Rubin Observatory Research Articles

Ultra-long-period Cepheids as Standard Candles from Gaia to Rubin-LSST

An analysis of the properties of ultra-long-period Cepheids (ULPs) could significantly contribute to understanding the Hubble constant tension, e.g., the current discrepancy between determinations based on local distance indicators and those relying on cosmic microwave background measurements. These highly luminous variables are observable beyond 100 Mpc, so if they were confirmed to behave as standard candles, they would allow us a direct measurement of cosmological distances without any secondary distance indicator, thus reducing potential systematic errors in the calibration of the cosmic distance scale. This paper presents an analysis of the largest known sample of 73 ULPs, including 15 objects in nearby galaxies, with new accurate and homogeneous photometry obtained by Gaia DR3, and a new object, in our Galaxy, identified as a long-period variable in Gaia DR3, but recently reclassified as a ULP. The obtained results suggest that, by improving photometric accuracy, the ULP period–Wesenheit relation shows a smaller dispersion than that obtained in literature and is in better agreement with the classical Cepheid (CC) one, supporting the hypothesis that ULPs are the extension of the CCs at higher period, mass, and luminosity. However, to reach this aim, it is necessary to enrich the sample with high-quality data. The Rubin Observatory Legacy Survey of Space and Time (Rubin-LSST) survey offers the possibility to achieve this thanks to its photometric characteristics and time extension. In particular, we will explore the capabilities of the Rubin-LSST survey to recover ULP theoretical light curves by using a new tool called PulsationStarRecovery, built by our group for this type of analysis.

Deep drilling in the time domain with DECam II: characterizing the light curves of candidates in the extragalactic fields

Abstract In this second paper on the DECam deep drilling field (DDF) program we release 2,020 optical gri-band light curves for transients and variables in the extragalactic COSMOS and ELAIS fields based on time series observations with a 3-day cadence from semester 2021A through 2023A. In order to demonstrate the wide variety of time domain events detected by the program and encourage others to use the data set, we characterize the sample by presenting a brief analysis of the light curve parameters such as time span, amplitude, and peak brightness. We also present preliminary light curve categorizations, and identify potential stellar variables, active galactic nuclei, tidal disruption events, supernovae (such as Type Ia, Type IIP, superluminous, and gravitationally lensed supernovae), and fast transients. Where relevant, the number of identified transients is compared to the predictions of the original proposal. We also discuss the challenges of analyzing DDF data in the context of the upcoming Vera C. Rubin Observatory and its Legacy Survey of Space and Time, which will include DDFs. Images from the DECam DDF program are available without proprietary period and the light curves presented in this work are publicly available for analysis.

Constrain spatial curvature and dark energy with strong lenses and complementary probes: a forecast for next-generation surveys

Abstract Inferring spatial curvature of the Universe with high-fidelity is a longstanding interest in cosmology. However, the strong degeneracy between dark energy equation-of-state parameter w and curvature density parameter ΩK has always been a hurdle for precision measurements of curvature from late-universe probes. With the imminent commissioning of Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST), we demonstrate for the first time, using simulations of stage-IV surveys, the crucial role of time-delay distances from strong gravitational lenses in breaking this degeneracy. Our findings suggest that in non-flat owCDM model, while strong lensing data alone only yield a ΩK constraint at $\sim \mathcal {O}(10^{-1})$ level, the integration with SNe Ia and BAO data breaks the w-ΩK degeneracy and refines the ΩK constraint to $\sim \mathcal {O}(10^{-2})$. This surpasses the constraints typically derived from SNe Ia Hubble diagrams and BAO data and is comparable to the constraints obtained from Planck Primary CMB data. Additionally, we present a non-parametric approach using Gaussian Process to avoid parameter-dependency of the expansion history H(z) and achieve similar $\mathcal {O}(10^{-2})$ level constraint on ΩK. This study demonstrates the significant potential of strong gravitational lenses and Stage-IV surveys like LSST to achieve high-fidelity, independent constraints on ΩK, contributing to our understanding of the Universe’s geometry and the dynamics of dark energy.

