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ChangHoon Hahn

Publications and source records attributed to ChangHoon Hahn.

At least 19 recordsLinked to original sources
arXiv2026

Semiresolved Stellar Populations as Distance Indicators

Galaxy distances are central to our understanding of the Universe. Despite the success of existing approaches, independent and complementary methods remain valuable for testing systematic effects and extending the applicability range of different distance metrics. Here we demonstrate that the spectrum of an individual semi-resolved stellar population encodes direct information about its distance and that valuable distance constraints can, in principle, be obtained by combining multiple independent measurements. When applied to optical spectra of bulge of the Andromeda galaxy (M\,31), we derive a stellar population-based distance of 756$\pm$57 kpc ($\mu_0 = 24.39 \pm 0.15$), in agreement with state-of-the-art measurements. Crucially, semi-resolved stellar population distances does not require secondary calibrations, although its absolute scale remains conditional on the adopted stellar population models. Our findings provide a first assessment of the feasibility of deriving stellar population-based distance estimates from simple stellar population models, motivating further tests of the broader applicability and precision of the method.

astro-ph.GA↗
arXiv2026

Alleviating prior dependencies for DESI DR1 clustering fits through reparameterization

Bayesian analyses of the full-shape clustering of Dark Energy Spectroscopic Instrument (DESI) Data Release 1 (DR1) exhibit prior-volume projection effects, whereby weakly constrained nuisance parameters of the Effective Field Theory of Large Scale Structure (EFTofLSS) shift marginalized cosmological posteriors away from the posterior maximum. We reanalyze DESI DR1 power spectrum multipoles using two complementary mitigation strategies: (i) nonlinear orthogonalization to decorrelate nuisance and cosmological parameter priors, and (ii) a fully reparameterization-invariant Jeffreys prior over all EFTofLSS coefficients, evaluated on-the-fly via closed-form Jacobians. Including data from DESI, Big-Bang Nuclesynthesis and a constraint on $n_{\mathrm{s}}$, baseline priors lead to multi-$\sigma$ projection in the Hubble parameter $H_{0}$ and dark energy equation of state parameters $w_{0}$ and $w_{a}$; the Jeffreys prior successfully recenters these posteriors to enclose the maximum a posteriori estimate within the 68\% credible regions, demonstrating clear mitigation of projection effects for these late-time expansion parameters. A hybrid Jeffreys+baseline-Gaussian configuration controls residual over-broad tails in the physical cold dark matter density $\omega_{\mathrm{c}}$ while preserving the volume correction, and is our favoured approach. We compare the credible intervals derived using our methodology to those obtained using Halo Occupation Distribution (HOD)-informed priors and to confidence intervals derived using frequentist profile likelihood analyses, finding agreement in both central values and degeneracy directions in the $w_{0}$--$w_{a}$ plane. This demonstrates that, once projection effects are properly controlled, we can make robust inferences about the late-time cosmological expansion independent of the statistical framework adopted.

astro-ph.CO↗
arXiv2026

The 3D clustering of Lyman Alpha Emitters measured with DESI

We present a clustering analysis of Lyman-$\alpha$ emitters (LAEs) using spectroscopic observations from the Dark Energy Spectroscopic Instrument (DESI) of candidates selected from the Blanco/DECam Intermediate-Band Imaging Survey (IBIS). We measure the two-point correlation function and the power spectrum, including cross-correlations with DESI quasars. Using both analytical and halo occupation distribution (HOD) simulation-based modeling, we find a linear bias of $b \sim 2.31$--$2.62$ for LAEs over the redshift range $2.26 < z < 3.41$. The analytical modeling also provides constraints on the strength of radiative transfer effects, while the HOD analysis characterizes the LAE-halo connection across multiple models. Finally, we quantify the magnitude of non-perturbative clustering effects such as Fingers of God in the LAE population, providing essential input for the accurate modeling of LAE-based cosmological analyses in forthcoming high-redshift surveys such as DESI-II.

astro-ph.CO↗
arXiv2026

Nonlinear Information from DESI Luminous Red Galaxies: An Emulator-Based Analysis of Pre- and Post-Reconstruction Power Spectra

