About

Professor Rachel Mandelbaum leads a research group that currently includes four postdoctoral researchers and advises multiple physics PhD candidates, including those at pre-candidacy stages. Her collaborative work spans several departments at Carnegie Mellon University, including physics, statistics and data science, and machine learning. As the principal investigator for CMU in the LINCC Frameworks project, she works closely with the entire LINCC Frameworks team. Her research group has a history of alumni who continue to collaborate with her, indicating an ongoing engagement with former group members.

Research topics

• Computer Science
• Physics
• Astronomy
• Data Mining
• Geography
• Computer Security
• Astrophysics
• Engineering
• Statistics
• Data science
• Geodesy
• Remote sensing
• Database
• Geology
• Computer graphics (images)
• Systems engineering
• Cartography

Selected publications

• Redshift Assessment Infrastructure Layers (RAIL): Rubin-era photometric redshift stress-testing and at-scale production

  The Open Journal of Astrophysics · 2026-02-23 · 1 citations

  preprintOpen access

  Virtually all extragalactic use cases of the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST) require the use of galaxy redshift information, yet the vast majority of its sample of tens of billions of galaxies will lack high-fidelity spectroscopic measurements thereof, instead relying on photometric redshifts (photo- <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>z</mml:mi> </mml:math> ) subject to systematic imprecision and inaccuracy best encapsulated by photo- <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>z</mml:mi> </mml:math> probability density functions (PDFs). We present the version 1 release of Redshift Assessment Infrastructure Layers (RAIL), an open source Python library for at-scale probabilistic photo- <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>z</mml:mi> </mml:math> estimation, initiated by the LSST Dark Energy Science Collaboration (DESC) with contributions from the LSST Interdisciplinary Network for Collaboration and Computing (LINCC) Frameworks team. RAIL’s three subpackages provide modular tools for end-to-end stress-testing, including a forward modeling suite to generate realistically complex photometry, a unified API for estimating per-galaxy and ensemble redshift PDFs by an extensible set of algorithms, and built-in metrics of both photo- <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>z</mml:mi> </mml:math> PDFs and point estimates. RAIL serves as a flexible toolkit enabling the derivation and optimization of photo- <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>z</mml:mi> </mml:math> data products at scale for a variety of science goals and is not specific to LSST data. We thus describe to the extragalactic science community, including and beyond Rubin the design and functionality of the RAIL software library so that any researcher may have access to its wide array of photo- <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>z</mml:mi> </mml:math> characterization and assessment tools.

  PublisherDOI

• cosmo-numba: B-modes and COSEBIs computations accelerated by Numba

  arXiv (Cornell University) · 2026-03-19

  preprintOpen accessSenior author

  Weak gravitational lensing is a widely used probe in cosmological analysis. It allows astrophysists to understand the content and evolution of the Universe. We are entering an era where we are not limited by the data volume but by systematic uncertainties. It is in this context that we present here a simple python-based software package to help in the computation of E-/B-mode decomposition, which can be use for systematic checks or science analysis. As we demonstrate, our implementation has both the high precision and speed required to perform this kind of analysis while avoiding a scenario wherein either numerical precision or computational time is a significant limiting factor.

  PublisherDOI

• cosmo-numba: B-modes and COSEBIs computations accelerated by Numba

  ArXiv.org · 2026-03-19

  articleOpen accessSenior author

  Weak gravitational lensing is a widely used probe in cosmological analysis. It allows astrophysists to understand the content and evolution of the Universe. We are entering an era where we are not limited by the data volume but by systematic uncertainties. It is in this context that we present here a simple python-based software package to help in the computation of E-/B-mode decomposition, which can be use for systematic checks or science analysis. As we demonstrate, our implementation has both the high precision and speed required to perform this kind of analysis while avoiding a scenario wherein either numerical precision or computational time is a significant limiting factor.

