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Joshua S. Speagle

Publications and source records attributed to Joshua S. Speagle.

At least 19 recordsLinked to original sources
arXiv2026

Untangling Similarities and Differences in Star Formation Histories Across Cosmological Simulations

How galaxies assemble depends on processes ranging from small-scale baryonic physics to large-scale cosmological assembly, which are difficult to disentangle with observations alone. Using $\sim$63,000 simulated galaxies from seven cosmological simulations spanning hydrodynamical, semi-analytic, and semi-empirical models, we explore how specific star formation histories (sSFHs) vary across modelling assumptions. Using non-linear dimensionality reduction (UMAP), we find that sSFH shape variation organizes along a few smooth axes in a 2D embedding, broadly corresponding to (1) peak timing, (2) peak sharpness, and (3) subsequent quenching rate. We identify sSFH shapes that many simulations share ("common"), well described to first order by dark matter halo assembly, and a few tied to individual simulations ("unique"), which likely reflect subgrid and parametrization choices such as merger-triggered burst prescriptions. A notable exception is a common population of rapidly quenched galaxies found across most simulations, likely driven by e.g., active galactic nucleus (AGN) feedback, that halo assembly alone cannot reproduce. The origin of the most distinctive unique shape, an Illustris-dominated history with an early peak and slow decline, remains unclear, but we test the effects of short-term variability. With a multi-objective auto-encoder neural network, we recover the simulation that a given sSFH is drawn from with $\sim$95\% out-of-sample accuracy; ablation tests indicate this information is embedded in long-timescale sSFH morphology rather than short-term variability. These results provide avenues for theoretical and observational follow-up connecting star formation histories to subgrid physical models.

astro-ph.GA↗
arXiv2026

How to Craft the Right Language AI Policy For Your Research Group (Some Assembly Required)

Language AI is rapidly becoming part of the astronomy research ecosystem, prompting research teams to develop policies governing its use. But resources and advice for AI adoption assume that all research groups share the same goals and values. This paper lays out an argument for why there is no single "correct" AI policy for astronomy research groups. Instead, we introduce four research laboratory archetypes with competing research priorities, and we use them to explore how a small laboratory or research group's priorities shape decisions about AI's impact on research productivity, scientist development, scientific integrity, and data governance. Rather than prescribing a universal set of rules, this paper provides a framework for aligning AI policies with a group's scientific values and mission. The objective is to help research leaders decide how Language AI should be used within their particular research environment.

astro-ph.IM↗
arXiv2026

Unresolved Binary Systems in the Rubin Era I: An Autoencoder Framework for Binary Identification Applied to 47 Tucanae

Binary systems are extremely common and influence many areas of astrophysics, such as stellar evolution and cluster dynamics. In this work, we introduce flexAE, a framework that combines a classical autoencoder architecture with a dedicated classifier component. It is designed to distinguish between single stars and binary systems based on their broadband spectral energy distributions, using all available photometry simultaneously while accounting for observational uncertainties. We demonstrate the capability of flexAE by using it to identify unresolved main sequence binaries in the outskirts of the 47 Tucanae globular cluster (NGC 104) with photometric data from the Vera C. Rubin Observatory. We train the model on a simulated sample of single star and binary systems created with stellar atmosphere models. The model accurately reconstructs the photometric input features and achieves a classification accuracy of 0.85 on the simulated test set. We then apply the trained model to a sample of 1,424 cluster members found in Rubin DP1 with reliable $gri$ photometry, of which 32 are identified by the model as unresolved main sequence binaries. This corresponds to an observed binary fraction of $2.2^{+0.5}_{-0.3}$%. We estimate that the intrinsic main sequence binary fraction lies between $3.7^{+0.8}_{-0.5}$% and $7.5^{+1.5}_{-1.1}$%. The binary fraction stays constant between 18-36 arcmin (5.7-11.4 half-light radii) from the cluster center. We highlight that flexAE can be easily adapted for other use cases. This makes flexAE well-suited for large-scale binary classification in upcoming wide-field surveys, such as Rubin LSST. The code is publicly available at https://github.com/tobiasgeron/flexAE.

astro-ph.SR↗
arXiv2026

Model Predictive Scoring Shows Specific BAO Observations (not SNIa) Drives $w_0w_a$ Tension