We present joint measurements of the pre- and post-reconstruction power spectra, $P_{\rm pre}$ and $P_{\rm post}$, together with their cross-power spectrum, $P_{\rm cross}$, for the Luminous Red Galaxies (LRGs) in the DESI Data Release 1 (DR1). We jointly analyse these observables with an emulator-based full-shape modeling framework, thereby, for the first time, we extract complementary nonlinear information from the galaxy density field before and after reconstruction in real survey data. Specifically, including $P_{\rm post}$ and $P_{\rm cross}$ in addition to $P_{\rm pre}$ (hereafter $P_{\rm all}$) yields an improvement of approximately $18$-$27\%$ in the $\sigma_8$ constraint in both $\Lambda$CDM and $w$CDM, depending on the redshift bin, relative to the $P_{\rm pre}$-only analysis with the cosmic microwave background distance priors (hereafter CMB). In $w$CDM, the joint CMB+$P_{\rm all}$ analysis can tighten the constraints on $w$ by approximately $5$-$15\%$ across the two LRG redshift bins, compared to the CMB+$P_{\rm pre}$ combination. Further incorporating the Type Ia supernova dataset and comparing the cosmological constraints in $w$CDM from each individual power-spectrum component with those from the full combination, we find that $P_{\rm all}$ consistently provides the tightest constraints. From the joint CMB+$P_{\rm all}$+DES-Dovekie dataset, we obtain $\Omega_m = 0.314 \pm 0.0048$ and $w = -0.988 \pm 0.023$ for the \texttt{LRG1} sample, and $\Omega_m = 0.318 \pm 0.0046$ and $w = -0.988 \pm 0.025$ for \texttt{LRG2}. These results demonstrate that combining pre- and post-reconstruction power spectra with their cross-correlation enables DESI to harvest additional nonlinear information, leading to tighter constraints on cosmological parameters.

astro-ph.CO↗
arXiv2026

HaloFlow II: Robust Galaxy Halo Mass Inference with Domain Adaptation

Precise halo mass ($M_h$) measurements are crucial for cosmology and galaxy formation. HaloFlow introduced a simulation-based inference (SBI) framework that uses state-of-the-art simulated galaxy images to precisely infer $M_h$. However, for HaloFlow to be applied to observations, it must be generalizable even when the underlying galaxy formation physics differ from those in the simulations on which it was trained. Without this generalization, HaloFlow produces biased and overconfident $M_h$ posteriors when applied to simulations with different physics. We introduce HaloFlow$^{\rm DA}$, an extension of HaloFlow that integrates domain adaptation (DA) with SBI to mitigate these cross-simulation shifts. Using synthetic galaxy images forward-modeled from the IllustrisTNG, EAGLE, and SIMBA simulations, we test two DA methods: Domain-Adversarial Neural Networks (DANN) and Maximum Mean Discrepancy (MMD). Incorporating DA significantly reduces bias and improves calibration, with MMD achieving the most stable performance, lowering the normalized residual metric, $\beta$, by an average of 31% and up to 57% when trained and tested on different simulations. Overall, HaloFlow$^{\rm DA}$ produces more robust, less biased with similar precision, $M_h$ constraints than the standard approach using the stellar-to-halo mass relation. HaloFlow$^{\rm DA}$ enables consistent, simulation-trained inference models to generalize across domains, establishing a foundation for robust $M_h$ inference from real HSC-SSP observations.

astro-ph.GA↗
arXiv2026

Extremely Metal-Poor Galaxies in DESI DR1: Connections to Galaxies in the Early Universe

