The environments forming stellar and planetary systems

Michael Kuffmeier; Jaime E. Pineda; Peter Schilke; Jes K. Jørgensen; Korash Assani; Alain Baudry; Maite Beltrán; Eleonora Bianchi; Claudio Codella; Laura Colzi; Audrey Coutens; Timea Csengeri; Maria Drozdovskaya; Vardan Elbakyan; Francesco Fontani; Gary Fuller; Josep Miquel Girart; Katharine Johnston; Pamela Klaassen; Rolf Kuiper; Sébastien Maret; Nagayoshi Ohashi; Linda Podio; Víctor M. Rivilla; Giovanni Sabatini; Patricio Sanhueza; Dominique Segura-Cox; Kei Tanaka; Maxime Tanious; Fengwei Xu; Yao-Lun Yang; Qizhou Zhang

arXiv · 2610.22462

The environments forming stellar and planetary systems

View original source ↗Read source document ↗Export metadata ↓

Abstract

How does a star's birth environment set the mass, angular momentum and chemical complexity delivered to its planet-forming disk, and thereby whether it builds planets, brown dwarfs or stellar companions? This requires following material from the cloud to the sub-au to few-au scales where infalling gas, magnetized winds and the disk interact, across all star-forming environments. At frequencies where young disks are optically thin, current facilities, even ALMA after its Wideband Sensitivity Upgrade, fall short by a factor of a few in angular resolution and an order of magnitude in sensitivity. Likewise, resolving how disks around distant high-mass stars fragment into tight multiples requires a linear resolution of a few to a few tens of au, currently out of reach. Yet the disks characterized so far lie almost exclusively in nearby, isolated clouds at near-solar metallicity, although the environment (radiation field, ionization rate, gas and stellar density, pressure and metallicity) varies by orders of magnitude across the Galaxy. Most stars, probably including the Sun, form in clustered regions at kpc distances, and the widest metallicity range lies farther still, in the outer Galaxy, the Central Molecular Zone and the Magellanic Clouds. With three times finer angular resolution and ten times the sensitivity, ALMA2040 will follow the path of material from cloud to disk across the full stellar-mass spectrum and, for the first time, measure how this path changes with environment: resolving the sub-au infall-impact and wind-launching zones nearby, disk fragmentation and the onset of multiplicity in distant high-mass regions, and disk formation at a few tenths of solar metallicity. It will thereby reveal the dust-obscured, embedded origins of planetary systems that optical and infrared facilities, including the ELT, see only once they emerge, and show how the birth environment shapes them.

Explore related subjects

astro-ph.IM astro-ph.EP astro-ph.GA astro-ph.SR

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Michael Kuffmeier, Jaime E. Pineda, Peter Schilke, Jes K. Jørgensen, Korash Assani, Alain Baudry, Maite Beltrán, Eleonora Bianchi, Claudio Codella, Laura Colzi, Audrey Coutens, Timea Csengeri, Maria Drozdovskaya, Vardan Elbakyan, Francesco Fontani, Gary Fuller, Josep Miquel Girart, Katharine Johnston, Pamela Klaassen, Rolf Kuiper, Sébastien Maret, Nagayoshi Ohashi, Linda Podio, Víctor M. Rivilla, Giovanni Sabatini, Patricio Sanhueza, Dominique Segura-Cox, Kei Tanaka, Maxime Tanious, Fengwei Xu, Yao-Lun Yang, Qizhou Zhang. 2026-10-15. The environments forming stellar and planetary systems. https://arxiv.org/abs/2610.22462

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Validation of Satellite Lifetime Predictions at Leonid Space

