arXiv subjects

Victor F. Ksoll

Publications and source records attributed to Victor F. Ksoll.

14 recordsLinked to original sources

arXiv2026

IRIS: Deciphering Spectral-Line Imagery of the Galactic Center by Machine-Learning on Simulations

In understanding the 3D structure of the Milky Way's Central Molecular Zone (CMZ), we are limited by our edge-on perspective. Towards addressing this problem, we introduce Imagery Reversion Informed by Simulation (IRIS). IRIS is a novel machine-learning code base featuring a deep convolutional neural network (CNN), which we have designed to translate edge-on observations of our Milky Way Galaxy into top-down images by training on data generated from AREPO galaxy simulations and synthetic observations of those simulations. We develop a large custom dataset on which we train our bespoke model, and then test the trained model on synthetic data to probe the potential of this machine-learning method, which we call supervised reversion. We then apply our trained model to real observations from the SEDIGISM 13CO(2-1) survey, yielding new top-down views of our CMZ. Though our SEDIGISM reversions are not fully consistent across model training runs, we posit that this lack of convergence can be alleviated by expansion of the training dataset. We argue that these results represent a strong proof-of-concept for the use of supervised reversion to decipher our CMZ's 3D structure. Crucial in generating our training dataset's 100k synthetic observations, we introduce IRIS Synthetic Observation (IRIS-SO), a new GPU-accelerated and fully differentiable code implemented in PyTorch for the non-LTE synthetic observation of spectral lines and dust. We find that IRIS-SO provides up to 10,000x speedups in comparison to the synthetic-observation code RADMC-3D. We release all the IRIS code open-source at https://github.com/bldubois/IRIS.

astro-ph.GA↗

arXiv2026

Observational constraints on the origin of the elements. X. Combining NLTE and machine learning for chemical diagnostics of 4 million stars in the 4MIDABLE-HR survey

We present the 4MOST-HR resolution Non-Local Thermal Equilibrium (NLTE) Payne artificial neural network (ANN), trained on $404\,793$ new FGK spectra with 16 elements computed in NLTE. This network will be part of the Stellar Abundances and atmospheric Parameters Pipeline (SAPP), which will analyse 4 million stars during the five year long 4MOST consortium 4: 4MOST MIlky way Disc And BuLgE High-Resolution (4MIDABLE-HR) survey. A fitting algorithm using this ANN is also presented that is able to fully-automatically and self-consistently derive both stellar parameters and elemental abundances. The ANN is validated by fitting 121 observed spectra of low-mass FGKM type stars, including main-sequence dwarf, subgiant and giant stars down to [Fe/H] $\approx -3.3$ degraded to 4MOST-HR resolution of $R\approx20\,000$, and comparing the derived abundances with the output of the classical radiative transfer code TSFitPy. We are able to recover all 18 elemental abundances with a bias~$<0.13$ and spread~$<0.16$\,dex, although the typical values are $<0.09$ dex for most elements. These abundances are compared to the OMEGA+ Galactic Chemical Evolution model, showcasing for the first time, the expected performance and results obtained from high-resolution spectra of the quality expected to be obtained with 4MOST. The expected Galactic trends are recovered, and we highlight the potential of using many chemical elements to constrain the formation history of the Galaxy.

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arXiv2026

A method to derive self-consistent NLTE astrophysical parameters for 4 million high-resolution 4MOST stellar spectra in half a day with invertible neural networks