A Cohesive Deep Drilling Field Strategy for LSST Cosmology

The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will image billions of astronomical objects in the wide–fast–deep primary survey and in a set of minisurveys including intensive observations of a group of deep drilling fields (DDFs). The DDFs are a critical piece of three key aspects of the LSST Dark Energy Science Collaboration (DESC) cosmological measurements: they provide a required calibration for photometric redshifts (photo-z) and weak gravitational lensing (WL) measurements and they directly contribute to cosmological constraints from the most distant Type Ia supernovae (SNe Ia). We present a set of cohesive DDF strategies fulfilling science requirements relevant to DESC and following the guidelines of the Survey Cadence Optimization Committee. We propose a method to estimate the observing strategy parameters and we perform simulations of the corresponding surveys. We define a set of metrics for each science case to assess the performance of the proposed observing strategies. We show that the most promising results are achieved with deep rolling surveys characterized by two sets of fields: ultradeep fields (z ≲ 1.1) observed at a high cadence with a large number of visits over a limited number of seasons, and deep fields (z ≲ 0.7), observed with a cadence of ∼3 nights for 10 yr. These encouraging results should be confirmed with realistic simulations using the LSST scheduler. A DDF budget of ∼8.5% is required to design observing strategies satisfying all the cosmological requirements. A lower DDF budget leads to surveys that either do not fulfill the photo-z/WL requirements or are not optimal for SN Ia cosmology.

How Meteor Showers Can Guide the Search for Long-period Comets

With orbital periods longer than 200 yr, most long-period comets (LPCs) remain undiscovered until they are in-bound toward perihelion. The comets that pass close to Earth’s orbit are potentially hazardous objects. Those with orbital periods up to ∼4000 yr tend to have passed close to Earth’s orbit in a previous orbit and produced a meteoroid stream dense enough to be detected at Earth as a meteor shower. In anticipation of Rubin Observatory’s Legacy Survey of Space and Time (LSST), we investigate how these meteor showers can guide dedicated searches for their parent comets. Assuming search parameters informed by LSST, we calculated where the 17 known parent bodies of LPC meteor showers would have been discovered based on a cloud of synthetic comets generated from the shower properties as measured at Earth. We find that the synthetic comets predict the on-sky location of the parent comets at the time of their discovery. The parent comet’s location on average would have been 1.°51 ± 1.°19 from a line fit through the synthetic comet cloud. The difference between the heliocentric distance of the parent and mean heliocentric distance of synthetic comets on the line was 2.09 ± 1.89 au for comets with unknown absolute nuclear magnitudes and 0.96 ± 0.80 au for comets with known absolute nuclear magnitudes. We applied this method to the σ-Hydrids, the proposed meteor shower of comet Nishimura, and found that it successfully matched the pre-covery location of this comet 8 months prior to Nishimura’s discovery.

HOLISMOKES. XI. Evaluation of supervised neural networks for strong-lens searches in ground-based imaging surveys

