Legacy Survey Of Space And Time Research Articles

Simulating image coaddition with the Nancy Grace Roman Space Telescope – I. Simulation methodology and general results

ABSTRACT The upcoming Nancy Grace Roman Space Telescope will carry out a wide-area survey in the near-infrared. A key science objective is the measurement of cosmic structure via weak gravitational lensing. Roman data will be undersampled, which introduces new challenges in the measurement of source galaxy shapes; a potential solution is to use linear algebra-based coaddition techniques such as imcom that combine multiple undersampled images to produce a single oversampled output mosaic with a desired ‘target’ point spread function (PSF). We present here an initial application of imcom to 0.64 square degrees of simulated Roman data, based on the Roman branch of the Legacy Survey of Space and Time (LSST) Dark Energy Science Collaboration (DESC) Data Challenge 2 (DC2) simulation. We show that imcom runs successfully on simulated data that includes features such as plate scale distortions, chip gaps, detector defects, and cosmic ray masks. We simultaneously propagate grids of injected sources and simulated noise fields as well as the full simulation. We quantify the residual deviations of the PSF from the target (the ‘leakage’), as well as noise properties of the output images; we discuss how the overall tiling pattern as well as Moiré patterns appear in the final leakage and noise maps. We include appendices on interpolation algorithms and the interaction of undersampling with image processing operations that may be of broader applicability. The companion paper (‘Paper II’) explores the implications for weak lensing analyses.

Intrinsic alignment from multiple shear estimates: a first application to data and forecasts for stage IV

ABSTRACT 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 per cent 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 per cent differences in alignment amplitude and ρ = 0.50.

Experience deploying an analysis facility for the Rubin Observatory’s Legacy Survey of Space and Time (LSST) data

Experience deploying an analysis facility for the Rubin Observatory’s Legacy Survey of Space and Time (LSST) data

Impact of blending on weak lensing measurements with the Vera C. Rubin Observatory

Upcoming deep optical surveys such as the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will scan the sky to unprecedented depths and detect billions of galaxies. This amount of detections will however cause the apparent superposition of galaxies on the images, called blending, and generate a new systematic error due to the confusion of sources. As consequences, the measurements of individual galaxies properties such as their redshifts or shapes will be impacted, and some galaxies will not be detected. However, galaxy shapes are key quantities, used to estimate masses of large scale structures, such as galaxy clusters, through weak gravitational lensing. This work presents a new catalog matching algorithm, called friendly, for the detection and characterization of blends in simulated LSST data for the Dark Energy Science Collaboration (DESC) Data Challenge 2. By identifying a specific type of blends, we show that removing them from the data may partially correct the amplitude of the excess surface mass density ΔΣ weak lensing profile that could be biased low by around 20% due to blending. This would result in impacting clusters weak lensing mass estimate and cosmology.

Overview of the distributed image processing infrastructure to produce the Legacy Survey of Space and Time

Overview of the distributed image processing infrastructure to produce the Legacy Survey of Space and Time

Search for Supernova Progenitor Stars with ZTF and LSST

The direct detection of core-collapse supernova (SN) progenitor stars is a powerful way of probing the last stages of stellar evolution. However, detections in archival Hubble Space Telescope images are limited to about one detection per year. Here, we explore whether we can increase the detection rate by using data from ground-based wide-field surveys. Due to crowding and atmospheric blurring, progenitor stars can typically not be identified in preexplosion images alone. Instead, we combine many pre-SN and late-time images to search for the disappearance of the progenitor star. As a proof of concept, we implement our search of ZTF data. For a few hundred images, we achieve limiting magnitudes of ∼23 mag in the g and r bands. However, no progenitor stars or long-lived outbursts are detected for 29 SNe within z ≤ 0.01, and the ZTF limits are typically several magnitudes less constraining than detected progenitors in the literature. Next, we estimate progenitor detection rates for the Legacy Survey of Space and Time (LSST) with the Vera C. Rubin telescope by simulating a population of nearby SNe. The background from bright host galaxies reduces the nominal LSST sensitivity by, on average, 0.4 mag. Over the 10 yr survey, we expect the detection of ∼50 red supergiant progenitors and several yellow and blue supergiants. The progenitors of Type Ib and Ic SNe will be detectable if they are brighter than −4.7 or −4.0 mag in the LSST i band, respectively. In addition, we expect the detection of hundreds of pre-SN outbursts depending on their brightness and duration.