Extremely Metal-Poor Galaxies (XMPGs), defined as having metallicities below 10\% of the solar value, are considered possible local analogs to primordial systems and offer a unique window into early galaxy evolution. This study presents a large-scale search for XMPGs using data from the Dark Energy Spectroscopic Instrument DR1, systematically evaluating their resemblance to high-redshift galaxies. From a parent sample of over 14 million galaxies, we identify 656 (551 new) confirmed XMPGs and 767 (670 new) high-quality candidates via the direct $T_{\mathrm{e}}$ method. Results reveal that XMPGs follow a distinct star-forming main sequence (SFMS) that is elevated and shallower than that of the comparing star-forming galaxies. Notably, at higher stellar masses ($M_{\star} > 10^{7.5} M_{\odot}$), the XMPG SFMS converges with the sequence observed in high-redshift galaxies by James Webb Space Telescope (JWST), indicating that mature XMPGs sustain star formation rates comparable to their primordial counterparts. Furthermore, XMPGs consistently deviate below the local fundamental metallicity relation, mirroring high-redshift galaxy behavior. These findings demonstrate that XMPGs not only exhibit low metallicities but also preserve scaling relations characteristic of the early Universe, confirming their potential value as local laboratories for studying early galaxy formation processes.

astro-ph.GA↗
arXiv2025

The Denario project: Deep knowledge AI agents for scientific discovery

We present Denario, an AI multi-agent system designed to serve as a scientific research assistant. Denario can perform many different tasks, such as generating ideas, checking the literature, developing research plans, writing and executing code, making plots, and drafting and reviewing a scientific paper. The system has a modular architecture, allowing it to handle specific tasks, such as generating an idea, or carrying out end-to-end scientific analysis using Cmbagent as a deep-research backend. In this work, we describe in detail Denario and its modules, and illustrate its capabilities by presenting multiple AI-generated papers generated by it in many different scientific disciplines such as astrophysics, biology, biophysics, biomedical informatics, chemistry, material science, mathematical physics, medicine, neuroscience and planetary science. Denario also excels at combining ideas from different disciplines, and we illustrate this by showing a paper that applies methods from quantum physics and machine learning to astrophysical data. We report the evaluations performed on these papers by domain experts, who provided both numerical scores and review-like feedback. We then highlight the strengths, weaknesses, and limitations of the current system. Finally, we discuss the ethical implications of AI-driven research and reflect on how such technology relates to the philosophy of science. We publicly release the code at https://github.com/AstroPilot-AI/Denario. A Denario demo can also be run directly on the web at https://huggingface.co/spaces/astropilot-ai/Denario, and the full app will be deployed on the cloud.

cs.AI↗
arXiv2025

Dwarf galaxy halo masses from spectroscopic and photometric lensing in DESI and DES

We present the most precise and lowest-mass weak lensing measurements of dwarf galaxies to date, enabled by spectroscopic lenses from the Dark Energy Spectroscopic Instrument (DESI) and photometric lenses from the Dark Energy Survey (DES) calibrated with DESI redshifts. Using DESI spectroscopy from the first data release, we construct clean samples of galaxies with median stellar masses $\log_{10}(M_*/M_{\odot})=8.3-10.1$ and measure their weak lensing signals with sources from DES, KiDS, and SDSS, achieving detections with $S/N$ up to 14 for dwarf galaxies ($\log_{10}(M_*/M_{\odot})<$9.25) -- opening up a new regime for lensing measurements of low-mass systems. Leveraging DES photometry calibrated with DESI, we extend to a photometric dwarf sample of over 700,000 galaxies, enabling robust lensing detections of dwarf galaxies with combined $S/N=38$ and a significant measurement down to $\log_{10}(M_*/M_{\odot})=8.0$. We show that the one-halo regime (scales $\lesssim 0.15h^{-1}\rm Mpc$) is insensitive to various systematic and sample selection effects, providing robust halo mass estimates, while the signal in the two-halo regime depends on galaxy color and environment. These results demonstrate that DESI already enables precise dwarf lensing measurements, and that calibrated photometric samples extend this capability. Together, they pave the way for novel constraints on dwarf galaxy formation and dark matter physics with upcoming surveys like the Vera C. Rubin Observatory's LSST.

astro-ph.GA↗
arXiv2025

Frequentist Cosmological Constraints from Full-Shape Clustering Measurements in DESI DR1