While supervised neural networks have become state of the art for identifying the rare strong gravitational lenses from large imaging data sets, their selection remains significantly affected by the large number and diversity of non-lens contaminants. This work evaluates and compares systematically the performance of neural networks in order to move towards a rapid selection of galaxy-scale strong lenses with minimal human input in the era of deep, wide-scale surveys. We used multiband images from PDR2 of the Hyper-Suprime Cam (HSC) Wide survey to build test sets mimicking an actual classification experiment, with 189 securely-identified strong lenses from the literature over the HSC footprint and 70\,910 non-lens galaxies in COSMOS covering representative lens-like morphologies. Multiple networks were trained on different sets of realistic strong-lens simulations and non-lens galaxies, with various architectures and data preprocessing, mainly using the deepest $gri$-bands. Most networks reached excellent area under the Receiver Operating Characteristic (ROC) curves on the test set of 71,099 objects, and we determined the ingredients to optimize the true positive rate for a total number of false positives equal to zero or 10 $ and TPR$_ $). The overall performances strongly depend on the construction of the ground-truth training data and they typically, but not systematically, improve using our baseline residual network architecture presented in HOLISMOKES VI. TPR$_ $ tends to be higher for ResNets (simeq 10--40<!PCT!>) compared to AlexNet-like networks or G-CNNs. Improvements are found when (1) applying random shifts to the image centroids, (2) using square-root scaled images to enhance faint arcs, (3) adding $z$-band to the otherwise used $gri$-bands, or (4) using random viewpoints of the original images. In contrast, we find no improvement when adding $g - i$ difference images (where alpha is a tuned constant) to subtract emission from the central galaxy. The most significant gain is obtained with committees of networks trained on different data sets, with a moderate overlap between populations of false positives. Nearly-perfect invariance to image quality can be achieved by using realistic PSF models in our lens simulation pipeline, and by training networks either with large number of bands, or jointly with the PSF and science frames. Overall, we show the possibility to reach a $ as high as 60<!PCT!> for the test sets under consideration, which opens promising perspectives for pure selection of strong lenses without human input using the Rubin Observatory and other forthcoming ground-based surveys.

Dark energy reconstruction analysis with artificial neural networks: Application on simulated Supernova Ia data from Rubin Observatory

In this paper, we present an analysis of Supernova Ia (SNIa) distance moduli (μ(z)) and dark energy using an Artificial Neural Network (ANN) reconstruction based on LSST simulated three-year SNIa data. The ANNs employed in this study utilize genetic algorithms for hyperparameter tuning and Monte Carlo Dropout for predictions. Our ANN reconstruction architecture is capable of modeling both the distance moduli and their associated statistical errors given redshift values. We compare the performance of the ANN-based reconstruction with two theoretical dark energy models: ΛCDM and Chevallier–Linder–Polarski (CPL). Bayesian analysis is conducted for these theoretical models using the LSST simulations and compared with observations from Pantheon and Pantheon+ SNIa real data. We demonstrate that our model-independent ANN reconstruction is consistent with both theoretical models. Performance metrics and statistical tests reveal that the ANN produces distance modulus estimates that align well with the LSST dataset and exhibit only minor discrepancies with ΛCDM and CPL.

Spectroscopic analysis of the strongly lensed SN Encore: Constraints on cosmic evolution of Type Ia supernovae

Abstract Strong gravitational lensing magnifies the light from a background source, allowing us to study these sources in detail. Here, we study the spectra of a z = 1.95 lensed Type Ia supernova SN Encore for its brightest Image A, taken 39 days apart. We infer the spectral age with template matching using the supernova identification (SNID) software and find the spectra to be at 29.0 ±5.0 and 37.4 ±2.8 rest-frame days post maximum respectively, consistent with separation in the observer frame after accounting for time-dilation. Since SNe Ia measure dark energy properties by providing relative distances between low- and high-z SNe, it is important to test for evolution of spectroscopic properties. Comparing the spectra to composite low-z SN Ia spectra, we find strong evidence for similarity between the local sample and SN Encore. The line velocities of common SN Ia spectral lines, Si II 6355 and Ca II NIR triplet are consistent with the distribution for the low-z sample as well as other lensed SNe Ia, e.g. iPTF16geu (z = 0.409)and SN H0pe (z = 1.78). The consistency between the low-z sample and lensed SNe at high-z suggests no obvious cosmic evolution demonstrating their using as high-z distance indicators, though this needs to be confirmed/refuted via a larger sample. We also find that the spectra of SN Encore match the predictions for explosion models very well. With future large samples of lensed SNe Ia e.g. with the Vera C. Rubin Observatory, spectra at such late phases will be important to distinguish between different explosion scenarios.

