arXiv subjects

The LSST Dark Energy Science Collaboration

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

At least 37 records · Page 2Linked to original sources

arXiv2025

DeepDISC-photoz: Deep Learning-Based Photometric Redshift Estimation for Rubin LSST

Photometric redshifts will be a key data product for the Rubin Observatory Legacy Survey of Space and Time (LSST) as well as for future ground and space-based surveys. The need for photometric redshifts, or photo-zs, arises from sparse spectroscopic coverage of observed galaxies. LSST is expected to observe billions of objects, making it crucial to have a photo-z estimator that is accurate and efficient. To that end, we present DeepDISC photo-z, a photo-z estimator that is an extension of the DeepDISC framework. The base DeepDISC network simultaneously detects, segments, and classifies objects in multi-band coadded images. We introduce photo-z capabilities to DeepDISC by adding a redshift estimation Region of Interest head, which produces a photo-z probability distribution function for each detected object. On simulated LSST images, DeepDISC photo-z outperforms traditional catalog-based estimators, in both point estimate and probabilistic metrics. We validate DeepDISC by examining dependencies on systematics including galactic extinction, blending and PSF effects. We also examine the impact of the data quality and the size of the training set and model. We find that the biggest factor in DeepDISC photo-z quality is the signal-to-noise of the imaging data, and see a reduction in photo-z scatter approximately proportional to the image data signal-to-noise. Our code is fully public and integrated in the RAIL photo-z package for ease of use and comparison to other codes at https://github.com/LSSTDESC/rail_deepdisc

astro-ph.IM↗

arXiv2025

Galaxy Clustering with LSST: Effects of Number Count Bias from Blending

The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will survey the southern sky to create the largest galaxy catalog to date, and its statistical power demands an improved understanding of systematic effects such as source overlaps, also known as blending. In this work we study how blending introduces a bias in the number counts of galaxies (instead of the flux and colors), and how it propagates into galaxy clustering statistics. We use the $300\,$deg$^2$ DC2 image simulation and its resulting galaxy catalog (LSST Dark Energy Science Collaboration et al. 2021) to carry out this study. We find that, for a LSST Year 1 (Y1)-like cosmological analyses, the number count bias due to blending leads to small but statistically significant differences in mean redshift measurements when comparing an observed sample to an unblended calibration sample. In the two-point correlation function, blending causes differences greater than 3$σ$ on scales below approximately $10'$, but large scales are unaffected. We fit $Ω_{\rm m}$ and linear galaxy bias in a Bayesian cosmological analysis and find that the recovered parameters from this limited area sample, with the LSST Y1 scale cuts, are largely unaffected by blending. Our main results hold when considering photometric redshift and a LSST Year 5 (Y5)-like sample.

astro-ph.CO↗

arXiv2025

A direct detection method of galaxy intrinsic ellipticity-gravitational shear correlation in non-linear regimes using self-calibration

Intrinsic alignment (IA) of galaxies is a challenging source of contamination in the Cosmic shear (GG) signals. The galaxy intrinsic ellipticity-gravitational shear (IG) correlation is generally the most dominant component of such contamination for cross-correlating redshift bins. The self-calibration (SC) method is one of the most effective techniques to mitigate such contamination from the GG signal. In a photometric survey, the SC method first extracts the galaxy number density-galaxy intrinsic ellipticity (gI) correlation from the observed galaxy-galaxy lensing correlation using the redshift dependence of lens-source pairs. The IG correlation is computed through a scaling relation using the gI correlation and other lensing observables. We extend the SC method beyond the linear regime by modifying its scaling relation which can account for the non-linear galaxy bias model and various IA models. In this study, we provide a framework to detect the IG correlation for the redshift bins for source galaxies for the proposed year 1 survey of the Rubin Legacy Survey of Space and Time (LSST Y1). We tested the method for the tidal alignment and tidal torquing (TATT) model of IA and we found that the scaling relation is accurate within 10$\%$ and 20$\%$ for cross-correlating and auto-correlating redshift bins, respectively. Hence the suppression of IG contamination can be accomplished with a factor of 10 and 5, for cross-correlating and auto-correlating redshift bins, respectively. We tested the method's robustness and found that the suppression of IG contamination by a factor of 5 is still achievable for all combinations of cross-correlating bins even with the inclusion of a moderate amount of uncertainties on IA and bias parameters, respectively. [Abridged]

astro-ph.CO↗

arXiv2025

OpenUniverse2024: A shared, simulated view of the sky for the next generation of cosmological surveys