Kilonova-Targeting Lightcurve Classification for Wide Field Survey Telescope

With the enhancement of the sensitivity of gravitational wave (GW) detectors and capabilities of large survey facilities, such as the Vera Rubin Observatory Legacy Survey of Space and Time (LSST) and the 2.5 m Wide Field Survey Telescope (WFST), we now have the potential to detect an increasing number of distant kilonova (KN). However, distinguishing KN from the plethora of detected transients in ongoing and future follow-up surveys presents a significant challenge. In this study, our objective is to establish an efficient classification mechanism tailored for the follow-up survey conducted by WFST, with a specific focus on identifying KN associated with GW. We employ a novel temporal convolutional neural network architecture, trained using simulated multi-band photometry lasting for 3 days by WFST, accompanied by contextual information, i.e., luminosity distance information by GW. By comparison of the choices of contextual information, we can reach 95% precision and 94% recall for our best model. It also performs good validation of photometry data on AT2017gfo and AT2019npv. Furthermore, we investigate the ability of the model to distinguish KN in a GW follow-up survey. We conclude that there is over 80% probability that we can capture true KN in 20 selected candidates among ∼250 detected astrophysical transients that have passed the real–bogus filter and cross-matching.

Cosmology from weak lensing, galaxy clustering, CMB lensing, and tSZ – I. 10 × 2pt modelling methodology

ABSTRACT The overlap of galaxy surveys and cosmic microwave background (CMB) experiments presents an ideal opportunity for joint cosmological data set analyses. In this paper we develop a halo model-based method for the first joint analysis combining these two experiments using 10 correlated two-point functions (10 × 2pt) derived from galaxy position, galaxy shear, CMB lensing convergence, and Compton-y fields. We explore this method using the Vera Rubin Observatory Legacy Survey of Space and Time (LSST) and the Simons Observatory (SO) as examples. We find such LSS × CMB joint analyses lead to significant improvement in Figure-of-Merit of Ωm and S8 over the constraints from using LSS-only probes within Λ cold dark matter (ΛCDM) model. We identify that the shear–y and y–y correlations are the most valuable additions when thermal Sunyaev–Zel’dolvich (tSZ) is included. We further identify the dominant sources of halo model uncertainties in the small-scale modelling, and investigate the impact of halo self-calibration due to the inclusion of small-scale tSZ information.

Synthetic Detections of Interstellar Objects with the Rubin Observatory Legacy Survey of Space and Time

The discovery of two interstellar objects passing through the solar system, 1I/‘Oumuamua and 2I/Borisov, implies that a galactic population exists with a spatial number density of order ∼0.1 au−3. The forthcoming Rubin Observatory Legacy Survey of Space and Time (LSST) has been predicted to detect more asteroidal interstellar objects like 1I/‘Oumuamua. We apply recently developed methods to simulate a suite of galactic populations of interstellar objects with a range of assumed kinematics, albedos, and size–frequency distributions (SFDs). We incorporate these populations into the objectsInField algorithm, which simulates detections of moving objects by an arbitrary survey. We find that the LSST should detect between ∼0 and 70 asteroidal interstellar objects every year (assuming the implied number density), with sensitive dependence on the SFD slope and characteristic albedo of the host population. The apparent rate of motion on the sky—along with the associated trailing loss—appears to be the largest barrier to detecting interstellar objects. Specifically, a relatively large number of synthetic objects would be detectable by the LSST if not for their rapid sky motion (>0.°5 day−1). Therefore, algorithms that could successfully link and detect rapidly moving objects would significantly increase the number of interstellar object discoveries with the LSST (and in general). The mean diameter of detectable, inactive interstellar objects ranges from ∼50 to 600 m and depends sensitively on the SFD slope and albedo.