  PublisherOA PDF

• Toward testing gravity with LSST using <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:msub> <mml:mi>E</mml:mi> <mml:mi>G</mml:mi> </mml:msub> </mml:math>

  Physical review. D/Physical review. D. · 2026-01-29

  articleOpen access

  <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" display="inline"> <a:msub> <a:mi>E</a:mi> <a:mi>G</a:mi> </a:msub> </a:math> 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 <c:math xmlns:c="http://www.w3.org/1998/Math/MathML" display="inline"> <c:msub> <c:mi>E</c:mi> <c:mi>G</c:mi> </c:msub> </c:math> 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:math xmlns:e="http://www.w3.org/1998/Math/MathML" display="inline"> <e:msub> <e:mi>E</e:mi> <e:mi>G</e:mi> </e:msub> </e:math> to accommodate this Stage IV scenario. We then demonstrate that <g:math xmlns:g="http://www.w3.org/1998/Math/MathML" display="inline"> <g:msub> <g:mi>E</g:mi> <g:mi>G</g:mi> </g:msub> </g:math> 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 <i:math xmlns:i="http://www.w3.org/1998/Math/MathML" display="inline"> <i:msub> <i:mi>E</i:mi> <i:mi>G</i:mi> </i:msub> </i:math> ’s theoretical dependence on the measured value of <k:math xmlns:k="http://www.w3.org/1998/Math/MathML" display="inline"> <k:msubsup> <k:mi mathvariant="normal">Ω</k:mi> <k:mi mathvariant="normal">M</k:mi> <k:mn>0</k:mn> </k:msubsup> </k:math> . We propose a framework that takes advantage of the posterior predictive test to consistently incorporate uncertainty on <o:math xmlns:o="http://www.w3.org/1998/Math/MathML" display="inline"> <o:msubsup> <o:mi mathvariant="normal">Ω</o:mi> <o:mi mathvariant="normal">M</o:mi> <o:mn>0</o:mn> </o:msubsup> </o:math> in tests of gravity with <s:math xmlns:s="http://www.w3.org/1998/Math/MathML" display="inline"> <s:msub> <s:mi>E</s:mi> <s:mi>G</s:mi> </s:msub> </s:math> , which should be of general use beyond the LSST + DESI scenario. Our forecasting study using this method shows that the prior information available for <u:math xmlns:u="http://www.w3.org/1998/Math/MathML" display="inline"> <u:msubsup> <u:mi mathvariant="normal">Ω</u:mi> <u:mi mathvariant="normal">M</u:mi> <u:mn>0</u:mn> </u:msubsup> </u:math> is instrumental in determining the power of <y:math xmlns:y="http://www.w3.org/1998/Math/MathML" display="inline"> <y:msub> <y:mi>E</y:mi> <y:mi>G</y:mi> </y:msub> </y:math> in the LSST + DESI context. For the full survey dataset, with priors on <ab:math xmlns:ab="http://www.w3.org/1998/Math/MathML" display="inline"> <ab:msubsup> <ab:mi mathvariant="normal">Ω</ab:mi> <ab:mi mathvariant="normal">M</ab:mi> <ab:mn>0</ab:mn> </ab:msubsup> </ab:math> 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.

  PublisherDOI

• Modelling Galaxy Clustering and Tomographic Galaxy-Galaxy Lensing with HSC Y3 and SDSS using the Point-Mass Correction Model and Redshift Self-Calibration

  ArXiv.org · 2025-07-02

  preprintOpen access

  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

  PublisherOA PDFDOI

• Accurate shear estimation with fourth-order moments

  Monthly Notices of the Royal Astronomical Society · 2025-01-13 · 1 citations

  articleOpen accessSenior author

  ABSTRACT As imaging surveys progress in exploring the large-scale structure of the Universe through the use of weak gravitational lensing, achieving sub-per cent accuracy in estimating shape distortions caused by lensing, or shear, is imperative for precision cosmology. In this paper, we extend the Fourier power function shapelets (FPFS) shear estimator using fourth-order shapelet moments and combine it with the original second-order shear estimator to reduce galaxy shape noise. We calibrate this novel shear estimator analytically to a sub-per cent level-accuracy using the AnaCal framework. This higher order shear estimator is tested with realistic image simulations, and after analytical correction for the detection/selection bias and noise bias, the multiplicative shear bias $|m|$ is below $3\times 10^{-3}$ (99.7 per cent confidence interval) for both isolated and blended galaxies. Once combined with the second-order FPFS shear estimator, the shape noise is reduced by $\sim 35~{{\ \rm per\ cent}}$ for isolated galaxies in simulations with Hyper Suprime-Cam and Vera C. Rubin Observatory Legacy Survey of Space and Time observational conditions. However, for blended galaxies, the effective number density does not significantly improve with the combination of the two estimators. Based on these results, we recommend exploration of how this framework can further reduce the systematic uncertainties in shear due to point spread function leakage and modelling error, and potentially provide improved precision in shear inference in high-resolution space-based images.