The OpenUniverse2024 simulation suite is a cross-collaboration effort to produce matched simulated imaging for multiple surveys as they would observe a common simulated sky. Both the simulated data and associated tools used to produce it are intended to uniquely enable a wide range of studies to maximize the science potential of the next generation of cosmological surveys. We have produced simulated imaging for approximately 70 deg$^2$ of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) Wide-Fast-Deep survey and the Nancy Grace Roman Space Telescope High-Latitude Wide-Area Survey, as well as overlapping versions of the ELAIS-S1 Deep-Drilling Field for LSST and the High-Latitude Time-Domain Survey for Roman. OpenUniverse2024 includes i) an early version of the updated extragalactic model called Diffsky, which substantially improves the realism of optical and infrared photometry of objects, compared to previous versions of these models; ii) updated transient models that extend through the wavelength range probed by Roman and Rubin; and iii) improved survey, telescope, and instrument realism based on up-to-date survey plans and known properties of the instruments. It is built on a new and updated suite of simulation tools that improves the ease of consistently simulating multiple observatories viewing the same sky. The approximately 400 TB of synthetic survey imaging and simulated universe catalogs are publicly available, and we preview some scientific uses of the simulations.

astro-ph.CO↗

arXiv2025

Analytical Noise Bias Correction for Precise Weak Lensing Shear Inference

Noise bias is a significant source of systematic error in weak gravitational lensing measurements that must be corrected to satisfy the stringent standards of modern imaging surveys in the era of precision cosmology. This paper reviews the analytical noise bias correction method and provides analytical derivations demonstrating that we can recover shear to its second order using the 'renoising' noise bias correction approach introduced by Metacalibration. We implement this analytical noise bias correction within the AnaCal shear estimation framework and propose several enhancements to the noise bias correction algorithm. We evaluate the improved AnaCal using simulations designed to replicate Rubin LSST imaging data. These simulations feature semi-realistic galaxies and stars, complete with representative distributions of magnitudes and Galactic spatial density. We conduct tests under various observational challenges, including cosmic rays, defective CCD columns, bright star saturation, bleed trails, and spatially variable point spread functions. Our results indicate a multiplicative bias in weak lensing shear recovery of less than a few tenths of a percent, meeting LSST DESC requirements without requiring calibration from external image simulations. Additionally, our algorithm achieves rapid processing, handling one galaxy in less than a millisecond.

astro-ph.CO↗

arXiv2024

Modeling the 3-point correlation function of projected scalar fields on the sphere

One of the main obstacles for the signal extraction of the three point correlation function using photometric surveys, such as the Rubin Observatory Legacy Survey of Space and Time (LSST), will be the prohibitive computation time required for dealing with a vast quantity of sources. Brute force algorithms, which naively scales as $O(N^3)$ with the number of objects, can be further improved with tree methods but not enough to deal with large scale correlations of Rubin's data. However, a harmonic basis decomposition of these higher order statistics reduces the time dramatically, to scale as a two-point correlation function with the number of objects, so that the signal can be extracted in a reasonable amount of time. In this work, we aim to develop the framework to use these expansions within the Limber approximation for scalar (or spin-0) fields, such as galaxy counts, weak lensing convergence or aperture masses. We develop an estimator to extract the signal from catalogs and different phenomenological and theoretical models for its description. The latter includes halo model and standard perturbation theory, to which we add a simple effective field theory prescription based on the short range of non-locality of cosmic fields, significantly improving the agreement with simulated data. In parallel to the modeling of the signal, we develop a code that can efficiently calculate three points correlations of more than 200 million data points (a full sky simulation with Nside=4096) in $\sim$40 minutes, or even less than 10 minutes using an approximation in the searching algorithm, on a single high-performance computing node, enabling a feasible analysis for the upcoming LSST data.

astro-ph.CO↗

arXiv2024

Astrometric Redshifts of Supernovae

Differential Chromatic Refraction (DCR) is caused by the wavelength dependence of our atmosphere's refractive index, which shifts the apparent positions of stars and galaxies and distorts their shapes depending on their spectral energy distributions (SEDs). While this effect is typically mitigated and corrected for in imaging observations, we investigate how DCR can instead be used to our advantage to infer the redshifts of supernovae from multi-band, time-series imaging data. We simulate Type Ia supernovae (SNe Ia) in the proposed Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) Deep Drilling Field (DDF), and evaluate astrometric redshifts. We find that the redshift accuracy improves dramatically with the statistical quality of the astrometric measurements as well as with the accuracy of the astrometric solution. For a conservative choice of a 5-mas systematic uncertainty floor, we find that our redshift estimation is accurate at $z < 0.6$. We then combine our astrometric redshifts with both host galaxy photometric redshifts and supernovae photometric (light-curve) redshifts and show that this considerably improves the overall redshift estimates. These astrometric redshifts will be valuable especially since Rubin will discover a vast number of supernovae for which we will not be able to obtain spectroscopic redshifts.