Enabling discoveries of Solar System objects in large alert data streams

Context. With the advent of large-scale astronomical surveys, such as the Zwicky Transient Facility (ZTF) and the forthcoming Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST), the number of alerts generated by transient, variable, and moving astronomical objects is growing rapidly, reaching millions of alerts per night. Concerning the minor planets of the Solar System, their identification requires linking the alerts for many observations over a potentially lengthy period of time, leading to a very large combinatorial number. Aims. The goal is to demonstrate how a third-party module dedicated to the identification of new minor planets of the Solar System can be integrated with the Fink alert broker real-time operations, which deals with massive alert data streams produced by large-scale surveys. Methods. Our analysis takes advantage of the scientific surplus brought on by the Fink alert broker classification capabilities to first reduce the 111 275 131 processed alerts from ZTF between November 2019 and December 2022 (755 observation nights) to only 389 530 new Solar System alert candidates over the same period. We implemented a simple, yet pedagogical linking algorithm called Fink-FAT to create trajectory candidates in real time from alert data and extract orbital parameters. The analysis was validated on ZTF alert packets linked to confirmed Solar System objects from the Minor Planet Center (MPC) database. Finally, the candidates were confronted with follow-up observations. Results. Between November 2019 and December 2022, Fink-FAT extracted 327 new orbits from candidate Solar System objects at the time of the observations, of which 65 had still remained unreported in the MPC database as of March 2023. After two late follow-up observation campaigns of six orbit candidates, four were associated with known minor planets of the Solar System, and two still remain unknown. In terms of performance, Fink-FAT took under 3 h to link alerts into trajectory candidates and to extract the orbital elements over the three years of Fink data, using a modest hardware configuration. Conclusions. Despite a much lower efficiency than present linking algorithms, Fink-FAT reaches a high level of purity in reconstructing orbits and it runs fast, making it suitable for the real-time discovery of new minor planets. Fink-FAT is deployed in the Fink broker and analyzes, in real time, the alert data from the ZTF survey by regularly extracting new candidates for Solar System objects. Tests of scalability also show that Fink-FAT is capable of handling the even larger volume of alert data that will be sent by the Rubin Observatory’s real-time difference image analysis processing.

Spectral Variability Studies in Active Galactic Nuclei: Exploring Continuum and Emission Line Regions in the Age of LSST and JWST

The investigation of emission line regions within active galaxies (AGNs) has a rich and extensive history, now extending to the use of AGNs and quasars as “standardizable” cosmological indicators, shedding light on the evolution of our universe. As we enter the era of advanced observatories, such as the successful launch of the JWST and the forthcoming Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST), the landscape of AGN exploration across cosmic epochs is poised for exciting advancements. In this work, we delve into recent developments in AGN variability research, anticipating the substantial influx of data facilitated by LSST. The article highlights recent strides made by the AGN Polish Consortium in their contributions to LSST. The piece emphasizes the role of quasars in cosmology, dissecting the intricacies of their calibration as standard candles. The primary focus centers on the relationship between the broad-line region size and luminosity, showcasing recent breakthroughs that enhance our comprehension of this correlation. These breakthroughs encompass a range of perspectives, including spectroscopic analyses, photoionization modeling, and collaborative investigations with other cosmological tools. The study further touches on select studies, underlining how the synergy of theoretical insights and advancements in observational capabilities has yielded deeper insights into these captivating cosmic entities.