The recent analyses of DESI DR2 BAO, Planck CMB and various Supernovae datasets have shown preferences for evolving dark energy at various levels of significance, under frequentist model comparison tests. However, the same analysis done with a pure Bayesian model comparison test can lead to the opposite conclusion due to the impact of $w_0w_a$ priors. In contrast to these in-sample model comparison route, we approach the problem along a third axis -- which model better predicts unseen data. We use the Expected Log Predictive Density (ELPD) metric to score the predictiveness of $Λ$CDM and $w_0w_a$CDM models, using the leave-one-redshift-block-out cross-validation estimator. The comparison metric $Δ$ELPD is out-of-sample, unlike $Δχ^2_{\rm MAP}$ and Bayes Factor, and insensitive to prior width, unlike the Bayes factor. Aggregated, we find modest preferences for $w_0 w_a$CDM, primarily driven by the BAO. Decomposing the $Δ$ELPD score by redshift we find that a single BAO block, LRG2 ($z = 0.706$), supplies the entire BAO preference. Interestingly, the biggest outlier point, LRG1, contributes little to the model predictive scoring because both $Λ$CDM and $w_0w_a$CDM fail to predict the observation by equal amount. On the other hand, all SNIa datasets' $Δ$ELPD scores are consistent with $0$, indicating that at an out-of-distribution predictive level, the $w_0w_a$CDM tension is entirely driven by one BAO point, not SNIa.

astro-ph.CO↗
arXiv2026

EncoTESS: Age-Sensitive Encodings from Raw TESS Light Curves

Main sequence stars of spectral types late F through M exhibit systematic variability in photometric light curves, particularly when they are young. Rotational modulation of starspots manifests as quasi-sinusoidal variability, which enables the measurement of rotation periods. Variability can also be stochastic, as in stellar flaring. However, since measurements of stochastic processes depend on the time of observation, they are typically noisier. Considering that different manifestations of variability have unique observational nuances, models that naturally unify these are incredibly useful for stellar characterization. Towards this goal, we have developed EncoTESS: a Time Series Foundation Model (TSFM) trained on a subset of TESS 2-min light curves. EncoTESS is specifically designed to handle the observational noise, heteroskedastic measurements, irregular sampling, and large data gaps common to TESS data. It is also ~1% of the size of a typical literature TSFM, so can be run easily on a modern laptop. EncoTESS encodes light curves into a fixed-size latent parameter space, which can be used to infer physical stellar properties and recovers light curve summary statistics well. EncoTESS outperforms rotation period and variability amplitude as age indicators for stars that have not converged onto the slow rotator sequence yet; broadly these include K and M stars less than ~100 Myr, and M stars less than ~1 Gyr. We focus on age inference as an application of EncoTESS in this work, but other downstream tasks such as stellar classification could also be explored. The architecture of EncoTESS enables its future extension to TESS light curves of all cadences, and additional surveys such as Kepler and the upcoming PLATO mission. The core EncoTESS framework and library of encodings produced for the stars used in this work are publicly available at https://github.com/philvanlane/encotess.

astro-ph.SR↗
arXiv2026

Integral Field Unit Spectroscopy with One Fiber

Integral field unit (IFU) spectroscopy provides spatially resolved spectra across galaxies, offering crucial insights into their evolution. However, its high observational cost limits current IFU datasets to $\sim 10^4$ objects. We present a multi-modal, probabilistic foundation model that predicts high-resolution spectra with calibrated uncertainties at arbitrary spatial locations within a galaxy directly from broadband images. Built on a masked autoencoder framework, our architecture injects fiber positional encodings and redshift aware wavelength encodings, enabling spatially conditioned predictions. Trained on 4.7 million images and single fiber spectroscopic observations from the Dark Energy Spectroscopic Instrument (DESI) survey, our model exploits the natural variance of fiber placements and the morphological self-similarity of galaxies to achieve IFU-like capabilities without any IFU training data. Predicted emission line flux maps match independent IFU observations from the Mapping Nearby Galaxies at APO (MaNGA) survey, with performance comparable to a supervised baseline trained directly on IFU data.

