arXiv subjects

Grant Merz

Publications and source records attributed to Grant Merz.

12 recordsLinked to original sources

arXiv2026

DB-Bench: Benchmarking Deblenders for LSST DESC Using the Blending ToolKit

Blending will be a major source of systematic uncertainty in downstream science analyses of LSST data. We benchmark the performance of several deblenders, leveraging the Blending ToolKit (BTK) to perform rigorous, end-to-end testing. This benchmark incorporates key deblending algorithms, including SourceExtractor, SCARLET, and DeepDISC, with the goal of comparing their effectiveness in handling blended galaxy images from LSST/Rubin simulations. A key focus is characterizing algorithm performance in the regime of unrecognized blends, where multiple galaxies are misidentified as a single object, as these cases introduce systematic biases that propagate into downstream cosmological analyses for galaxy surveys. By utilizing BTK's ability to create customized, reproducible blends, we systematically test these deblenders against different blending conditions, such as source separation and brightness. The toolkit's standardized evaluation metrics, including detection precision, segmentation accuracy, and source reconstruction, are comprehensive assessments of each algorithm's strengths and limitations. Each deblender has performance caveats that may impact their true performance in real survey conditions. We find that SCARLET has high segmentation and reconstruction performance, whereas DeepDISC has strong detection recall for faint and low-SNR sources, and SourceExtractor has accurate peak finding abilities but low segmentation and reconstruction performance. This benchmark provides valuable insights into the performance of existing deblenders and highlights areas for future development.

astro-ph.IM↗

arXiv2026

DeepDISC-Euclid: Source Classification and Photometric Redshifts in Euclid Deep Field North With a Pixel-Level Deep Learning Approach

The first Euclid Quick Data Release (Q1) provides extensive imaging and spectroscopic data for hundreds of millions of photometric objects across several deep fields. Accurate classifications and photometric redshifts (photo-z) for these sources are crucial to maximizing the value of these data. In this work, we perform source classification and photo-z estimation for the Euclid Deep Field North (EDF-N) around the North Ecliptic Pole, using a deep learning framework (DeepDISC) that learns and infers using 9-band images simultaneously. We train three dedicated models for (1) source detection and classification, (2) galaxy photo-z, and (3) quasar photo-z. The Euclid Q1 input source catalog, and classifications and spectroscopic redshifts (spec-z) from the Dark Energy Spectroscopic Instrument Data Release 1 are adopted as our training data. DeepDISC source detection achieves overall completeness of ~93% and purity of ~80% if using the Euclid source catalog as the ground truth. Using a JWST source catalog within EDF-N as the reference, we estimate a true purity of ~ 90% for DeepDISC sources. About 99.2%, 99.0%, and 84.8% of stars, galaxies, and quasars, respectively, are correctly recovered with their spectroscopic classifications. The DeepDISC photo-zs show good agreement with spectroscopic redshifts, for both galaxies and quasars. Comparisons with other Euclid Q1 products demonstrate that DeepDISC provides comparable or improved performance in source detection/deblending, classification and photo-z, especially for quasars. These results demonstrate the potential of pixel-level deep learning approaches for large-scale sky surveys such as Euclid and Roman, which will continue to improve with better training labels. We release the full DeepDISC source catalog (~13 million objects) for EDF-N with classifications and photo-zs, including photo-z probability distributions.

astro-ph.GA↗

arXiv2026

Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration

The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will produce unprecedented volumes of heterogeneous astronomical data (images, catalogs, and alerts) that challenge traditional analysis pipelines. The LSST Dark Energy Science Collaboration (DESC) aims to derive robust constraints on dark energy and dark matter from these data, requiring methods that are statistically powerful, scalable, and operationally reliable. Artificial intelligence and machine learning (AI/ML) are already embedded across DESC science workflows, from photometric redshifts and transient classification to weak lensing inference and cosmological simulations. Yet their utility for precision cosmology hinges on trustworthy uncertainty quantification, robustness to covariate shift and model misspecification, and reproducible integration within scientific pipelines. This white paper surveys the current landscape of AI/ML across DESC's primary cosmological probes and cross-cutting analyses, revealing that the same core methodologies and fundamental challenges recur across disparate science cases. Since progress on these cross-cutting challenges would benefit multiple probes simultaneously, we identify key methodological research priorities, including Bayesian inference at scale, physics-informed methods, validation frameworks, and active learning for discovery. With an eye on emerging techniques, we also explore the potential of the latest foundation model methodologies and LLM-driven agentic AI systems to reshape DESC workflows, provided their deployment is coupled with rigorous evaluation and governance. Finally, we discuss critical software, computing, data infrastructure, and human capital requirements for the successful deployment of these new methodologies, and consider associated risks and opportunities for broader coordination with external actors.