We present a frequentist analysis of clustering measurements from Data Release 1 of the Dark Energy Spectroscopic Instrument (DESI) using the standard profile likelihood method. While Bayesian inferences for effective field theory models of galaxy clustering can be highly sensitive to prior choices for extended cosmological models, frequentist inferences are not susceptible to such effects. We compare frequentist and Bayesian constraints for the parameter set $\{\sigma_8, H_0, \Omega_{\rm{m}}, w_0, w_a\}$ using the full-shape power spectrum multipoles, post-reconstruction baryon acoustic oscillation (BAO) measurements, and external datasets from the CMB and type Ia supernovae measurements. The frequentist confidence intervals are significantly shifted relative to the Bayesian credible intervals for the $w_0w_a$CDM model, unless supernovae data are included. When DESI full-shape and BAO data are fit jointly, we obtain the following $1\sigma$ frequentist confidence intervals for $\Lambda$CDM ($w_0w_a$CDM): $\sigma_8 = 0.863^{+0.048}_{-0.040} , \ H_0 = 68.96^{+0.81}_{-0.80} \ \rm{km \ s^{-1}Mpc^{-1}} , \ \Omega_{\rm{m}} = 0.3034\pm0.0110$ ($\sigma_8 = 0.782^{+0.060}_{-0.036} , \ H_0 = 63.7^{+4.2}_{-2.0} \ \rm{km \ s^{-1}Mpc^{-1}} , \ \Omega_{\rm{m}} = 0.378^{+0.024}_{-0.047} , \ w_0 = -0.16^{+0.10}_{-0.50} , \ w_a = -3.0^{+1.7}_{}$), corresponding to 0.8$\sigma$, 0.3$\sigma$, 0.7$\sigma$ (2.1$\sigma$, 4.1$\sigma$, 6.5$\sigma$, 6.3$\sigma$, 6.6$\sigma$) shifts between the maximum likelihood estimate and the Bayesian posterior mean for $\Lambda$CDM ($w_0w_a$CDM) respectively.

astro-ph.CO↗
arXiv2025

Mitigating Model Misspecification in Simulation-Based Inference for Galaxy Clustering

Simulation-based inference (SBI) has become an important tool in cosmology for extracting additional information from observational data using simulations. However, all cosmological simulations are approximations of the actual universe, and SBI methods can be sensitive to model misspecification - particularly when the observational data lie outside the support of the training distribution. We present a method to improve the robustness of cosmological analyses under such conditions. Our approach first identifies and discards components of the summary statistics that exhibit inconsistency across related simulators, then learns a transformation that brings the observation back within the support of the training distribution. We apply our method in the context of a recent SimBIG SBI galaxy clustering analysis using the wavelet scattering transform (WST) summary statistic. The original analysis struggled to produce robust constraints for certain subsets of WST coefficients, where the observational data appeared out-of-distribution (OOD) relative to the training data. We show that our method enables robust cosmological inference and resolves OOD issues, while preserving most of the constraining power. In particular, the improved SimBIG WST analysis yields $\Lambda$CDM constraints of $\Omega_m = 0.32^{+0.02}_{-0.02}$ and $\sigma_8 = 0.80^{+0.02}_{-0.02}$, which are respectively $1.4\times$ and $3.1\times$ tighter than those from a standard perturbation-theory-based power spectrum analysis, confirming the significant information gain of WST summary statistics. The proposed method is easily applicable to other cosmological SBI contexts and represents a step toward more robust SBI pipelines.

astro-ph.CO↗
arXiv2025

Reconstructing Quasar Spectra and Measuring the Ly$\alpha$ Forest with ${\rm S{\scriptsize pender}Q}$