astro-ph.CO↗

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.

astro-ph.CO↗

arXiv2024

The little coadd that could: Estimating shear from coadded images

Upcoming wide field surveys will have many overlapping epochs of the same region of sky. The conventional wisdom is that in order to reduce the errors sufficiently for systematics-limited measurements, like weak lensing, we must do simultaneous fitting of all the epochs. Using current algorithms this will require a significant amount of computing time and effort. In this paper, we revisit the potential of using coadds for shear measurements. We show on a set of image simulations that the multiplicative shear bias can be constrained below the 0.1% level on coadds, which is sufficient for future lensing surveys. We see no significant differences between simultaneous fitting and coadded approaches for two independent shear codes: Metacalibration and BFD. One caveat of our approach is the assumption of a principled coadd, i.e. the PSF is mathematically well-defined for all the input images. This requires us to reject CCD images that do not fully cover the coadd region. We estimate that the number of epochs that must be rejected for a survey like LSST is on the order of 20%, resulting in a small loss in depth of less than 0.1 magnitudes. We also put forward a cell-based coaddition scheme that meets the above requirements for unbiased weak lensing shear estimation in the context of LSST.

astro-ph.CO↗

arXiv2024

Slitless spectrophotometry with forward modelling: principles and application to atmospheric transmission measurement

In the next decade, many optical surveys will aim to tackle the question of dark energy nature, measuring its equation of state parameter at the permil level. This requires trusting the photometric calibration of the survey with a precision never reached so far, controlling many sources of systematic uncertainties. The measurement of the on-site atmospheric transmission for each exposure, or on average for each season or for the full survey, can help reach the permil precision for magnitudes. This work aims at proving the ability to use slitless spectroscopy for standard star spectrophotometry and its use to monitor on-site atmospheric transmission as needed, for example, by the Vera C. Rubin Observatory Legacy Survey of Space and Time supernova cosmology program. We fully deal with the case of a disperser in the filter wheel, which is the configuration chosen in the Rubin Auxiliary Telescope. The theoretical basis of slitless spectrophotometry is at the heart of our forward model approach to extract spectroscopic information from slitless data. We developed a publicly available software called Spectractor (https://github.com/LSSTDESC/Spectractor) that implements each ingredient of the model and finally performs a fit of a spectrogram model directly on image data to get the spectrum. We show on simulations that our model allows us to understand the structure of spectrophotometric exposures. We also demonstrate its use on real data, solving specific issues and illustrating how our procedure allows the improvement of the model describing the data. Finally, we discuss how this approach can be used to directly extract atmospheric transmission parameters from data and thus provide the base for on-site atmosphere monitoring. We show the efficiency of the procedure on simulations and test it on the limited data set available.

astro-ph.IM↗

arXiv2024

The impact of environment on size: Galaxies are 50% smaller in the Fornax Cluster compared to the field

Size is a fundamental parameter for measuring the growth of galaxies and the role of the environment on their evolution. However, the conventional size definitions used for this purpose are often biased and miss the diffuse, outermost signatures of galaxy growth, including star formation and gas accretion. This issue is addressed by examining low surface brightness truncations or galaxy ``edges'' as a physically motivated tracer of size based on star formation thresholds. Our total sample consists of $\sim900$ galaxies with stellar masses ranging from $10^5 M_{\odot} < M_{\star} < 10^{11} M_{\odot}$. This sample of nearby cluster, group satellite and nearly isolated field galaxies was compiled using multi-band imaging from the Fornax Deep Survey, deep IAC Stripe 82 and Dark Energy Camera Legacy Surveys. We find that the edge radii scale as $R_{\rm edge} \propto M_{\star}^{0.42}$ with a very small intrinsic scatter ($\sim 0.07$ dex). The scatter is driven by the morphology and environment of galaxies. In both the cluster and field, early-type dwarfs are systematically smaller by $\sim20\%$ than the late-types. However, compared to the field galaxies in the Fornax cluster are the most impacted. At a fixed stellar mass, edges in the cluster can be found at $\sim$ 50\% smaller radii and the average stellar surface density at the edges is a factor of two higher $\sim 1\,M_{\odot}$/pc$^2$. Our findings support the rapid removal of loosely bound neutral hydrogen in hot, crowded environments which truncates galaxies outside-in earlier, preventing the formation of more extended sizes and lower density edges. Our results highlight the importance of deep imaging surveys to study the low surface brightness imprints of the large scale structure and environment on galaxy evolution.