astro-ph.GA↗
arXiv2026

On the correlation between globular clusters and the distribution of dark matter in galaxy clusters: the case of Abell 2744

Globular clusters (GCs) lie scattered around the inner $40\%$ of the virial radius of galaxy clusters, potentially being excellent tracers of the underlying mass distribution. In this paper, we present a statistical method based on assuming that the location of GCs around a galaxy cluster follows an inhomogenous spatial Poisson point process, and we use this method to assess to which galactic component GCs are better tracers of. We apply the method to the galaxy cluster Abell 2744, and we find that the spatial distribution of bright GCs roughly traces the three main interacting clumps in the galaxy cluster, alongside other galaxies with sizeable GC populations. The GC populations are more closely correlated to the predicted mass maps than any other galactic component (Spearman rank coefficients $>0.7$). A perk of this statistical method is that it allows us to distinguish to which map the agreement is closest to. In particular, we find that the Bright Blue GCs are compatible with the mass map solely derived from weak lensing, suggesting that they can provide complementary and independent information on the mass distribution in galaxy clusters with a similar level of detail to that of weak lensing. This statistical method is available in a public repository, and combined with catalogs of GCs in galaxy clusters at different cosmic epochs, it provides an independent method for investigating the mass distribution in these galactic environments.

astro-ph.GA↗
arXiv2026

SPYGLASS. VII-B. Tracing the Fragments of Massive Star Formation Using Low-Mass Associations

New observations from the Gaia spacecraft have traced an emerging demographic of low-mass associations disconnected from larger associations or GMCs. The first of these associations were recently characterized, but the star-forming environments they trace remain unknown. Using new velocities and ages alongside literature catalogs, we uncover the origins of 16 low-mass associations ($M\lesssim100$ M$_{\odot}$, $τ\lesssim50$ Myr) using dynamical traceback. We reveal that three groups of currently disparate populations share common formation sites, comprising the Leo, CaNMoS, and AquENS associations. Twelve of 16 associations have plausible connections to larger complexes, six of which form while moving outward from well-established multi-generational star-forming events that drive known or suspected bubbles. We find that feedback from the oldest co-spatial and co-moving relatives of these associations can explain the current morphologies of the Local and Orion-Eridanus Bubbles, along with the formation of related associations like Sco-Cen and Orion OB1. Most remaining populations show evidence for triggered star formation. In the Leo Association, high vertical velocities and a deceleration signature suggest that it formed out of an intermediate velocity cloud colliding with gas in Orion, which would make it the first known case of star formation in one of these clouds. The other newly defined associations show similar asymmetric velocity signatures, such as CaNMoS, which may trace bubble-driven acceleration or a cloud collision. We conclude that the lowest-mass young associations remain undiscovered, and that these populations may have a critical role revealing the small gas overdensities that trace the processes sculpting galactic star formation.

astro-ph.GA↗
arXiv2026

Homogeneous Stellar Parameters from Heterogeneous Spectra with Deep Learning

Large-scale spectroscopic surveys have collectively observed millions of stars across the Milky Way, but each derives stellar labels using independent pipelines with distinct modelling assumptions, introducing systematic offsets that obscure signals in chemical space and hinder large-scale Galactic archaeology. We present a unified deep-learning framework that delivers atmospheric parameters, chemical abundances for 20 elements, distances, and ages -- all on a single, self-consistent scale -- for an arbitrary number of spectroscopic surveys simultaneously. Our approach uses a Transformer model that ingests spectra of arbitrary wavelength range and resolution, trained end-to-end as a single model across all surveys, eliminating the need for post-hoc recalibration. We apply this framework to spectra from APOGEE DR17, GALAH DR3, DESI DR1, and $\textit{Gaia}$ RVS DR3, spanning resolutions from R ~ 2,000 to 28,000 and wavelengths from the optical to the near-infrared. On high-resolution APOGEE spectra the model achieves precisions of $18~$K in $\textrm{T}_{\rm eff}$, $0.04~$dex in $\textrm{log}\,\textit{g}$, $0.015~$dex in [Fe/H], and ${<}\,0.03~$dex across all abundances; on lower-resolution DESI spectra, typical precisions are $51~$K, $0.09~$dex, $0.04~$dex, and ${\sim}\,0.06~$dex, respectively. Cross-survey comparisons demonstrate that labels for the same stars observed by different surveys are consistent within model uncertainties; we further validate against external distance catalogs and open cluster metallicities and ages. The resulting homogeneous catalog enables Galactic archaeology at unprecedented scale and consistency, and the framework is readily extensible to forthcoming spectroscopic surveys such as SDSS-V, WEAVE, and 4MOST. The catalog is publicly available at https://doi.org/10.5281/zenodo.19830515.