Modern spectroscopic surveys obtain spectra for millions of stars. However, classical spectroscopic methods can often be computationally expensive, rendering them impractical for the analysis of large datasets. We introduce a novel simulation-based deep-learning approach for the efficient analysis of high-resolution stellar spectra to be obtained with the upcoming high-resolution 4MOST spectrograph. We used a suite of synthetic non-local thermodynamic equilibrium (NLTE) spectra generated with Turbospectrum to mimic 4MOST observations and trained a conditional invertible neural network (cINN) for the purpose of predicting self-consistently stellar surface parameters and chemical abundances. The cINN is a neural network architecture that estimates full posterior distributions for the target stellar properties, providing an intrinsic uncertainty estimate. We evaluated the predictive performance of the trained cINN model on both synthetic data and observed spectra of stars. We found that our new cINN trained on NLTE synthetic spectra is capable of recovering stellar parameters with average errors ($σ$) of $33$ K for $T_\mathrm{eff}$, $0.16$ dex for $\log(g)$, and $0.12$ dex for [Fe/H], $0.1$ dex for [Ca/Fe], $0.11$ for [Mg/Fe], and $0.51$ dex for [Li/Fe], respectively, at a signal to noise ratio of 250 per Angstrom. From the analysis of the observed spectra of Gaia-ESO / 4MOST / PLATO benchmark stars, we verified that our NLTE estimates for stellar parameters and abundances are consistent with results obtained with the independent code TSFitPy. We conclude that the NLTE cINN is robust and can, theoretically, evaluate 4 million high-resolution 4MOST spectra in less than a day, using GPU acceleration.

astro-ph.SR↗

arXiv2026

A normalizing flow approach for the inference of star cluster properties from unresolved broadband photometry I: Comparison to spectral energy distribution fitting

Estimating properties of star clusters from unresolved broadband photometry is a challenging problem that is classically tackled by spectral energy distribution (SED) fitting methods that are based on simple stellar population models. However, because of their exponential scaling, grid-based methods suffer from computational limitations. In addition, nuisance parameters in the model can make the computation of the likelihood function intractable. These limitations can be overcome by modern generative deep learning methods that offer flexible and powerful tools for modeling high-dimensional posterior distributions and fast inference from learned data. We present a normalizing flow approach for the inference of cluster age, mass, and reddening from Hubble Space Telescope broadband photometry. In particular, we explore our network's behavior on an inference problem that has been analyzed in previous works. We used the SED modeling code CIGALE to create a dataset of synthetic photometric observations for $5 \times 10^6$ mock star clusters. Subsequently, this data set was used to train a coupling-based flow in the form of a conditional invertible neural network (cINN) to predict posterior probability distributions for cluster age, mass, and reddening from photometric observations. We predicted cluster parameters for the 'Physics at High Angular resolution in Nearby GalaxieS' (PHANGS) Data Release 3 catalog. To evaluate the capabilities of the network, we compared our results to the publicly available PHANGS estimates and found that the estimates agree reasonably well. We demonstrate that normalizing flow methods can be a viable tool for the inference of cluster parameters, and argue that this approach is especially useful when nuisance parameters make the computation of the likelihood intractable and in scenarios that require efficient density estimation.

astro-ph.GA↗

arXiv2025

Exoplanet formation inference using conditional invertible neural networks

The interpretation of the origin of observed exoplanets is usually done only qualitatively due to uncertainties of key parameters in planet formation models. To allow a quantitative methodology which traces back in time to the planet birth locations, we train recently developed conditional invertible neural networks (cINN) on synthetic data from a global planet formation model which tracks growth from dust grains to evolved final giant planets. In addition to deterministic single planet formation runs, we also include gravitationally interacting planets in multiplanetary systems, which include some measure of chaos. For the latter case, we treat them as individual planets or choose the two or three planets most likely to be discovered by telescopes. We find that training on multiplanetary data, each planet treated as individual point, is promising. The single-planet data only covers a small range of planets and does not extrapolate well to planet properties not included in the training data. Extension to planetary systems will require more training data due to the higher dimensionality of the problem.

astro-ph.EP↗

arXiv2025

Spectral classification of young stars using conditional invertible neural networks II. Application to Trumpler 14 in Carina