astro-ph.GA↗

arXiv2024

Detecting strongly-lensed type Ia supernovae with LSST

Strongly-lensed supernovae are rare and valuable probes of cosmology and astrophysics. Upcoming wide-field time-domain surveys, such as the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST), are expected to discover an order-of-magnitude more lensed supernovae than have previously been observed. In this work, we investigate the cosmological prospects of lensed type Ia supernovae (SNIa) in LSST by quantifying the expected annual number of detections, the impact of stellar microlensing, follow-up feasibility, and how to best separate lensed and unlensed SNIa. We simulate SNIa lensed by galaxies, using the current LSST baseline v3.0 cadence, and find an expected number of 44 lensed SNIa detections per year. Microlensing effects by stars in the lens galaxy are predicted to lower the lensed SNIa detections by $\sim 8 \%$. The lensed events can be separated from the unlensed ones by jointly considering their colours and peak magnitudes. We define a `gold sample' of $\sim 10$ lensed SNIa per year with time delay $> 10$ days, $> 5$ detections before light-curve peak, and sufficiently bright ($m_i < 22.5$ mag) for follow-up observations. In three years of LSST operations, such a sample is expected to yield a $1.5\%$ measurement of the Hubble constant.

astro-ph.CO↗

arXiv2024

Improving Photometric Redshift Estimates with Training Sample Augmentation

Large imaging surveys will rely on photometric redshifts (photo-z's), which are typically estimated through machine learning methods. Currently planned spectroscopic surveys will not be deep enough to produce a representative training sample for LSST, so we seek methods to improve the photo-z estimates that arise from non-representative training samples. Spectroscopic training samples for photo-z's are biased towards redder, brighter galaxies, which also tend to be at lower redshift than the typical galaxy observed by LSST, leading to poor photo-z estimates with outlier fractions nearly 4 times larger than for a representative training sample. In this paper, we apply the concept of training sample augmentation, where we augment simulated non-representative training samples with simulated galaxies possessing otherwise unrepresented features. When we select simulated galaxies with (g-z) color, i-band magnitude and redshift outside the range of the original training sample, we are able to reduce the outlier fraction of the photo-z estimates for simulated LSST data by nearly 50% and the normalized median absolute deviation (NMAD) by 56%. When compared to a fully representative training sample, augmentation can recover nearly 70% of the degradation in the outlier fraction and 80% of the degradation in NMAD. Training sample augmentation is a simple and effective way to improve training samples for photo-z's without requiring additional spectroscopic samples.

astro-ph.IM↗

arXiv2024

Probabilistic Forward Modeling of Galaxy Catalogs with Normalizing Flows

Evaluating the accuracy and calibration of the redshift posteriors produced by photometric redshift (photo-z) estimators is vital for enabling precision cosmology and extragalactic astrophysics with modern wide-field photometric surveys. Evaluating photo-z posteriors on a per-galaxy basis is difficult, however, as real galaxies have a true redshift but not a true redshift posterior. We introduce PZFlow, a Python package for the probabilistic forward modeling of galaxy catalogs with normalizing flows. For catalogs simulated with PZFlow, there is a natural notion of "true" redshift posteriors that can be used for photo-z validation. We use PZFlow to simulate a photometric galaxy catalog where each galaxy has a redshift, noisy photometry, shape information, and a true redshift posterior. We also demonstrate the use of an ensemble of normalizing flows for photo-z estimation. We discuss how PZFlow will be used to validate the photo-z estimation pipeline of the Dark Energy Science Collaboration (DESC), and the wider applicability of PZFlow for statistical modeling of any tabular data.

astro-ph.IM↗

arXiv2024

Machine Learning LSST 3x2pt analyses -- forecasting the impact of systematics on cosmological constraints using neural networks