astro-ph.IM↗

arXiv2025

Photometric Redshifts in JWST Deep Fields: A Pixel-Based Alternative with DeepDISC

Photo-z algorithms that utilize SED template fitting have matured, and are widely adopted for use on high-redshift near-infrared data that provides a unique window into the early universe. Alternative photo-z methods have been developed, largely within the context of low-redshift optical surveys. Machine learning based approaches have gained footing in this regime, including those that utilize raw pixel information instead of aperture photometry. However, the efficacy of image-based algorithms on high-redshift, near-infrared data remains underexplored. Here, we test the performance of Detection, Instance Segmentation and Classification with Deep Learning (DeepDISC) on photometric redshift estimation with NIRCam images from the JWST Advanced Deep Extragalactic Survey (JADES) program. DeepDISC is designed to produce probabilistic photometric redshift estimates directly from images, after detecting and deblending sources in a scene. Using NIRCam-only images and a compiled catalog of spectroscopic redshifts, we show that DeepDISC produces reliable photo-zs and uncertainties comparable to those estimated from template fitting using HST+JWST filters; DeepDISC even outperforms template fitting (lower scatter/fewer outliers) when the input photometric filters are matched. Compared with template fitting, DeepDISC does not require measured photometry from images, and can produce a catalog of 94000 photo-zs in ~4 minutes on a single NVIDIA A40 GPU. While current spectroscopic training samples are small and incomplete in color-magnitude space, this work demonstrates the potential of DeepDISC for increasingly larger image volumes and spectroscopic samples from ongoing and future programs. We discuss the impact of the training data on applications to broader samples and produce a catalog of photo-zs for all JADES DR2 photometric sources in the GOOD-S field, with quality flags indicating caveats.

astro-ph.IM↗

arXiv2025

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

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-$z$) subject to systematic imprecision and inaccuracy best encapsulated by photo-$z$ 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-$z$ 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-$z$ PDFs and point estimates. RAIL serves as a flexible toolkit enabling the derivation and optimization of photo-$z$ 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-$z$ characterization and assessment tools.

astro-ph.IM↗

arXiv2024

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↗

arXiv2024

The Blending ToolKit: A simulation framework for evaluation of galaxy detection and deblending

We present an open source Python library for simulating overlapping (i.e., blended) images of galaxies and performing self-consistent comparisons of detection and deblending algorithms based on a suite of metrics. The package, named Blending Toolkit (BTK), serves as a modular, flexible, easy-to-install, and simple-to-use interface for exploring and analyzing systematic effects related to blended galaxies in cosmological surveys such as the Vera Rubin Observatory Legacy Survey of Space and Time (LSST). BTK has three main components: (1) a set of modules that perform fast image simulations of blended galaxies, using the open source image simulation package GalSim; (2) a module that standardizes the inputs and outputs of existing deblending algorithms; (3) a library of deblending metrics commonly defined in the galaxy deblending literature. In combination, these modules allow researchers to explore the impacts of galaxy blending in cosmological surveys. Additionally, BTK provides researchers who are developing a new deblending algorithm a framework to evaluate algorithm performance and make principled comparisons with existing deblenders. BTK includes a suite of tutorials and comprehensive documentation. The source code is publicly available on GitHub at https://github.com/LSSTDESC/BlendingToolKit.

astro-ph.IM↗

arXiv2021

Primordial non-Gaussianity from the Completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey I: Catalogue Preparation and Systematic Mitigation