Quasar spectra carry the imprint of foreground intergalactic medium (IGM) through absorption features. In particular, absorption caused by neutral hydrogen gas, the ``Ly$\alpha$ forest,'' is a key spectroscopic tracer for cosmological analyses used to measure cosmic expansion and test physics beyond the standard model. Despite their importance, current methods for measuring LyA absorption cannot directly derive the intrinsic quasar continuum and make strong assumptions on its shape, thus distorting the measured LyA clustering. We present SpenderQ, a ML-based approach for directly reconstructing the intrinsic quasar spectra and measuring the LyA forest from observations. SpenderQ uses the Spender spectrum autoencoder to learn a compact and redshift-invariant latent encoding of quasar spectra, combined with an iterative procedure to identify and mask absorption regions. To demonstrate its performance, we apply SpenderQ to 400,000 synthetic quasar spectra created to validate the Dark Energy Spectroscopic Instrument Year 1 LyA cosmological analyses. SpenderQ accurately reconstructs the true intrinsic quasar spectra, including the broad LyB, LyA, SiIV, CIV, and CIII emission lines. Redward of LyA, SpenderQ provides percent-level reconstructions of the true quasar spectra. Blueward of LyA, SpenderQ reconstructs the true spectra to < 5\%. SpenderQ reproduces the shapes of individual quasar spectra more robustly than the current state-of-the-art. We, thus, expect it will significantly reduce biases in LyA clustering measurements and enable studies of quasars and their physical properties. SpenderQ also provides informative latent variable encodings that can be used to, e.g., classify quasars with Broad Absorption Lines. Overall, SpenderQ provides a new data-driven approach for unbiased LyA forest measurements in cosmological, quasar, and IGM studies.

astro-ph.CO↗
arXiv2025

Simulation-based inference of galaxy properties from JWST pixels

We present an efficient Bayesian SED-fitting framework tailored to multiwavelength pixel photometry from the JWST Advanced Deep Extragalactic Survey (JADES). Our method employs simulation-based inference to enable rapid posterior sampling across galaxy pixels, leveraging the unprecedented spatial resolution, wavelength coverage, and depth provided by the survey. It is trained on synthetic photometry generated from MILES stellar population models, incorporating both parametric and non-parametric SFHs, realistic noise, and JADES-like filter sensitivity thresholds. We validate this amortised inference approach on mock datasets, achieving robust and well-calibrated posterior distributions, with an $R^2$ score of 0.99 for stellar mass. Applying our pipeline to real observations, we derive spatially resolved maps of stellar population properties down to $\mathrm{S/N}_{\rm{pixel}}=5$ (averaged over F277W, F356W, F444W) for 1083 JADES galaxies and ~2 million pixels with spectroscopic redshifts. These maps enable the identification of dusty or starburst regions and offer insights into mass growth and the structural assembly. We assess the outshining phenomenon by comparing pixel-based and integrated stellar mass estimates, finding limited impact only in low-mass galaxies ($<10^8M_{\odot}$) but systematic differences of ~0.20 dex linked to SFH priors. With an average posterior sampling speed of $10^{-4}$ seconds per pixel and a total inference time of ~1 CPU-day for the full dataset, our model offers a scalable solution for extracting high-fidelity stellar population properties from HST+JWST datasets, opening the way for statistical studies at sub-galactic scales.

astro-ph.GA↗
arXiv2025

Enhancing DESI DR1 Full-Shape analyses using HOD-informed priors

We present an analysis of DESI Data Release 1 (DR1) that incorporates Halo Occupation Distribution (HOD)-informed priors into Full-Shape (FS) modeling of the power spectrum based on cosmological perturbation theory (PT). By leveraging physical insights from the galaxy-halo connection, these HOD-informed priors on nuisance parameters substantially mitigate projection effects in extended cosmological models that allow for dynamical dark energy. The resulting credible intervals now encompass the posterior maximum from the baseline analysis using gaussian priors, eliminating a significant posterior shift observed in baseline studies. In the $\Lambda$CDM framework, a combined DESI DR1 FS information and constraints from the DESI DR1 baryon acoustic oscillations (BAO)-including Big Bang Nucleosynthesis (BBN) constraints and a weak prior on the scalar spectral index-yields $\Omega_{\rm m} = 0.2994\pm 0.0090$ and $\sigma_8 = 0.836^{+0.024}_{-0.027}$, representing improvements of approximately 4% and 23% over the baseline analysis, respectively. For the $w_0w_a$CDM model, our results from various data combinations are highly consistent, with all configurations converging to a region with $w_0 > -1$ and $w_a < 0$. This convergence not only suggests intriguing hints of dynamical dark energy but also underscores the robustness of our HOD-informed prior approach in delivering reliable cosmological constraints.