The Dark Energy Survey Supernova Program: Cosmological Analysis and Systematic Uncertainties

We present the full Hubble diagram of photometrically classified Type Ia supernovae (SNe Ia) from the Dark Energy Survey supernova program (DES-SN). DES-SN discovered more than 20,000 SN candidates and obtained spectroscopic redshifts of 7000 host galaxies. Based on the light-curve quality, we select 1635 photometrically identified SNe Ia with spectroscopic redshift 0.10 < z < 1.13, which is the largest sample of supernovae from any single survey and increases the number of known z > 0.5 supernovae by a factor of 5. In a companion paper, we present cosmological results of the DES-SN sample combined with 194 spectroscopically classified SNe Ia at low redshift as an anchor for cosmological fits. Here we present extensive modeling of this combined sample and validate the entire analysis pipeline used to derive distances. We show that the statistical and systematic uncertainties on cosmological parameters are σΩM,stat+sysΛCDM= 0.017 in a flat ΛCDM model, and (σΩM,σw)stat+syswCDM = (0.082, 0.152) in a flat wCDM model. Combining the DES SN data with the highly complementary cosmic microwave background measurements by Planck Collaboration reduces by a factor of 4 uncertainties on cosmological parameters. In all cases, statistical uncertainties dominate over systematics. We show that uncertainties due to photometric classification make up less than 10% of the total systematic uncertainty budget. This result sets the stage for the next generation of SN cosmology surveys such as the Vera C. Rubin Observatory's Legacy Survey of Space and Time.

The Power of High-precision Broadband Photometry: Tracing the Milky Way Density Profile with Blue Horizontal Branch Stars in the Dark Energy Survey

Blue horizontal branch stars (BHBs), excellent distant tracers for probing the Milky Way’s halo density profile, are distinguished in the g−r0 versus (i − z)0 color space from another class of stars, blue straggler stars. We develop a Bayesian mixture model to classify BHBs using high-precision photometry data from the Dark Energy Survey Data Release 2 (DES DR2). We select ∼2100 highly probable BHBs based on their griz photometry and the associated uncertainties, and we use these stars to map the stellar halo over the Galactocentric radial range 20 kpc ≲ R ≲ 70 kpc. After excluding known stellar overdensities, we find that the number density n ⋆ of BHBs can be represented by a power-law density profile n ⋆ ∝ R −α with an index of α=4.34−0.12+0.13±0.52 , consistent with existing literature values. In addition, we examine the impact of systematic errors and the spatial inhomogeneity on the fitted density profile. Our work demonstrates the effectiveness of high-precision griz photometry in selecting BHBs. The upcoming photometric survey from the Rubin Observatory, expected to reach depths 2–3 mag greater than DES during its 10 yr mission, will enable us to investigate the density profile of the Milky Way’s halo out to the virial radius, unraveling the complex processes of formation and evolution in our Galaxy.

Machine learning methods for automated interstellar object classification with LSST

The Legacy Survey of Space and Time (LSST), to be conducted with the Vera C. Rubin Observatory, is poised to revolutionize our understanding of the Solar System by providing an unprecedented wealth of data on various objects, including the elusive interstellar objects (ISOs). Detecting and classifying ISOs is crucial for studying the composition and diversity of materials from other planetary systems. However, the rarity and brief observation windows of ISOs, coupled with the vast quantities of data to be generated by LSST, create significant challenges for their identification and classification. This study aims to address these challenges by exploring the application of machine learning algorithms to the automated classification of ISO tracklets in simulated LSST data. We employed various machine learning algorithms, including random forests (RFs), stochastic gradient descent (SGD), gradient boosting machines (GBMs), and neural networks (NNs), to classify ISO tracklets in simulated LSST data. Our results demonstrate that GBM and RF algorithms outperform SGD and NN algorithms in accurately distinguishing ISOs from other Solar System objects. RF analysis shows that many derived Digest2 values are more important than direct observables (right ascension, declination, and magnitude) in classifying ISOs from the LSST tracklets. The GBM model achieves the highest precision, recall, and F1 score, with values of 0.9987, 0.9986, and 0.9987, respectively. These findings lay the foundation for the development of an efficient and robust automated system for ISO discovery using LSST data, paving the way for a deeper understanding of the materials and processes that shape planetary systems beyond our own. The integration of our proposed machine learning approach into the LSST data processing pipeline will optimize the survey's potential for identifying these rare and valuable objects, enabling timely follow-up observations and further characterization.