We introduce an updated version of our deep learning tool that predicts stellar parameters from the optical spectra of young low-mass stars with intermediate spectral resolution. We adopt a conditional invertible neural network (cINN) architecture to infer the posterior distribution of stellar parameters and train our cINN on two Phoenix stellar atmosphere model libraries (Settl and Dusty). Compared to the cINNs presented in our first study, the updated cINN considers the influence of the relative flux error on the parameter estimation and predicts an additional fourth parameter, veiling. We test the performance of cINN on synthetic test models to quantify the intrinsic error of the cINN as a function of relative flux error and on 36 class III template stars to validate the performance on real spectra. Using our cINN, we estimate the stellar parameters of young stars in Trumpler 14 (Tr14) in the Carina Nebula Complex, observed with VLT-MUSE, and compare them with those derived using the classical template fitting method. We provide Teff, log g, Av, and veiling values measured by our cINN as well as stellar ages and masses derived from the HR diagram. Our parameter estimates generally agree well with those measured by template fitting. However, for K- and G-type stars, the Teff derived from template fitting is, on average, 2-3 subclasses hotter than the cINN estimates, while the corresponding veiling values from template fitting appear to be underestimated compared to the cINN predictions. We obtain an average age of 0.7(+3.2)(-0.6) Myr for the Tr14 stars. By examining the impact of veiling on the equivalent width-based classification, we demonstrate that the main cause of temperature overestimation for K- and G-type stars in the previous study is that veiling and effective temperature are not considered simultaneously in their process.

astro-ph.SR↗

arXiv2024

A deep-learning approach to the 3D reconstruction of dust density and temperature in star-forming regions

Aims: We introduce a new deep-learning approach for the reconstruction of 3D dust density and temperature distributions from multi-wavelength dust emission observations on the scale of individual star-forming cloud cores (<0.2pc). Methods: We construct a training data set by processing cloud cores from the Cloud Factory simulations with the POLARIS radiative transfer code to produce synthetic dust emission observations at 23 wavelengths between 12 and 1300 $μ$m. We simplify the task by reconstructing the cloud structure along individual lines of sight and train a conditional invertible neural network (cINN) for this purpose. The cINN belongs to the group of normalising flow methods and is able to predict full posterior distributions for the target dust properties. We test different cINN setups, ranging from a scenario that includes all 23 wavelengths down to a more realistically limited case with observations at only seven wavelengths. We evaluate the predictive performance of these models on synthetic test data. Results: We report an excellent reconstruction performance for the 23-wavelengths cINN model, achieving median absolute relative errors of about 1.8% in $\log(n/m^{-3})$ and 1% in $\log(T_{dust}/K)$, respectively. We identify trends towards overestimation at the low end of the density range and towards underestimation at the high end of both the density and temperature values, which may be related to a bias in the training data. Limiting our coverage to a combination of only seven wavelengths, we still find a satisfactory performance with average absolute relative errors of about 3.3% and 2.5% in $\log(n/m^{-3})$ and $\log(T_{dust}/K)$. Conclusions: This proof-of-concept study shows that the cINN-based approach for 3D reconstruction of dust density and temperature is very promising and even compatible with a more realistically constrained wavelength coverage.

astro-ph.GA↗

arXiv2023

Spectral classification of young stars using conditional invertible neural networks I. Introducing and validating the method

Aims. We introduce a new deep learning tool that estimates stellar parameters (such as effective temperature, surface gravity, and extinction) of young low-mass stars by coupling the Phoenix stellar atmosphere model with a conditional invertible neural network (cINN). Our networks allow us to infer the posterior distribution of each stellar parameter from the optical spectrum. Methods. We discuss cINNs trained on three different Phoenix grids: Settl, NextGen, and Dusty. We evaluate the performance of these cINNs on unlearned Phoenix synthetic spectra and on the spectra of 36 Class III template stars with well-characterised stellar parameters. Results. We confirm that the cINNs estimate the considered stellar parameters almost perfectly when tested on unlearned Phoenix synthetic spectra. Applying our networks to Class III stars, we find good agreement with deviations of at most 5--10 per cent. The cINNs perform slightly better for earlier-type stars than for later-type stars like late M-type stars, but we conclude that estimations of effective temperature and surface gravity are reliable for all spectral types within the network's training range. Conclusions. Our networks are time-efficient tools applicable to large amounts of observations. Among the three networks, we recommend using the cINN trained on the Settl library (Settl-Net), as it provides the best performance across the largest range of temperature and gravity.