astro-ph.GA↗
arXiv2026

Finding the boundary: Using galaxy membership to inform galaxy cluster extent through machine learning

The spatial extent of the environment's impact on galaxies marks a transitional region between cluster and field galaxies. We present a data-driven method to identify this region in galaxy clusters with masses $M_{200\rm ,mean}>10^{13} M_{\odot}$ at $z = 0$. Using resolved galaxy samples from the largest simulation volume of IllustrisTNG (TNG300-1), we examine how galaxy properties vary as a function of distance to the closest cluster. We train neural networks to classify galaxies into cluster and field galaxies based on their intrinsic properties. Using this classifier, we present the first quantitative and probabilistic map of the transition region. It is represented as a broad and intrinsically scattered region near cluster outskirts, rather than a sharp physical boundary. This is the physical detection of a mixed population. In order to determine transition regions of different physical processes by training property-specific models, we categorise galaxy properties based on their underlying physics, i.e. gas, stellar, and dynamical. Changes to the dynamical properties dominate the innermost regions of the clusters of all masses. Stellar properties and gas properties, on the other hand, exhibit transitions at similar locations for low mass clusters, yet gas properties have transitions in the outermost regions for high mass clusters. These results have implications for cluster environmental studies in both simulations and observations, particularly in refining the definition of cluster boundaries while considering environmental preprocessing and how galaxies evolve under the effect of the cluster environment.

astro-ph.GA↗
arXiv2026

Disk Wind Feedback from High-mass Protostars. V. Application of Multi-Modal Machine Learning to Characterize Outflow Properties

Characterizing protostellar outflows is fundamental to understanding star formation feedback, yet traditional methods are often hindered by projection effects and complex morphologies. We present a multi-modal deep learning framework that jointly leverages spatial and spectral information from CO observations to infer protostellar mass, inclination, and position angle ($PA$). Our model, trained on synthetic ALMA observations generated from 3D magnetohydrodynamic simulations, utilizes a cross-attention fusion mechanism to integrate morphological and kinematic features with probabilistic uncertainty estimation. Our results demonstrate that Vision Transformer architectures significantly outperform convolutional networks, showing remarkable robustness to reduced spatial resolution. Interpretability analysis reveals a physically consistent hierarchy: spatial features dominate across all parameters, whereas spectral profiles provide secondary constraints for mass and inclination. Applied to observational ALMA data, the framework delivers stable mass and $PA$ estimates with exceptionally tightly constrained inclination angles. This study establishes multi-modal deep learning as a powerful, interpretable tool for overcoming projection biases in high-mass star formation studies.

astro-ph.GA↗
arXiv2026

It's a matter of time: Empirical Constraints on Supernova Yields and Delay Times from Dwarf Spheroidal Galaxies

The chemical abundances of a stellar population encode information about nucleosynthesis and its astrophysical sites, but this information is confounded by the specific star formation history of the host galaxy. As a result, placing empirical constraints on supernova yields and timing using abundances has been very challenging. We introduce a galactic chemical evolution model DLEIY that uses an observed star formation history and metallicity distribution to reduce these confounding factors. Using a joint statistical model of the dwarf spheroidal galaxies Sculptor and Fornax, simultaneous constraints on population-averaged yields and galactic outflows are achieved with DLEIY, without fixing the absolute scale of nucleosynthetic yields. The Fe yield from core collapse supernovae is consistent with existing theoretical yield models, while the measured Mg yield is a factor of 2-4 higher, corroborating previous suggestions that yield models may under-predict [Mg/Fe]. We also find that the rate of Type Ia supernovae is enhanced by about a factor of 5 relative to field galaxies, and the delay-time distribution goes as $\sim t^{-2}$, a much steeper relationship than that measured from supernova surveys ($\sim t^{-1.1}$). These findings may suggest a metallicity dependence of the Type Ia rate and delay-time distribution.