Forecasting the power of higher order weak-lensing statistics with automatically differentiable simulations

Aims. We present the fully differentiable physical Differentiable Lensing Lightcone (DLL) model, designed for use as a forward model in Bayesian inference algorithms that require access to derivatives of lensing observables with respect to cosmological parameters. Methods. We extended the public FlowPM N-body code, a particle-mesh N-body solver, while simulating the lensing lightcones and implementing the Born approximation in the Tensorflow framework. Furthermore, DLL is aimed at achieving high accuracy with low computational costs. As such, it integrates a novel hybrid physical-neural (HPN) parameterization that is able to compensate for the small-scale approximations resulting from particle-mesh schemes for cosmological N-body simulations. We validated our simulations in the context of the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST) against high-resolution κTNG-Dark simulations by comparing both the lensing angular power spectrum and multiscale peak counts. We demonstrated its ability to recover lensing Cℓ up to a 10% accuracy at ℓ = 1000 for sources at a redshift of 1, with as few as ∼0.6 particles per Mpc h−1. As a first-use case, we applied this tool to an investigation of the relative constraining power of the angular power spectrum and peak counts statistic in an LSST setting. Such comparisons are typically very costly as they require a large number of simulations and do not scale appropriately with an increasing number of cosmological parameters. As opposed to forecasts based on finite differences, these statistics can be analytically differentiated with respect to cosmology or any systematics included in the simulations at the same computational cost of the forward simulation. Results. We find that the peak counts outperform the power spectrum in terms of the cold dark matter parameter, Ωc, as well as on the amplitude of density fluctuations, σ8, and the amplitude of the intrinsic alignment signal, AIA.

Robustness of baryon acoustic oscillations measurements with photometric redshift uncertainties

ABSTRACT We investigate the robustness of baryon acoustic oscillations (BAO) measurements with a photometric galaxy sample using mock galaxy catalogues with various sizes of photometric redshift (photo-z) uncertainties. We first conduct the robustness of BAO measurements, assuming we have a perfect knowledge of photo-z uncertainties. We find that the BAO shift parameter α can be constrained in an unbiased manner even for 3 per cent photometric redshift uncertainties up to z ∼ 1. For instance, α = 1.006 ± 0.078 with 95 per cent confidence level is obtained from 3 per cent photo-z uncertainty data at z = 1.03 using the sample of M* ≥ 1010.25 M⊙ h−2. We also find that a sparse galaxy sample, e.g. <2 × 10−4 [h Mpc−1]3, causes additional noise in the covariance matrix calculation and can bias the constraint on α. Following this, we look into the scenario where incorrect photometric redshift uncertainties are assumed in the fitting model. We find that underestimating the photo-z uncertainty leads to a degradation in the constraining power on α. However, the constrained value of α is not biased. We also quantify the constraining power on Ωm0 assuming the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST)-like covariance and find that the 95 per cent confidence level is σ(Ωm0) ∼ 0.03–0.05 corresponding to the photo-z uncertainties of 1–3 per cent, respectively. Finally, we examine whether the skewness in the photometric redshift can bias the constraint on α and confirm that the constraint on α is unbiased, even assuming a Gaussian photo-z uncertainty in our model.

Paying attention to astronomical transients: introducing the time-series transformer for photometric classification

Abstract Future surveys such as the Legacy Survey of Space and Time (LSST) of the Vera C. Rubin Observatory will observe an order of magnitude more astrophysical transient events than any previous survey before. With this deluge of photometric data, it will be impossible for all such events to be classified by humans alone. Recent efforts have sought to leverage machine learning methods to tackle the challenge of astronomical transient classification, with ever improving success. Transformers are a recently developed deep learning architecture, first proposed for natural language processing, that have shown a great deal of recent success. In this work, we develop a new transformer architecture, which uses multihead self-attention at its core, for general multivariate time-series data. Furthermore, the proposed time-series transformer architecture supports the inclusion of an arbitrary number of additional features, while also offering interpretability. We apply the time-series transformer to the task of photometric classification, minimizing the reliance of expert domain knowledge for feature selection, while achieving results comparable with state-of-the-art photometric classification methods. We achieve a logarithmic-loss of 0.507 on imbalanced data in a representative setting using data from the Photometric LSST Astronomical Time-Series Classification Challenge. Moreover, we achieve a micro-averaged receiver-operating-characteristic area under curve of 0.98 and micro-averaged precision–recall area under curve of 0.87.