This paper validates Leonid Space's satellite lifetime prediction pipeline through a backtesting campaign of 1283 deorbited satellites spanning six decades of spaceflight, showing that it accurately recreates their last year of flight. The pipeline combines a custom high-speed orbit propagator, ballistic coefficient estimation from on-orbit TLE data, atmospheric density corrections from data assimilation, custom space weather forecasts, and probabilistic Monte Carlo analysis. Validation proceeds in four steps that progressively remove the benefit of hindsight: starting from a "perfect knowledge" scenario using actual space weather data and ephemerides, we gradually replace historical data with contemporary predictions to arrive at a fully predictive scenario analogous to a deorbit prediction performed for a spacecraft flying today. These forecasts are scored using the continuously ranked probability score and root mean square calibration error, yielding a median one-year prediction accuracy of 2.4 days (0.7%) with perfect knowledge and 35.3 days (9.7%) in the fully predictive scenario. Solar flux forecasts contribute 75% of the variance in our predictions, dominating all other sources of error. To our knowledge, this is the first comprehensive backtesting of long-term satellite lifetime predictions that reports quantitative results for the fully predictive case, and it establishes a new framework for scoring solar cycle forecasts by grounding forecast accuracy in operationally-relevant satellite lifetime predictions.

astro-ph.IM↗

arXiv2026

A Solar Optical Clock

Precision spectroscopy underpins quantum sensing, atomic timekeeping, and the search for terrestrial-mass exoplanets. Finding an Earth-Sun analog requires fractional splitting of spectral features that rivals the precision of the best laboratory spectroscopy. This corresponds to resolution of a few parts in $10^5$ of a typical stellar spectral line, or a Doppler velocity precision of less than 10 cm s$^{-1}$. Only a few astrophysical spectrometers reach this regime, and only by dispersing starlight with large gratings and averaging thousands of lines. Here, we show that comparable precision can be achieved on a single solar line by leveraging techniques used in quantum sensors and optical clocks. A laser is frequency-locked to the 1565 nm solar Fe I line by coupling to a single mode of the solar thermal field and counted against an optical frequency comb referenced to the SI second, thus creating a solar optical clock. Operating at the fundamental photon-noise limit of thermal heterodyne spectroscopy, we achieve centroid precision of one part in $10^9$ of the optical frequency, or 30 cm s$^{-1}$ in radial velocity. We recover the Sun's five-minute acoustic oscillations and the expected Rossiter-McLaughlin signal during a solar eclipse. Combining these techniques from quantum sensing with a thirty-meter-class telescope could enable our demonstrated precision on the nearest Sun-like stars in a few hours. Multiplexing such absolute frequency measurements across lines with different magnetic sensitivity and formation depth would reduce averaging time and help disentangle stellar granulation and activity from the Doppler signature of terrestrial exoplanets, providing new approaches to address some of the most demanding measurements in astrophysical spectroscopy.

astro-ph.IM↗

arXiv2026

Data-driven characterisation of variable source light curves with multimodal representation learning

Surveys such as the Zwicky Transient Facility (ZTF) and the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) produce light curves for hundreds of millions of variables. Their characterisation traditionally relies on supervised classifiers with hand-crafted features and curated labels, limiting them to predefined taxonomies and individual surveys. We present a general, survey-agnostic framework that characterises variable sources without labelled training data, producing low-dimensional representations of light curve properties such as amplitude, periodicity, and asymmetry. A multimodal architecture combines two complementary views, a Transformer encoder on raw observations and a convolutional neural network on a generalised Lomb-Scargle periodogram. Each light curve is randomly split into two temporal halves as independent views, and the branches are trained jointly with VICReg to produce embeddings invariant across halves while keeping each dimension informative. We evaluate on 37 006 ZTF sources spanning 14 variable star classes in four broad physical categories. A linear classifier on the frozen 64-dimensional embeddings achieves 77.1% balanced accuracy for classes and 90.5% for broad categories. Combining both branches improves balanced accuracy by up to 14.9 percentage points over the best single modality, confirming their complementarity. Interpretable light curve properties are encoded on distinct dimensions, and within-class analysis reveals physical substructure unresolved by reference labels. Retraining on ZTF g and i bands without modification reproduces the main results, and concatenating multi-band embeddings improves balanced accuracy to 86.1%. The framework needs no labels, phase folding, or survey-specific preprocessing and applies directly to other surveys with different cadences and wavelength coverage.

astro-ph.IM↗