Third-order intrinsic alignment of SDSS BOSS LOWZ galaxies

Cosmic shear is a powerful probe of cosmology, but it is affected by the intrinsic alignment (IA) of galaxy shapes with the large-scale structure. Upcoming surveys such as Euclid and Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) require an accurate understanding of IA, particularly for higher-order cosmic shear statistics that are vital for extracting the most cosmological information. In this paper, we report the first detection of third-order IA correlations using the LOWZ galaxy sample from the Sloan Digital Sky Survey (SDSS) Baryon Oscillation Spectroscopic Survey (BOSS). We compare our measurements with predictions from the MICE cosmological simulation and an analytical model inspired by the Non-linear Linear Alignment (NLA) model and informed by second-order correlations. We also explore the dependence of the third-order correlation on the galaxies' luminosity. We find that the amplitude $A_ IA $ of the IA signal is non-zero at the $4.7 level for scales between Mpc Mpc Mpc Mpc $ the inferred $A_ IA $ agrees both with the prediction from the simulation and estimates from second-order statistics within 1sigma but deviations arise at smaller scales. Our results demonstrate the feasibility of measuring third-order IA correlations and using them for constraining IA models. The agreement between second- and third-order IA constraints also opens the opportunity for a consistent joint analysis and IA self-calibration, promising tighter parameter constraints for upcoming cosmological surveys.

Vision Transformers for identifying asteroids interacting with secular resonances

Currently, more than 1.4 million asteroids are known in the main belt. Future surveys, like those that the Vera C. Rubin Observatory will perform, may increase this number to up to 8 million. While in the past identification of asteroids interacting with secular resonances was performed by a visual analysis of images of resonant arguments, this method is no longer feasible in the age of big data. Deep learning methods based on Convolutional Neural Networks (CNNs) have been used in the recent past to automatically classify databases of several thousands of images of resonant arguments for resonances like the ν6, the g−2g6+g5, and the s−s6−g5+g6. However, it has been shown that computer vision methods based on the Transformer architecture tend to outperform CNN models if the scale of the image database is large enough. Here, for the first time, we developed a Vision Transformer (ViT) model and applied it to publicly available databases for the three secular resonances quoted above. ViT architecture outperforms CNN models in speed and accuracy while avoiding overfitting concerns. If hyper-parameter tuning research is undertaken for each analyzed database, ViT models should be preferred over CNN architectures.

3D hydrodynamic simulations of white dwarf–main-sequence star collisions – I. Head-on collisions

ABSTRACT Recently inaugurated telescopes, such as the MeerKAT radio telescope and the upcoming Rubin Observatory Legacy Survey of Space and Time, will be able to detect millions of transient events in the night sky. Stellar collisions between white dwarfs (WDs) and main-sequence (MS) stars may be detectable among such transients. Simulations will play a key role in characterizing these events and selecting targets for follow-up. We present 3D smoothed particle hydrodynamics models of dynamical interactions between a $0.6\ {\rm M}_{\odot }$ WD and 0.3, 0.6, and $1.2\ {\rm M}_{\odot }$ MS stars within globular cluster environments. Utilizing a 34-isotope nuclear network, we investigate the energetics, gas morphologies, and mass-loss properties of these collisions for different stellar mass ratios. Our models predict an overabundance of $^{13}$C, $^{15}$N, and $^{17}$O isotopes relative to solar abundances. Moreover, we find that the time-scale of the collisions is too short and maximum temperatures too low for any significant hydrogen burning or triple-alpha reactions to occur. This combined with a negligible production of elements heavier than neon may be key signatures in distinguishing these events from other transient events with similar peak bolometric luminosities ($\sim\!\! 10^{38}\!\!-\!\!10^{41}$ erg s$^{-1}$).