astro-ph.SR↗

arXiv2023

Noise-Net: Determining physical properties of HII regions reflecting observational uncertainties

Stellar feedback, the energetic interaction between young stars and their birthplace, plays an important role in the star formation history of the universe and the evolution of the interstellar medium (ISM). Correctly interpreting the observations of star-forming regions is essential to understand stellar feedback, but it is a non-trivial task due to the complexity of the feedback processes and degeneracy in observations. In our recent paper, we introduced a conditional invertible neural network (cINN) that predicts seven physical properties of star-forming regions from the luminosity of 12 optical emission lines as a novel method to analyze degenerate observations. We demonstrated that our network, trained on synthetic star-forming region models produced by the WARPFIELD-Emission predictor (WARPFIELD-EMP), could predict physical properties accurately and precisely. In this paper, we present a new updated version of the cINN that takes into account the observational uncertainties during network training. Our new network named Noise-Net reflects the influence of the uncertainty on the parameter prediction by using both emission-line luminosity and corresponding uncertainties as the necessary input information of the network. We examine the performance of the Noise-Net as a function of the uncertainty and compare it with the previous version of the cINN, which does not learn uncertainties during the training. We confirm that the Noise-Net outperforms the previous network for the typical observational uncertainty range and maintains high accuracy even when subject to large uncertainties.

astro-ph.GA↗

arXiv2022

Emission-line diagnostics of HII regions using conditional Invertible Neural Networks

Young massive stars play an important role in the evolution of the interstellar medium (ISM) and the self-regulation of star formation in giant molecular clouds (GMCs) by injecting energy, momentum, and radiation (stellar feedback) into surrounding environments, disrupting the parental clouds, and regulating further star formation. Information of the stellar feedback inheres in the emission we observe, however inferring the physical properties from photometric and spectroscopic measurements is difficult, because stellar feedback is a highly complex and non-linear process, so that the observational data are highly degenerate. On this account, we introduce a novel method that couples a conditional invertible neural network (cINN) with the WARPFIELD-emission predictor (WARPFIELD-EMP) to estimate the physical properties of star-forming regions from spectral observations. We present a cINN that predicts the posterior distribution of seven physical parameters (cloud mass, star formation efficiency, cloud density, cloud age which means age of the first generation stars, age of the youngest cluster, the number of clusters, and the evolutionary phase of the cloud) from the luminosity of 12 optical emission lines, and test our network with synthetic models that are not used during training. Our network is a powerful and time-efficient tool that can accurately predict each parameter, although degeneracy sometimes remains in the posterior estimates of the number of clusters. We validate the posteriors estimated by the network and confirm that they are consistent with the input observations. We also evaluate the influence of observational uncertainties on the network performance.

astro-ph.GA↗

arXiv2021

Measuring Young Stars in Space and Time -- I. The Photometric Catalog and Extinction Properties of N44