astro-ph.GA↗
arXiv2026

It's More Complicated Than You Think: A Forward Model to Infer the Recent Star Formation History, Bursty or Not, of Galaxy Populations

Observations of the early Universe (z > 4) with the James Webb Space Telescope reveal galaxy populations with a wide range of intrinsic luminosities and colors. Bursty star formation histories (SFHs), characterized by short-term fluctuations in the star formation rate (SFR), may explain this diversity, but constraining burst timescales and amplitudes in individual galaxies is challenging due to degeneracies and sensitivity limits. We introduce a population-level simulation-based inference framework that recovers the power and timescales of SFR fluctuations by forward-modeling galaxy populations and distributions of rest-UV to rest-optical spectral features sensitive to star formation timescales. We adopt a stochastic SFH model based on a power spectral density formalism spanning 1 Myr-10 Gyr. Using simulated samples of N=500 galaxies at z~4 with typical JWST/NIRSpec uncertainties, we demonstrate that: (i) the power of SFR fluctuations can be measured with sufficient precision to distinguish between simulations (e.g., FIRE-2-like vs. Illustris-like populations at >99% confidence for timescales < 100 Myr); (ii) simultaneously modeling stochastic fluctuations and the recent (t_L < 500 Myr) average SFH slope is essential, as secular trends otherwise mimic burstiness in common diagnostics; (iii) frequent, intense bursts impose an outshining limit, and bias inference toward underestimating burstiness due to the obscuration of long-timescale power; and (iv) the power of SFR fluctuations can be inferred to 95% confidence across all timescales in both smooth and bursty populations. This framework establishes a novel and robust method for placing quantitative constraints on the feedback physics regulating star formation using large, uniformly selected spectroscopic samples.

astro-ph.GA↗
arXiv2026

Trustworthy scientific inference with generative models

Generative artificial intelligence (AI) excels at producing complex data structures (text, images, videos) by learning patterns from training examples. Across scientific disciplines, researchers are now applying generative models to "inverse problems" to directly predict hidden parameters from observed data along with measures of uncertainty. While these predictive or posterior-based methods can handle intractable likelihoods and large-scale studies, they can also produce biased or overconfident conclusions even without model misspecifications. We present a solution with Frequentist-Bayes (FreB), a mathematically rigorous protocol that reshapes AI-generated posterior probability distributions into (locally valid) confidence regions that consistently include true parameters with the expected probability, while achieving minimum size when training and target data align. We demonstrate FreB's effectiveness by tackling diverse case studies in the physical sciences: identifying unknown sources under dataset shift, reconciling competing theoretical models, and mitigating selection bias and systematics in observational studies. By providing validity guarantees with interpretable diagnostics, FreB enables trustworthy scientific inference across fields where direct likelihood evaluation remains impossible or prohibitively expensive.

stat.ML↗
arXiv2025

SPYGLASS. VII-A. The Demographics and Ages of Small Nearby Young Associations

Recent Gaia-based young stellar association surveys have revealed dozens of low-mass populations that have, until recently, been too small or sparse to detect. These populations represent a largely unstudied demographic with unknown origins, and their relative isolation may minimize gravitational disruptions that impact traceback, making them compelling targets for dynamical studies. In this paper, we survey 15 of these isolated young associations for the first time: Andromeda South (SCYA-97), Aquila East, Aries South (SCYA-104), Cassiopeia East (SCYA-43), Canis Major North, Leo Central (SCYA-2), Leo East (SCYA-3), Theia 72, Ophiuchus Southeast, Scutum North (SCYA-70), Taurus-Orion 1 (TOR1), Theia 78, Vulpecula East (UPK 88), SCYA-54, and SCYA-79. By combining Gaia astrometry and photometry with new ground-based spectroscopic measurements, we assess the membership of each population, search for substructure, analyze their demographics, and compute ages. We find that the smallest populations in our sample contain $<20$ $M_{\odot}$ of stellar mass, making them the smallest associations ever detected. Four host substantial substructure, including TOR1, where we discover TOR1B, a new 16 $M_{\odot}$ association with radial velocities inconsistent with an origin in the parent complex. Using PARSEC isochrones, we produce self-consistent ages for all populations supported by dynamical and lithium depletion ages, which range from 6.9 $\pm$ 0.5 Myr in TOR1A to 42.8 $\pm$ 2.4 in AndS. Our results provide the first detailed overview of the properties of these populations, characterizing a largely unknown category of young associations that may have an important role in tracing the processes that guide local star formation.