We investigate the large-scale clustering of the final spectroscopic sample of quasars from the recently completed extended Baryon Oscillation Spectroscopic Survey (eBOSS). The sample contains $343708$ objects in the redshift range $0.8<z<2.2$ and $72667$ objects with redshifts $2.2<z<3.5$, covering an effective area of $4699~{\rm deg}^{2}$. We develop a neural network-based approach to mitigate spurious fluctuations in the density field caused by spatial variations in the quality of the imaging data used to select targets for follow-up spectroscopy. Simulations are used with the same angular and radial distributions as the real data to estimate covariance matrices, perform error analyses, and assess residual systematic uncertainties. We measure the mean density contrast and cross-correlations of the eBOSS quasars against maps of potential sources of imaging systematics to address algorithm effectiveness, finding that the neural network-based approach outperforms standard linear regression. Stellar density is one of the most important sources of spurious fluctuations, and a new template constructed using data from the Gaia spacecraft provides the best match to the observed quasar clustering. The end-product from this work is a new value-added quasar catalogue with the improved weights to correct for nonlinear imaging systematic effects, which will be made public. Our quasar catalogue is used to measure the local-type primordial non-Gaussianity in our companion paper, Mueller et al. in preparation.

astro-ph.CO↗

arXiv2021

The clustering of the SDSS-IV extended Baryon Oscillation Spectroscopic Survey quasar sample: Testing observational systematics on the Baryon Acoustic Oscillation measurement

Baryon Acoustic Oscillations are considered to be a very robust standard ruler against various systematics. This premise has been tested against observational systematics, but not to the level required for the next generation of galaxy surveys such as the Dark Energy Spectroscopic Instrument (DESI) and Euclid. In this paper, we investigate the effect of observational systematics on the BAO measurement of the final sample of quasars from the extended Baryon Oscillation Spectroscopic Survey Data Release 16 in order to prepare and hone a similar analysis for upcoming surveys. We employ catalogues with various treatments of imaging systematic effects using linear and neural network-based nonlinear approaches and consider how the BAO measurement changes. We also test how the variations to the BAO fitting model respond to the observational systematics. As expected, we confirm that the BAO measurements obtained from the DR16 quasar sample are robust against imaging systematics well within the statistical error, while reporting slightly modified constraints that shift the line-of-sight BAO signal by less than 1.1% . We use realistic simulations with similar redshift and angular distributions as the DR16 sample to conduct statistical tests for validating the pipeline, quantifying the significance of differences, and estimating the expected bias on the BAO scale in future high-precision data sets. Although we find a marginal impact for the eBOSS QSO data, the work presented here is of vital importance for constraining the nature of dark energy with the BAO feature in the new era of big data cosmology with DESI and Euclid.

astro-ph.CO↗

arXiv2020

Urca Nuclide Production in Type-I X-ray Bursts and Implications for Nuclear Physics Studies

The thermal structure of accreting neutron stars is affected by the presence of urca nuclei in the neutron star crust. Nuclear isobars harboring urca nuclides can be produced in the ashes of Type I X-ray bursts, but the details of their production have not yet been explored. Using the code {\tt MESA}, we investigate urca nuclide production in a one-dimensional model of Type I X-ray bursts using astrophysical conditions thought to resemble the source GS 1826-24. We find that high-mass ($A\geq55$) urca nuclei are primarily produced late in the X-ray burst, during hydrogen-burning freeze-out that corresponds to the tail of the burst light curve. The $\sim0.4$--0.6~GK temperature relevant for nucleosynthesis of these urca nuclides is much lower than the $\sim1$~GK temperature most relevant for X-ray burst light curve impacts by nuclear reaction rates involving high-mass nuclides. The latter temperature is often assumed for nuclear physics studies. Therefore, our findings alter the excitation energy range of interest in compound nuclei for nuclear physics studies of urca nuclide production. We demonstrate that for some cases this will need to be considered in planning for nuclear physics experiments. Additionally, we show that the lower temperature range for urca nuclide production explains why variations of some nuclear reaction rates in model calculations impacts the burst light curve but not local features of the burst ashes.

