Rachel Mandelbaum

We present galaxy luminosity functions and photometric predictions for the Vera C. Rubin Observatory Legacy Survey of Space and Time using the ASTRID cosmological hydrodynamical simulation. Galaxy magnitudes combine stellar population synthesis with a dust attenuation model whose optical depth scales with metal surface density. We calibrate the dust model at z = 0 using SDSS luminosity functions and test it at z = 0.5, 1.0, and 1.5 in rest frame B , V , R , I bands, finding good agreement with observations. We then construct LSST ready mock photometric catalogs over 0 ≤ z ≤ 2 in steps of Δ z = 0.1, containing about 378 million galaxies, including about 47 million above the LSST Year 10 coadded i -band limit. We provide apparent magnitude luminosity functions in ugrizy , best-fit Schechter parameters, and differential and cumulative galaxy counts as a function of survey depth.

The Legacy Survey of Space and Time (LSST) that will be carried out by the NSF-DOE Vera C. Rubin Observatory promises to be the defining survey of the next decade for both static and time-domain science. Maximizing the LSST’s scientific output requires a nontrivial survey strategy (i.e., the sequence of observations in space, time, and passband). For time-domain science, the most promising strategy to date is a rolling survey strategy, whereby alternating subsets of the full LSST area are observed at a higher-than-nominal rate. Focusing on static science (galaxy clustering and weak lensing), we study how time-domain-optimized rolling strategies affect the depth uniformity at intermediate survey years and present new metrics directly connecting depth uniformity with science return. We characterize the amount of survey area at high risk of being lost in static-science analyses of a rolling LSST data set due to insufficient survey uniformity. At intermediate data releases, nearly half of the survey could be lost for static science, decreasing the dark energy figure of merit by 40%. We describe additional metrics focused on key analysis tasks, such as photometric redshifts and galaxy clustering. Finally, we propose a new strategy that returns the survey to uniformity at key release years, enabling use of the full area and restoring our metrics to the values they would have in a nonrolling cadence—without losing time domain data relative to a rolling survey with the same number of rolling cycles. These new “uniform rolling” strategies have been incorporated into the LSST baseline strategy.

The combination of galaxy-galaxy weak lensing and galaxy clustering is a powerful probe of the cosmological model, and exploration of how to best model and extract this information from the signals is essential. We present the measurement of the galaxy-galaxy weak lensing signals using the SDSS DR11 spectroscopic galaxies as lens galaxies, and the HSC Y3 shear catalog as source galaxies, binned into four tomographic bins by their photometric redshift. The SDSS DR11 galaxies, with a redshift range 0.15 < z < 0.7 , are binned into three redshift bins, each as a probe for measuring the projected correlation function, w p ( R p ) . We measure the galaxy-galaxy lensing signal Δ Σ ( R p ) in 12 lens-source bin pairs and show that there is no evidence for significant systematic biases in the measurement with null testing. We combine our w p ( R p ) and Δ Σ ( R p ) ( 2 × 2 pt ) data vectors and perform likelihood inference with a flat Λ CDM model. For Δ Σ ( R p ) , we extend the lower limit of the scale cut compared to previous HSC Y3 analyses to 2 h − 1 Mpc by including a point-mass correction term in addition to the minimal bias model. We present various tests to validate our model and provide extended consistency tests. In the Λ CDM context, our fiducial model yields S 8 = 0.80 4 − 0.051 + 0.051 . The 2 × 2 pt data vector provides redshift parameter constraints for the third and fourth redshift bins Δ z 3 = − 0.07 9 − 0.084 + 0.074 , and Δ z 4 = − 0.20 3 − 0.206 + 0.167 , which is consistent with results from the previous tomographic cosmic shear studies, and serves as the foundation for a future 3 × 2 pt analysis.

The combination of galaxy clustering and weak lensing is a powerful probe of the cosmology model. We present a joint analysis of galaxy clustering and weak lensing cosmology using Sloan Digital Sky Survey (SDSS) data as the tracer of dark matter (lens sample) and the Hyper Suprime-Cam year-3 (HSC Y3) dataset as source galaxies. The analysis divides HSC Y3 galaxies into four tomographic bins for both galaxy-galaxy lensing and cosmic shear measurements and employs a point-mass correction model to utilize galaxy-galaxy lensing signals down to 2 h − 1 Mpc , extending up to 70 h − 1 Mpc . These strategies enhance the signal-to-noise ratio of the galaxy-galaxy lensing data vector. Using a flat Λ CDM model, we find S 8 = 0.78 0 − 0.030 + 0.029 , and using a w CDM model, we obtain S 8 = 0.75 6 − 0.036 + 0.038 with w = − 1.17 6 − 0.346 + 0.310 . We apply uninformative priors on the redshift mean-shift parameters for the third and fourth tomographic bins. Leveraging the self-calibration power of tomographic weak lensing, we measure Δ z 3 = − 0.11 2 − 0.049 + 0.046 and Δ z 4 = − 0.18 5 − 0.081 + 0.071 , in agreement with previous HSC Y3 results. This demonstrates that weak lensing self-calibration can achieve redshift constraints comparable to other methods such as photometric and clustering redshift calibration.