astro-ph.SR↗
arXiv2025

Data-Driven Stellar Spectral Modelling with GSPICE

Spectral data reduction pipelines deal with a wide variety of challenges including masking cosmic rays, calibrating wavelength solutions, and estimating background noise while trying to remain model-agnostic. Traditional methods rely on hardware-specific code or pre-calculated stellar model templates to solve this problem, making them model-dependent and not suitable for large datasets that may contain new classes of objects. To solve this problem, we present a flexible, data-driven method: the GausSian PIxelwise Conditional Estimator (GSPICE) that models an ensemble of spectra as a multivariate Gaussian and estimates the expected value and expected variance of each pixel in each spectrum conditional on others. GSPICE compares observed fluxes and errors to its own flux and error estimates to reveal outliers, which then can be completely masked or replaced by their estimates. We apply GSPICE to 3.9 million stellar spectra from the LAMOST survey, and show that variations of the method can directly identify and correct both individual pixel-level outliers (e.g., from cosmic ray hits) as well as extended systematic features (e.g., from incorrect wavelength calibrations), while still providing a novel characterization of the true per-pixel measurement uncertainties. We also demonstrate how GSPICE can take advantage of data partitioning with an application to diffuse interstellar bands. Implementations of GSPICE in both Python and IDL can be found here http://github.com/dfink/gspice.

astro-ph.IM↗
arXiv2025

Early evidence for isotropic planetary obliquities in young super-Jupiter systems

This decade has seen the first measurements of extrasolar planetary obliquities, characterizing how an exoplanet's spin axis is oriented relative to its orbital axis. These measurements are enabled by combining projected rotational velocities, planetary rotation periods, and astrometric orbits for directly-imaged super-Jupiters. This approach constrains both the spin axis and orbital inclination relative to the line of sight, allowing obliquity measurements for individual systems and offering new insights into their formation. To test whether these super-Jupiters form more like scaled-up planets or scaled-down stars, we develop a hierarchical Bayesian framework to infer their population-level obliquity distribution. Using a single-parameter Fisher distribution, we compare two models: a planet-like formation scenario ($κ=5$) predicting moderate alignment, versus a brown dwarf-like formation scenario ($κ=0$) predicting isotropic obliquities. Based on a sample of four young super-Jupiter systems, we find early evidence favoring the isotropic case with a Bayes factor of 15, consistent with turbulent fragmentation.

astro-ph.EP↗
arXiv2025

How does feedback affect the star formation histories of galaxies?

Star formation in galaxies is regulated by the interplay of a range of processes that shape the multiphase gas in the interstellar and circumgalactic media. Using the CAMELS suite of cosmological simulations, we study the effects of varying feedback and cosmology on the average star formation histories (SFHs) of galaxies at $z\sim0$ across the IllustrisTNG, SIMBA and ASTRID galaxy formation models. We find that galaxy SFHs in all three models are sensitive to changes in stellar feedback, which affects the efficiency of baryon cycling and the rates at which central black holes grow, while effects of varying AGN feedback depend on model-dependent implementations of black hole seeding, accretion and feedback. We also find strong interaction terms that couple stellar and AGN feedback, usually by regulating the amount of gas available for the central black hole to accrete. Using a double power-law to describe the average SFHs, we derive a general set of equations relating the shape of the SFHs to physical quantities like baryon fraction and black hole mass across all three models. We find that a single set of equations (albeit with different coefficients) can describe the SFHs across all three CAMELS models, with cosmology dominating the SFH at early times, followed by halo accretion, and feedback and baryon cycling at late times. Galaxy SFHs provide a novel, complementary probe to constrain cosmology and feedback, and can connect the observational constraints from current and upcoming galaxy surveys with the physical mechanisms responsible for regulating galaxy growth and quenching.

astro-ph.GA↗