The Active Optics System on the Vera C. Rubin Observatory: Optimal Control of Degeneracy among the Large Number of Degrees of Freedom

The Vera C. Rubin Observatory is a unique facility for survey astronomy that will soon be commissioned and begin operations. Crucial to many of its scientific goals is the achievement of sustained high image quality, limited only by the seeing at the site. This will be maintained through an active optics system that controls optical element misalignments and corrects mirror figure error to minimize aberrations caused by both thermal and gravitational effects. However, the large number of adjustment degrees of freedom available on the Rubin Observatory introduces a range of degeneracies, including many that are induced by noise due to imperfect measurement of the wave-front errors. We present a structured methodology for identifying these degeneracies through an analysis of image noise level. We also present a novel scaling strategy based on truncated singular value decomposition that mitigates the degeneracy and optimally distributes the adjustment over the available degrees of freedom. Our approach ensures the attainment of optimal image quality, while avoiding excursions around the noise-induced subspace of degeneracies, marking a significant improvement over the previous techniques adopted for Rubin, which were based on an optimal integral controller. This new approach is likely to also yield significant benefits for all telescopes that incorporate large numbers of degrees of freedom of adjustment.

Score-matching neural networks for improved multi-band source separation

We present the implementation of a score-matching neural network that represents a data-driven prior for non-parametric galaxy morphologies. The gradients of this prior can be incorporated in the optimization of galaxy models to aid with tasks like deconvolution, inpainting or source separation. We demonstrate this approach with modification of the multi-band modeling framework scarlet that is currently employed as deblending method in the pipelines of the HyperSuprimeCam survey and the Rubin Observatory. The addition of the prior avoids the requirement of non-differentiable constraints, which can lead to convergence failures we discovered in scarlet. We present the architecture and training details of our score-matching neural network and show with simulated Rubin-like observations that using a data-driven prior outperforms the baseline scarlet method in accuracy of total flux and morphology estimates, while maintaining excellent performance for colors. We also demonstrate significant improvements in the robustness to inaccurate initializations. The trained score models used for this analysis are publicly available at https://github.com/SampsonML/galaxygrad.

Superphot+: Real-time Fitting and Classification of Supernova Light Curves

Photometric classifications of supernova (SN) light curves have become necessary to utilize the full potential of large samples of observations obtained from wide-field photometric surveys, such as the Zwicky Transient Facility (ZTF) and the Vera C. Rubin Observatory. Here, we present a photometric classifier for SN light curves that does not rely on redshift information and still maintains comparable accuracy to redshift-dependent classifiers. Our new package, Superphot+, uses a parametric model to extract meaningful features from multiband SN light curves. We train a gradient-boosted machine with fit parameters from 6061 ZTF SNe that pass data quality cuts and are spectroscopically classified as one of five classes: SN Ia, SN II, SN Ib/c, SN IIn, and SLSN-I. Without redshift information, our classifier yields a class-averaged F 1-score of 0.61 ± 0.02 and a total accuracy of 0.83 ± 0.01. Including redshift information improves these metrics to 0.71 ± 0.02 and 0.88 ± 0.01, respectively. We assign new class probabilities to 3558 ZTF transients that show SN-like characteristics (based on the ALeRCE Broker light-curve and stamp classifiers) but lack spectroscopic classifications. Finally, we compare our predicted SN labels with those generated by the ALeRCE light-curve classifier, finding that the two classifiers agree on photometric labels for 82% ± 2% of light curves with spectroscopic labels and 72% ± 0% of light curves without spectroscopic labels. Superphot+ is currently classifying ZTF SNe in real time via the ANTARES Broker, and is designed for simple adaptation to six-band Rubin light curves in the future.