Point spread function modelling for astronomical telescopes: a review focused on weak gravitational lensing studies

The accurate modelling of the point spread function (PSF) is of paramount importance in astronomical observations, as it allows for the correction of distortions and blurring caused by the telescope and atmosphere. PSF modelling is crucial for accurately measuring celestial objects’ properties. The last decades have brought us a steady increase in the power and complexity of astronomical telescopes and instruments. Upcoming galaxy surveys like Euclid and Legacy Survey of Space and Time (LSST) will observe an unprecedented amount and quality of data. Modelling the PSF for these new facilities and surveys requires novel modelling techniques that can cope with the ever-tightening error requirements. The purpose of this review is threefold. Firstly, we introduce the optical background required for a more physically motivated PSF modelling and propose an observational model that can be reused for future developments. Secondly, we provide an overview of the different physical contributors of the PSF, which includes the optic- and detector-level contributors and atmosphere. We expect that the overview will help better understand the modelled effects. Thirdly, we discuss the different methods for PSF modelling from the parametric and non-parametric families for ground- and space-based telescopes, with their advantages and limitations. Validation methods for PSF models are then addressed, with several metrics related to weak-lensing studies discussed in detail. Finally, we explore current challenges and future directions in PSF modelling for astronomical telescopes.

Lensed Type Ia supernovae in light of SN Zwicky and iPTF16geu

ABSTRACT Strong gravitationally lensed supernovae (glSNe) are a powerful probe to obtain a measure of the expansion rate of the Universe, but they are also extremely rare. To date, only two glSNe with multiple images strongly lensed by galaxies have been found, but their short time delays make them unsuitable for cosmography. Here, we simulate a realistic catalogue of lensed supernovae and study the characteristics of the population of detectable systems for different surveys. Compared to previous studies, our simulations also account for the effect of microlensing and its impact on the glSNe yields. We show that the properties of glSNe in shallow surveys (such as the Zwicky Transient Facility) are determined by the need for large magnifications, which favours systems of four images with short time delays and low image separations. This picture is consistent with the properties of iPTF16geu and SN Zwicky, but is not representative of the population found in deeper surveys, which are limited by the volume of the Universe that is strongly lensed. For deeper surveys, such as the Legacy Survey of Space and Time (LSST), glSNe show longer time delays and greater angular separations, and the inclusion of microlensing results in 8  per cent of glSNe becoming demagnified under the detection threshold. In the 10 yr of the survey LSST should be able to find ≈180 systems, of which 70 will be suited for cosmography enabling a ≈1.2  per cent precision H0 measurement with LSST glSNe.

A multimessenger model for neutron star–black hole mergers

ABSTRACT We present a semi-analytic model for predicting kilonova light curves from the mergers of neutron stars with black holes (NSBH). The model is integrated into the mosfit platform, and can generate light curves from input binary properties and nuclear equation-of-state considerations, or incorporate measurements from gravitational wave (GW) detectors to perform multimessenger parameter estimation. The rapid framework enables the generation of NSBH kilonova distributions from binary populations, light curve predictions from GW data, and statistically meaningful comparisons with an equivalent binary neutron star (BNS) model in mosfit. We investigate a sample of kilonova candidates associated with cosmological short gamma-ray bursts, and demonstrate that they are broadly consistent with being driven by NSBH systems, though most have limited data. We also perform fits to the very well sampled GW170817, and show that the inability of an NSBH merger to produce lanthanide-poor ejecta results in a significant underestimate of the early (≲2 d) optical emission. Our model indicates that NSBH-driven kilonovae may peak up to a week after merger at optical wavelengths for some observer angles. This demonstrates the need for early coverage of emergent kilonovae in cases where the GW signal is either ambiguous or absent; they likely cannot be distinguished from BNS mergers by the light curves alone from ∼2 d after the merger. We also discuss the detectability of our model kilonovae with the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST).