astro-ph.CO↗
arXiv2025

Dynamical Dark Energy in light of the DESI DR2 Baryonic Acoustic Oscillations Measurements

Understanding whether cosmic acceleration arises from a cosmological constant or a dynamical component is a central goal of cosmology, and the Dark Energy Spectroscopic Instrument (DESI) enables stringent tests with high-precision distance measurements. We analyze baryon acoustic oscillation (BAO) measurements from DESI Data Release 1 (DR1) and Data Release 2 (DR2), combined with Type Ia supernovae and a cosmic microwave background (CMB) distance prior. With the larger statistical power and wider redshift coverage of DR2, the preference for dynamical dark energy does not diminish relative to DR1. Using both a shape-function reconstruction and non-parametric approaches with a Horndeski-motivated correlation prior, we find that the dark-energy equation of state $w(z)$ varies with redshift. BAO data alone yield modest constraints, but in combination with independent supernova compilations and the CMB prior they strengthen the evidence for dynamics. Bayesian model comparison shows moderate support for departures from $\Lambda$CDM when multiple degrees of freedom in $w(z)$ are allowed, corresponding to $\approx3\sigma$ tension with $\Lambda$CDM (and higher for some data sets). Despite methodological differences, our results are consistent with companion DESI papers, underscoring the complementarity of approaches. Possible systematics remain under study; forthcoming DESI, \emph{Euclid}, and next-generation CMB data will provide decisive tests.

astro-ph.CO↗
arXiv2025

The Optical and Infrared Are Connected

Galaxies are often modelled as composites of separable components with distinct spectral signatures, implying that different wavelength ranges are only weakly correlated. They are not. We present a data-driven model which exploits subtle correlations between physical processes to accurately predict infrared (IR) WISE photometry from a neural summary of optical SDSS spectra. The model achieves accuracies of $\chi^2_N \approx 1$ for all photometric bands in WISE, as well as good colors. We are able to tightly constrain typically IR-derived properties, e.g., the bolometric luminosities of AGN and dust parameters such as $\mathrm{q_{PAH}}$. We also test whether current SED-fitting methods reproduce such panchromatic relations, but find their predictions biased and overconfident, likely due to model misspecification, with correlated biases in star-formation rates and AGN luminosities being most evident. To help improve SED models, we determine which features of the optical spectrum are responsible for our improved predictions, and identify several lines (CaII, SrII, FeI, [OII] and H$\alpha$), which point to the complex chronology of star formation and chemical enrichment being incorrectly modelled.

astro-ph.GA↗
arXiv2024

Cross-correlation of Luminous Red Galaxies with ML-selected AGN in HSC-SSP II: AGN classification and clustering with DESI spectroscopy

An unresolved question in studies of active galactic nuclei (AGN) is whether their different classes probe different evolutionary stages of black hole--host galaxy interaction. We present the projected two-point cross-correlation function between a sample of Dark Energy Spectroscopic Instrument (DESI)-matched AGN selected from Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) optical + Wide-field Infrared Survey Explorer ($WISE$) mid-IR photometry, and DESI-designated luminous red galaxies, for $z\in 0.5-1.0$. The total overlap area is 43.4 deg$^2$, including $\sim27,000$ spectroscopic LRGs in our redshift range. We visually classified 1,991 matched HSC-DESI objects in our redshift range, spectroscopically confirming that 1,517 ($76\%$) of them are AGN. Of these 1,517 objects, $73\%$ are broad-line AGN, $27\%$ are obscured AGN. We infer that the parent HSC+$WISE$ AGN catalog has a number density of at least $\sim 240$ deg$^{-2}$, confirming it is one of the most complete optical/infrared AGN catalog to date. We investigate the AGN clustering as a function of the spectroscopic classification and infer the halo mass for each sample. The inferred average mass of the halos $\langle M_h\rangle$ that host unobscured broad-line AGN ($M_h \approx 10^{13.4}h^{-1}M_\odot$) is $\sim 5.5\times$ larger than the halos that host obscured AGN ($M_h \approx 10^{12.6}\, h^{-1}M_\odot$), at $2.8\sigma$ significance, in the same sense as our prior work based on photometric redshifts. This suggests that we may relax our concerns about systematic shifts in the inferred redshift distribution producing this halo mass difference. While we do not yet find statistically significant spectroscopic evidence that unobscured AGN reside in more massive halos than their obscured counterparts, further analyses are necessary to distinguish if more complex evolutionary histories are needed to model these AGN populations.