Validating modeling choices through simulated analyses and quantifying the impact of different systematic effects will form a major computational bottleneck in the preparation for 3$\times$2 analysis with Stage-IV surveys such as Vera Rubin Observatory's Legacy Survey of Space and Time (LSST). We can significantly reduce the computational requirements by using machine learning based emulators, which allow us to run fast inference while maintaining the full realism of the data analysis pipeline. In this paper, we use such an emulator to run simulated 3$\times$2 (cosmic shear, galaxy-galaxy lensing, and galaxy clustering) analyses for mock LSST-Y1/Y3/Y6/Y10 surveys and study the impact of various systematic effects (galaxy bias, intrinsic alignment, baryonic physics, shear calibration and photo-$z$ uncertainties). Closely following the DESC Science Requirement Document (with several updates) our main findings are: {\it a)} The largest contribution to the `systematic error budget' of LSST 3$\times$2 analysis comes from galaxy bias uncertainties, while the contribution of baryonic and shear calibration uncertainties are significantly less important. {\it b)} Tighter constraints on intrinsic alignment and photo-$z$ parameters can improve cosmological constraints noticeably, which illustrates synergies of LSST and spectroscopic surveys. {\it c)} The scale cuts adopted in the DESC SRD may be too conservative and pushing to smaller scales can increase cosmological information significantly. {\it d)} We investigate the impact of photo-$z$ outliers on 3$\times$2 pt analysis and find that we need to determine the outlier fraction to within $5-10\%$ accuracy to ensure robust cosmological analysis. We caution that these findings depend on analysis choices (parameterizations, priors, scale cuts) and can change for different settings.

astro-ph.CO↗

arXiv2023

Cosmology with imaging galaxy surveys: the impact of evolving galaxy bias and magnification

The joint analysis of galaxy clustering and galaxy-shear cross-correlations (galaxy-galaxy lensing) in imaging surveys constitutes one of the main avenues to obtain cosmological information. Analyses from Stage III surveys have assumed constant galaxy bias and weak lensing magnification coefficients within redshift bins. We use simple models of bias and magnification evolution to show that plausible levels of evolution impact cosmological parameter inference significantly for Stage IV surveys and possibly for current datasets as well. The distance weighting factors in galaxy-galaxy lensing cause it to have a different effective mean redshift within a lens bin than galaxy clustering. If the evolution is not included carefully in the theory modeling, this produces an apparent de-correlation of clustering and lensing even on large scales and leads to biased cosmological constraints. The level of bias is sensitive to the redshift and the width of the lens redshift distributions: for magnitude-limited-like samples it is comparable to the statistical precision even for the full DES survey, and exceeds it for Rubin-LSST. We find that ignoring the evolution can lead to approximately $1σ$ biases in the cosmological constraints in future Rubin-LSST analyses. We describe how these effects can be mitigated from the modeling and data sides, in particular by using cross-correlations to constrain the evolution parameters. We also demonstrate that the combination of galaxy clustering and CMB lensing does not suffer from this problem, due to the different distance weighting.

astro-ph.CO↗

arXiv2023

What's the Difference? The potential for Convolutional Neural Networks for transient detection without template subtraction

We present a study of the potential for Convolutional Neural Networks (CNNs) to enable separation of astrophysical transients from image artifacts, a task known as "real-bogus" classification without requiring a template subtracted (or difference) image which requires a computationally expensive process to generate, involving image matching on small spatial scales in large volumes of data. Using data from the Dark Energy Survey, we explore the use of CNNs to (1) automate the "real-bogus" classification, (2) reduce the computational costs of transient discovery. We compare the efficiency of two CNNs with similar architectures, one that uses "image triplets" (templates, search, and difference image) and one that takes as input the template and search only. We measure the decrease in efficiency associated with the loss of information in input finding that the testing accuracy is reduced from 96% to 91.1%. We further investigate how the latter model learns the required information from the template and search by exploring the saliency maps. Our work (1) confirms that CNNs are excellent models for "real-bogus" classification that rely exclusively on the imaging data and require no feature engineering task; (2) demonstrates that high-accuracy (> 90%) models can be built without the need to construct difference images, but some accuracy is lost. Since once trained, neural networks can generate predictions at minimal computational costs, we argue that future implementations of this methodology could dramatically reduce the computational costs in the detection of transients in synoptic surveys like Rubin Observatory's Legacy Survey of Space and Time by bypassing the Difference Image Analysis entirely.

cs.CV↗

arXiv2023

Fat cosmic ray tracks in charge-coupled devices

Cosmic rays are particles from the upper atmosphere which often leave bright spots and trails in images from telescope CCDs. We investigate so-called ``fat" cosmic rays seen in images from Vera C. Rubin Observatory and the Subaru Telescope. These tracks are much wider and brighter than typical cosmic ray tracks, and therefore are more capable of obscuring data in science images. By understanding the origins of these tracks, we can better ensure that they do not interfere with on-sky data. We compare the properties of these tracks to simulated and theoretical models in order to identify both the particles causing these tracks as well as the reason for their excess spread. We propose that the origin of these tracks is cosmic ray protons, which deposit much greater charge in the CCDs than typical cosmic rays due to their lower velocities. The generated charges then repel each other while drifting through the detector, resulting in a track which is much wider than typical tracks.

astro-ph.IM↗