astro-ph.HE↗

arXiv2020

Sensitivity of ${^{44}}$Ti and ${^{56}}$Ni production in CCSN shock-driven nucleosynthesis to reaction rates

Recent observational advances have enabled high resolution mapping of ${^{44}}$Ti in core-collapse supernova (CCSN) remnants. Comparisons between observations and models provide stringent constraints on the CCSN mechanism. However, past work has identified several uncertain nuclear reaction rates that influence ${^{44}}$Ti and ${^{56}}$Ni production in post-processing model calculations. We evolved one dimensional models of $15~M_{\odot}$, $18~M_{\odot}$, $22~M_{\odot}$ and $25~M_{\odot}$ stars from zero-age main sequence through CCSN using {\tt MESA} (Modules for Experiments in Stellar Astrophysics) and investigated the previously identified reaction rate sensitivities of ${^{44}}$Ti and ${^{56}}$Ni production. We tested the robustness of our results by making various assumptions about the CCSN explosion energy and mass-cut. We found a number of reactions that have a significant impact on the nucleosynthesis of ${^{44}}$Ti and ${^{56}}$Ni, particularly for lower progenitor masses. Notably, the reaction rates $^{13}{\rm N}(\alpha,p)^{16}{\rm O}$, $^{17}{\rm F}(\alpha,p)^{20}{\rm Ne}$, $^{52}{\rm Fe}(\alpha,p)^{55}{\rm Co}$, $^{56}{\rm Ni}(\alpha,p)^{59}{\rm Cu}$, $^{57}{\rm Ni}(n,p)^{57}{\rm Co}$, $^{56}{\rm Co}(p,n)^{56}{\rm Ni}$, $^{39}{\rm K}(p,\gamma)^{40}{\rm Ca}$, $^{47}{\rm V}(p,\gamma)^{48}{\rm Cr}$, $^{52}{\rm Mn}(p,\gamma)^{53}{\rm Fe}$, $^{57}{\rm Co}(p,\gamma)^{58}{\rm Ni}$, and $^{39}{\rm K}(p,\alpha)^{36}{\rm Ar}$ are influential for a large number of model conditions. Furthermore, we found the list of influential reactions identified by previous post-processing studies of CCSN shock-driven nucleosynthesis is likely incomplete, motivating future larger-scale sensitivity studies.

astro-ph.HE↗

arXiv2018

Influence of Nuclear Reaction Rate Uncertainties on Neutron Star Properties Extracted From X-ray Burst Model-Observation Comparisons

Type-I X-ray bursts can be used to determine properties of accreting neutron stars via comparisons between model calculations and astronomical observations, exploiting the sensitivity of models to astrophysical conditions. However, the sensitivity of models to nuclear physics uncertainties calls into question the fidelity of constraints derived in this way. Using X-ray burst model calculations performed with the code {\tt MESA}, we investigate the impact of uncertainties for nuclear reaction rates previously identified as influential and compare them to the impact of changes in astrophysical conditions, using the conditions that are thought to best reproduce the source GS 1826-24 as a baseline. We find that reaction rate uncertainties are unlikely to significantly change conclusions about the properties of accretion onto the neutron star surface for this source. However, we find that reaction rate uncertainties significantly hinder the possibility of extracting the neutron star mass-radius ratio by matching the modeled and observed light curves due to the influence of reaction rates on the modeled light curve shape. Particularly influential nuclear reaction rates are $^{15}{\rm O}(\alpha,\gamma)$ and $^{23}{\rm Al}(p,\gamma)$, though other notable impacts arise from $^{14}{\rm O}(\alpha,p)$, $^{18}{\rm Ne}(\alpha,p)$, $^{22}{\rm Mg}(\alpha,p)$, $^{24}{\rm Mg}(\alpha,\gamma)$, $^{59}{\rm Cu}(p,\gamma)$, and $^{61}{\rm Ga}(p,\gamma)$. Furthermore, we find that varying some nuclear reaction rates within their uncertainties has an impact on the neutron star crust composition and thermal structure that is comparable to relatively significant changes accretion conditions.

astro-ph.HE↗