astro-ph.GA↗
arXiv2024

Inhomogeneous Dust Biases Photometric Redshifts and Stellar Masses for LSST

Spectral energy distribution (SED) modeling is one of the main methods to estimate galaxy properties, such as photometric redshifts, $z$, and stellar masses, $M_*$, for extragalactic imaging surveys. SEDs are currently modeled as light from a composite stellar population attenuated by a uniform foreground dust screen, despite evidence from simulations and observations that find large spatial variations in dust attenuation due to the detailed geometry of stars and gas within galaxies. In this work, we examine the impact of this simplistic dust assumption on inferred $z$ and $M_*$ for Rubin LSST. We first construct synthetic LSST-like observations ($ugrizy$ magnitudes) from the NIHAO-SKIRT catalog, which provides SEDs from high-resolution hydrodynamic simulations using 3D Monte Carlo radiative transfer. We then infer $z$ and $M_*$ from the synthetic observations using the PROVABGS Bayesian SED modeling framework. Overall, the uniform dust screen assumption biases both $z$ and $M_*$ in galaxies, consistently and significantly for galaxies with dust attenuation $A_V \gtrsim 0.5$, and likely below. The biases depend on the orientation in which the galaxies are observed. At $z=0.4$, $z$ is overestimated by $\sim$0.02 for face-on galaxies and $M_*$ is underestimated by $\sim$0.4 dex for edge-on galaxies. The bias in photo-$z$ is equivalent to the desired redshift precision level of LSST "gold sample" and will be larger at higher redshifts. Our results underscore the need for SED models with additional flexibility in the dust parameterization to mitigate significant systematic biases in cosmological analyses with LSST.

astro-ph.GA↗
arXiv2024

The MOST Hosts Survey: spectroscopic observation of the host galaxies of ~40,000 transients using DESI

We present the MOST Hosts survey (Multi-Object Spectroscopy of Transient Hosts). The survey is planned to run throughout the five years of operation of the Dark Energy Spectroscopic Instrument (DESI) and will generate a spectroscopic catalog of the hosts of most transients observed to date, in particular all the supernovae observed by most public, untargeted, wide-field, optical surveys (PTF/iPTF, SDSS II, ZTF, DECAT, DESIRT). Scientific questions for which the MOST Hosts survey will be useful include Type Ia supernova cosmology, fundamental plane and peculiar velocity measurements, and the understanding of the correlations between transients and their host galaxy properties. Here, we present the first release of the MOST Hosts survey: 21,931 hosts of 20,235 transients. These numbers represent 36% of the final MOST Hosts sample, consisting of 60,212 potential host galaxies of 38,603 transients (a transient can be assigned multiple potential hosts). Of these galaxies, 40% do not appear in the DESI primary target list and therefore require a specific program like MOST Hosts. Of all the transients in the MOST Hosts list, only 26.7% have existing classifications, and so the survey will provide redshifts (and luminosities) for nearly 30,000 transients. A preliminary Hubble diagram and a transient luminosity-duration diagram are shown as examples of future potential uses of the MOST Hosts survey. The survey will also provide a training sample of spectroscopically observed transients for photometry-only classifiers, as we enter an era when most newly observed transients will lack spectroscopic classification. The MOST Hosts DESI survey data will be released through the Wiserep platform on a rolling cadence and updated to match the DESI releases. Dates of future releases and updates are available through the https://mosthosts.desi.lbl.gov website.

astro-ph.HE↗