Negative Lags on the Viscous Timescale in Quasar Photometry and Prospects for Detecting More with LSST

The variability of quasar light curves can be used to study the structure of quasar accretion disks. For example, continuum reverberation mapping uses delays between variability in short and long wavelength bands (short lags) to measure the radial extent and temperature profile of the disk. Recently, a potential reverse lag, where variations in shorter wavelength bands lag the longer wavelength bands at the much longer viscous timescale, was detected for Fairall 9. Inspired by this detection, we derive a timescale for these long negative lags from fluctuation propagation models and recent simulations. We use this timescale to forecast our ability to detect long lags using the Vera Rubin Legacy Survey of Space and Time (LSST). After exploring several methods, including the interpolated cross-correlation function, a Von-Neumann estimator, javelin, and a maximum-likelihood Fourier method, we find that our two main methods, javelin and the maximum-likelihood method, can together detect long lags of up to several hundred days in mock LSST light curves. Our methods work best on proposed LSST cadences with long season lengths, but can also work for the current baseline LSST cadence, especially if we add observations from other optical telescopes during seasonal gaps. We find that LSST has the potential to detect dozens to hundreds of additional long lags. Detecting these long lags can teach us about the vertical structure of quasar disks and how it scales with different quasar properties.

Rubin Observatory LSST Transients and Variable Stars Roadmap

The Vera C. Rubin Legacy Survey of Space and Time (LSST) holds the potential to revolutionize time domain astrophysics, reaching completely unexplored areas of the Universe and mapping variability time scales from minutes to a decade. To prepare to maximize the potential of the Rubin LSST data for the exploration of the transient and variable Universe, one of the four pillars of Rubin LSST science, the Transient and Variable Stars Science Collaboration, one of the eight Rubin LSST Science Collaborations, has identified research areas of interest and requirements, and paths to enable them. While our roadmap is ever-evolving, this document represents a snapshot of our plans and preparatory work in the final years and months leading up to the survey’s first light.

Uchuu–ν2GC galaxies and AGN: cosmic variance forecasts of high-redshift AGN for JWST, Euclid, and LSST

ABSTRACT Measurements of the luminosity function of active galactic nuclei (AGN) at high redshift (z ≳ 6) are expected to suffer from field-to-field variance, including cosmic and Poisson variances. Future surveys, such as those from the Euclid telescope and JWST, will also be affected by field variance. We use the Uchuu simulation, a state-of-the-art cosmological N-body simulation with 2.1 trillion particles in a volume of 25.7 Gpc3, combined with a semi-analytic galaxy and AGN formation model, to generate the Uchuu–ν2GC catalogue, publicly available, that allows us to investigate the field-to-field variance of the luminosity function of AGN. With this Uchuu–ν2GC model, we quantify the cosmic variance as a function of survey area, AGN luminosity, and redshift. In general, cosmic variance decreases with increasing survey area and decreasing redshift. We find that at z ∼ 6 − 7, the cosmic variance depends weakly on AGN luminosity. This is because the typical mass of dark matter haloes in which AGN reside does not significantly depend on luminosity. Due to the rarity of AGN, Poisson variance dominates the total field-to-field variance, especially for bright AGN. We also examine the effect of parameters related to galaxy formation physics on the field variance. We discuss uncertainties present in the estimation of the faint-end of the AGN luminosity function from recent observations, and extend this to make predictions for the expected number of AGN and their variance for upcoming observations with Euclid, JWST, and the Legacy Survey of Space and Time (LSST).