DECam Multimessenger Astrophysics Pipeline. I. From Raw Data to Single-exposure Candidates

We introduce a pipeline that performs rapid image subtraction and source selection to detect transients, with a focus on identifying gravitational-wave optical counterparts using the Dark Energy Camera (DECam). In this work, we present the pipeline steps from processing raw data to identification of astrophysical transients on individual exposures. We process DECam data and build difference images using the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) Science Pipelines software, and we use flags and principal component analysis to select transients on a per-exposure basis, without associating the results from different exposures. Those candidates will be sent to brokers for further classification and alert distribution. We validate our pipeline using archival exposures that cover various types of objects, and the tested targets include a kilonova (GW170817), supernovae, stellar flares, variable stars (in a resolved galaxy or the Milky Way Bulge), and serendipitous objects. Overall, the data processing produces clean light curves that are comparable with published results, demonstrating the photometric quality of our pipeline. Real transients can be well selected by our pipeline when sufficiently bright (signal-to-noise ratio ≳15). This pipeline is intended to serve as a tool for the broader research community. Although this pipeline is designed for DECam, our method can be easily applied to other instruments and future LSST observations.

Asteroid Period Solutions from Combined Dense and Sparse Photometry

Deriving high-quality light curves for asteroids and other periodic sources from survey data is challenging owing to many factors, including the sparsely sampled observational record and diurnal aliasing, which is a signature imparted into the periodic signal of a source that is a function of the observing schedule of ground-based telescopes. In this paper we examine the utility of combining asteroid observational records from the Zwicky Transient Facility and the Transiting Exoplanet Survey Satellite, which are the ground- and space-based facilities, respectively, to determine to what degree the data from the space-based facility can suppress diurnal aliases. Furthermore, we examine several optimizations that are used to derive the rotation periods of asteroids, which we then compare to the reported rotation periods in the literature. Through this analysis we find that we can reliably derive the rotation periods for ∼85% of our sample of 222 objects that are also reported in the literature and that the remaining ∼15% are difficult to reliably derive, as many are asteroids that are insufficiently elongated, which produces a light curve with an insufficient amplitude and, consequently, an incorrect rotation period. We also investigate a binary classification method that biases against reporting incorrect rotation periods. We conclude the paper by assessing the utility of using other ground- or space-based facilities as companion telescopes to the forthcoming Rubin Observatory.

Prospect of Precision Cosmology and Testing General Relativity using Binary Black Holes- Galaxies Cross-correlation

Abstract Modified theories of gravity predict deviations from General Relativity (GR) in the propagation of gravitational waves (GW) across cosmological distances. A key prediction is that the GW luminosity distance will vary with redshift, differing from the electromagnetic (EM) luminosity distance due to varying effective Planck mass. We introduce a model-independent, data-driven approach to explore these deviations using multi-messenger observations of dark standard sirens (Binary Black Holes, BBH). By combining GW luminosity distance measurements from dark sirens with Baryon Acoustic Oscillation (BAO) measurements, BBH redshifts inferred from cross-correlation with spectroscopic or photometric galaxy surveys, and sound horizon measurements from the Cosmic Microwave Background (CMB), we can make a data-driven test of GR (jointly with the Hubble constant) as a function of redshift. Using the multi-messenger technique with the spectroscopic DESI galaxy survey, we achieve precise measurements of deviations in the effective Planck mass variation with redshift. For the Cosmic Explorer and Einstein Telescope (CEET), the best precision is approximately 3.6%, and for LIGO-Virgo-KAGRA (LVK), it is 7.4% at a redshift of $\rm {z = 0.425}$. Additionally, we can measure the Hubble constant with a precision of about 1.1% from CEET and 7% from LVK over five years of observation with a 75% duty cycle. We also explore the potential of cross-correlation with photometric galaxy surveys from the Rubin Observatory, extending measurements up to a redshift of $\rm {z \sim 2.5}$. This approach can reveal potential deviations from models affecting GW propagation using numerous dark standard sirens in synergy with DESI and the Rubin Observatory.