We report on the observation and measurement of astrometry, photometry, morphology, and activity of the interstellar object 3I/ATLAS, also designated C/2025 N1 (ATLAS) with the NSF-DOE Vera C. Rubin Observatory. Comet 3I/ATLAS, the third known interstellar object, was discovered on UT 2025 July 1. Rubin Observatory had coincidentally collected images of the object’s region of the sky during routine commissioning. Facilitated by Rubin’s high resolution and large aperture, we successfully recovered object detections from Rubin observations spanning UT 2025 June 21 (10 days before discovery, when 3I/ATLAS was 4.5 au from the Sun) through the date of discovery, and we acquired additional images through UT 2025 July 20 as part of commissioning. We measure on-sky locations of 3I/ATLAS in Rubin ugrizy bands, with a typical precision of ∼70 mas, and briefly describe the reason this is coarser than our measured static source astrometric precision of ∼3 mas in Rubin images. We measure grizy magnitudes of 3I/ATLAS photometry at ∼0.01 mag precision, detecting no short-term photometric variability above 0.01 mag. We derive an estimated near-nucleus dust-to-nucleus scattering cross-sectional ratio of η ≳ 13 on UT 2025 July 2 based on Rubin photometry and an upper limit nucleus size computed from Hubble Space Telescope observations. We find Rubin colors of g − r = (0.657 ± 0.013) mag, r − i = (0.235 ± 0.018) mag, i − z = (0.147 ± 0.042) mag, and z − y = (0.047 ± 0.052) mag. These data represent the earliest observations of this object by a large (≳8 m class) telescope and illustrate the type of measurements (and discoveries) Rubin’s Legacy Survey of Space and Time will provide after it begins in early 2026.

E G is a summary statistic that combines cosmological observables to achieve a test of gravity that is relatively model independent. Here, we consider the power of a measurement of E G using galaxy-galaxy lensing and galaxy clustering with sources from the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST), and lenses from the Dark Energy Spectroscopic Instrument (DESI). We first update the theoretical framework for the covariance of E G to accommodate this Stage IV scenario. We then demonstrate that E G offers in principle a model-agnostic test of gravity using only linear-scale information, with the caveat that a careful treatment of galaxy bias is required. We finally address the persistent issue of E G ’s theoretical dependence on the measured value of Ω M 0 . We propose a framework that takes advantage of the posterior predictive test to consistently incorporate uncertainty on Ω M 0 in tests of gravity with E G , which should be of general use beyond the LSST + DESI scenario. Our forecasting study using this method shows that the prior information available for Ω M 0 is instrumental in determining the power of E G in the LSST + DESI context. For the full survey dataset, with priors on Ω M 0 from existing cosmic microwave background data, we find that for some modified gravity scenarios considered, we are likely to be able to reject the general relativity null hypothesis.

Most LSST extragalactic science will rely on photometric redshifts (photo-$z$) to extract distance information for the galaxies. However, an incomplete or non-representative training set can introduce bias into photo-$z$ estimation. It is necessary to understand how various forms of training set imperfection, such as incompleteness and non-trivial spectroscopic target selection, affect photo-$z$ estimation algorithms, and to identify metrics best-suited to quantify the impact. This work aims to systematically study metrics for diagnosing how various photo-$z$ methods react to certain types of training set incompleteness and non-representativeness. We use methods available through the open-source Python library Redshift Assessment Infrastructure Layers (RAIL) to systematically test the algorithms CMNN, GPz, FlexZBoost, and PZFlow on mock training data degraded in accordance with several existing spectroscopic sky surveys, as well as under conditions of inverse redshift incompleteness, which approximately mimics observed patterns of incompleteness at high redshift. We employ the algorithm TrainZ as a control. Finally, we quantify photo-$z$ algorithm performance using a variety of statistical metrics implemented externally to RAIL. We determine that the Kullback-Liebler Divergence, Wasserstein Distance, and Probability Integral Transform are particularly informative metrics with which to assess the impact of training set imperfection on algorithmic performance. We also find that inverse redshift incompleteness effects alone lack the complexity to realistically represent anticipated training data.