1$. For Year 10, a $1σ$ detection and constraint become possible for $20\%$ differences in alignment amplitude and $ρ=0.50$.

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arXiv2024

Cosmological Constraints on 4-Dimensional Einstein-Gauss-Bonnet Gravity

4-Dimensional Einstein-Gauss-Bonnet (4DEGB) gravity has garnered significant attention in the last few years as a phenomenological competitor to general relativity. We consider the theoretical and observational implications of this theory in both the early and late universe, (re-)deriving background and perturbation equations and constraining its characteristic parameters with data from cosmological probes. Our investigation surpasses the scope of previous studies by incorporating non-flat spatial sections. We explore consequences of 4DEGB on the sound and particle horizons in the very early universe, and demonstrate that 4DEGB can provide an independent solution to the horizon problem for some values of its characteristic parameter $α$. Finally, we constrain an unexplored regime of this theory in the limit of small coupling $α$ (empirically supported in the post-Big Bang Nucleosynthesis era by prior constraints). This version of 4DEGB includes a geometric term that resembles dark radiation at the background level, but whose influence on the perturbed equations is qualitatively distinct from that of standard forms of dark radiation. In this limit, only one beyond-$Λ$CDM degree of freedom persists, which we denote as $\tildeα_C$. Our analysis yields the estimate $\tildeα_C = (-9 \pm 6) \times 10^{-6}$ thereby providing a new constraint of a previously untested sector of 4DEGB.

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arXiv2022

Rubin-Euclid Derived Data Products: Initial Recommendations

This report is the result of a joint discussion between the Rubin and Euclid scientific communities. The work presented in this report was focused on designing and recommending an initial set of Derived Data products (DDPs) that could realize the science goals enabled by joint processing. All interested Rubin and Euclid data rights holders were invited to contribute via an online discussion forum and a series of virtual meetings. Strong interest in enhancing science with joint DDPs emerged from across a wide range of astrophysical domains: Solar System, the Galaxy, the Local Volume, from the nearby to the primaeval Universe, and cosmology.

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arXiv2022

Constraints on dark matter to dark radiation conversion in the late universe with DES-Y1 and external data

We study a phenomenological class of models where dark matter converts to dark radiation in the low redshift epoch. This class of models, dubbed DMDR, characterizes the evolution of comoving dark matter density with two extra parameters, and may be able to help alleviate the observed discrepancies between early- and late-time probes of the universe. We investigate how the conversion affects key cosmological observables such as the CMB temperature and matter power spectra. Combining 3x2pt data from Year 1 of the Dark Energy Survey, {\it Planck}-2018 CMB temperature and polarization data, supernovae (SN) Type Ia data from Pantheon, and baryon acoustic oscillation (BAO) data from BOSS DR12, MGS and 6dFGS, we place new constraints on the amount of dark matter that has converted to dark radiation and the rate of this conversion. The fraction of the dark matter that has converted since the beginning of the universe in units of the current amount of dark matter, $ζ$, is constrained at 68\% confidence level to be $<0.32$ for DES-Y1 3x2pt data, $<0.030$ for CMB+SN+BAO data, and $<0.037$ for the combined dataset. The probability that the DES and CMB+SN+BAO datasets are concordant increases from 4\% for the $Λ$CDM model to 8\% (less tension) for DMDR. The tension in $S_8 = σ_8 \sqrt{Ω_{\rm m}/0.3}$ between DES-Y1 3x2pt and CMB+SN+BAO is slightly reduced from $2.3σ$ to $1.9σ$. We find no reduction in the Hubble tension when the combined data is compared to distance-ladder measurements in the DMDR model. The maximum-posterior goodness-of-fit statistics of DMDR and $Λ$CDM model are comparable, indicating no preference for the DMDR cosmology over $Λ$CDM.

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arXiv2022

Snowmass2021 Cosmic Frontier White Paper: Enabling Flagship Dark Energy Experiments to Reach their Full Potential

A new generation of powerful dark energy experiments will open new vistas for cosmology in the next decade. However, these projects cannot reach their utmost potential without data from other telescopes. This white paper focuses in particular on the compelling benefits of ground-based spectroscopic and photometric observations to complement the Vera C. Rubin Observatory, as well as smaller programs in aid of a DESI-2 experiment and CMB-S4. These additional data sets will both improve dark energy constraints from these flagship projects beyond what would possible on their own and open completely new windows into fundamental physics. For example, additional photometry and single-object spectroscopy will provide necessary follow-up information for supernova and strong lensing cosmology, while highly-multiplexed spectroscopy both from smaller facilities over wide fields and from larger facilities over narrower regions of sky will yield more accurate photometric redshift estimates for weak lensing and galaxy clustering measurements from the Rubin Observatory, provide critical spectroscopic host galaxy redshifts for supernova Hubble diagrams, provide improved understanding of limiting astrophysical systematic effects, and enable new measurements that probe the nature of gravity. A common thread is that access to complementary data from a range of telescopes/instruments would have a substantial impact on the rate of advance of dark energy science in the coming years.

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arXiv2021

Fisher Matrix Stability

Fisher forecasts are a common tool in cosmology with applications ranging from survey planning to the development of new cosmological probes. While frequently adopted, they are subject to numerical instabilities that need to be carefully investigated to ensure accurate and reproducible results. This research note discusses these challenges using the example of a weak lensing data vector and proposes procedures that can help in their solution.

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arXiv2019

Modified Gravity and Dark Energy models Beyond $w(z)$CDM Testable by LSST

One of the main science goals of the Large Synoptic Survey Telescope (LSST) is to uncover the nature of cosmic acceleration. In the base analysis, possible deviations from the Lambda-Cold-Dark-Matter ($Λ$CDM) background evolution will be probed by fitting a $w(z)$CDM model, which allows for a redshift-dependent dark energy equation of state with $w(z)$, within general relativity (GR). A rich array of other phenomena can arise due to deviations from the standard $Λ$CDM+GR model though, including modifications to the growth rate of structure and lensing, and novel screening effects on non-linear scales. Concrete physical models are needed to provide consistent predictions for these (potentially small) effects, to give us the best chance of detecting them and separating them from astrophysical systematics. A complex plethora of possible models has been constructed over the past few decades, with none emerging as a particular favorite. This document prioritizes a subset of these models along with rationales for further study and inclusion into the LSST Dark Energy Science Collaboration (DESC) data analysis pipelines, based on their observational viability, theoretical plausibility, and level of theoretical development. We provide references and theoretical expressions to aid the integration of these models into DESC software and simulations, and give justifications for why other models were not prioritized. While DESC efforts are free to pursue other models, we provide here guidelines on which theories appear to have higher priority for collaboration efforts due to their perceived promise and greater instructional value.

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