  PublisherOA PDFDOI

• Using LSDB to enable large-scale catalog distribution, cross-matching, and analytics

  arXiv (Cornell University) · 2025-01-03 · 1 citations

  preprintOpen access

  The Vera C. Rubin Observatory will generate an unprecedented volume of data, including approximately 60 petabytes of raw data and around 30 trillion observed sources, posing a significant challenge for large-scale and end-user scientific analysis. As part of the LINCC Frameworks Project we are addressing these challenges with the development of the HATS (Hierarchical Adaptive Tiling Scheme) format and analysis package LSDB. HATS partitions data adaptively using a hierarchical tiling system to balance the file sizes, enabling efficient parallel analysis. Recent updates include improved metadata consistency, support for incremental updates, and enhanced compatibility with evolving datasets. LSDB complements HATS by providing a scalable, user-friendly interface for large catalog analysis, integrating spatial queries, crossmatching, and time-series tools while utilizing Dask for parallelization. We have successfully demonstrated the use of these tools with datasets such as ZTF and Pan-STARRS data releases on both cluster and cloud environments. We are deeply involved in several ongoing collaborations to ensure alignment with community needs, with future plans for IVOA standardization and support for upcoming Rubin, Euclid and Roman data. We provide our code and materials at lsdb.io.

  PublisherOA PDFDOI

• Cosmology and Source Redshift Constraints from Galaxy Clustering and Tomographic Weak Lensing with HSC Y3 and SDSS using the Point-Mass Correction Model

  ArXiv.org · 2025-07-02

  preprintOpen access

  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 SDSS data as the tracer of dark matter (lens sample) and the 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 $S_8 = 0.780^{+0.029}_{-0.030}$, and using a $w$CDM model, we obtain $S_8 = 0.756^{+0.038}_{-0.036}$ with $w = -1.176^{+0.310}_{-0.346}$. 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 $Δz_3 = -0.112^{+0.046}_{-0.049}$ and $Δz_4 = -0.185^{+0.071}_{-0.081}$, 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.

  PublisherOA PDFDOI

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

  Monthly Notices of the Royal Astronomical Society · 2025-10-22 · 7 citations

  articleOpen access

  ABSTRACT 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.

  PublisherOA PDFDOI

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

  ArXiv.org · 2025-07-02

  preprintOpen access

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

  PublisherOA PDFDOI

Recent grants

• Intrinsic Alignment Models for Precision Cosmology

  NSF · $426k · 2017–2021

• Galaxy shape measurements and intrinsic alignments: reducing the impact of weak lensing systematic errors

  NSF · $363k · 2013–2017

Frequent coauthors

• Jason Rhodes

  175 shared

• Hironao Miyatake

  174 shared

• Barnaby Rowe

  159 shared

• Konrad Kuijken

  Leiden University

  151 shared

• A. Rassat

  150 shared

• Donnacha Kirk

  University College London

  149 shared

• Marina Shmakova

  Institute of Limnology of the Russian Academy of Sciences

  148 shared

• Andy Taylor

  Royal Observatory

  148 shared

Labs

• Rachel Mandelbaum's Research GroupPI

  Not provided

Education

• Ph.D.

  Princeton University

  2006

• Other

  Institute for Advanced Study

  2009

• Other

  Princeton University

  2011

Awards & honors

• Simons Investigator in Astrophysics (2019)
• Falco-DeBenedetti Career Development Professor (2013)
• Alfred P. Sloan Fellow (2013)
• DOE Early Career Award (2012)
• AAS Annie Jump Cannon Prize (2011)