In order to better understand the role of high-mass stellar feedback in regulating star formation in giant molecular clouds, we carried out a Hubble Space Telescope (HST) Treasury Program "Measuring Young Stars in Space and Time" (MYSST) targeting the star-forming complex N44 in the Large Magellanic Cloud (LMC). Using the F555W and F814W broadband filters of both the ACS and WFC3/UVIS, we built a photometric catalog of 461,684 stars down to $m_\mathrm{F555W} \simeq 29$ mag and $m_\mathrm{F814W} \simeq 28$ mag, corresponding to the magnitude of an unreddened 1 Myr pre-main-sequence star of $\approx0.09$ $M_\odot$ at the LMC distance. In this first paper we describe the observing strategy of MYSST, the data reduction procedure, and present the photometric catalog. We identify multiple young stellar populations tracing the gaseous rim of N44's super bubble, together with various contaminants belonging to the LMC field population. We also determine the reddening properties from the slope of the elongated red clump feature by applying the machine learning algorithm RANSAC, and we select a set of Upper Main Sequence (UMS) stars as primary probes to build an extinction map, deriving a relatively modest median extinction $A_{\mathrm{F555W}}\simeq0.77$ mag. The same procedure applied to the red clump provides $A_{\mathrm{F555W}}\simeq 0.68$ mag.

astro-ph.GA↗

arXiv2021

Measuring Young Stars in Space and Time -- II. The Pre-Main-Sequence Stellar Content of N44

The Hubble Space Telescope (HST) survey Measuring Young Stars in Space and Time (MYSST) entails some of the deepest photometric observations of extragalactic star formation, capturing even the lowest mass stars of the active star-forming complex N44 in the Large Magellanic Cloud. We employ the new MYSST stellar catalog to identify and characterize the content of young pre-main-sequence (PMS) stars across N44 and analyze the PMS clustering structure. To distinguish PMS stars from more evolved line of sight contaminants, a non-trivial task due to several effects that alter photometry, we utilize a machine learning classification approach. This consists of training a support vector machine (SVM) and a random forest (RF) on a carefully selected subset of the MYSST data and categorize all observed stars as PMS or non-PMS. Combining SVM and RF predictions to retrieve the most robust set of PMS sources, we find $\sim26,700$ candidates with a PMS probability above 95% across N44. Employing a clustering approach based on a nearest neighbor surface density estimate, we identify 18 prominent PMS structures at $1$ $σ$ significance above the mean density with sub-clusters persisting up to and beyond $3$ $σ$ significance. The most active star-forming center, located at the western edge of N44's bubble, is a subcluster with an effective radius of $\sim 5.6$ pc entailing more than 1,100 PMS candidates. Furthermore, we confirm that almost all identified clusters coincide with known H II regions and are close to or harbor massive young O stars or YSOs previously discovered by MUSE and Spitzer observations.

astro-ph.GA↗

arXiv2020

Stellar Parameter Determination from Photometry using Invertible Neural Networks

Photometric surveys with the Hubble Space Telescope (HST) allow us to study stellar populations with high resolution and deep coverage, with estimates of the physical parameters of the constituent stars being typically obtained by comparing the survey data with adequate stellar evolutionary models. This is a highly non-trivial task due to effects such as differential extinction, photometric errors, low filter coverage, or uncertainties in the stellar evolution calculations. These introduce degeneracies that are difficult to detect and break. To improve this situation, we introduce a novel deep learning approach, called conditional invertible neural network (cINN), to solve the inverse problem of predicting physical parameters from photometry on an individual star basis and to obtain the full posterior distributions. We build a carefully curated synthetic training data set derived from the PARSEC stellar evolution models to predict stellar age, initial/current mass, luminosity, effective temperature and surface gravity. We perform tests on synthetic data from the MIST and Dartmouth models, and benchmark our approach on HST data of two well-studied stellar clusters, Westerlund 2 and NGC 6397. For the synthetic data we find overall excellent performance, and note that age is the most difficult parameter to constrain. For the benchmark clusters we retrieve reasonable results and confirm previous findings for Westerlund 2 on cluster age ($1.04_{-0.90}^{+8.48}\,\mathrm{Myr} $), mass segregation, and the stellar initial mass function. For NGC 6397 we recover plausible estimates for masses, luminosities and temperatures, however, discrepancies between stellar evolution models and observations prevent an acceptable recovery of age for old stars.

astro-ph.SR↗

arXiv2018

Hubble Tarantula Treasury Project - VI. Identification of Pre-Main-Sequence Stars using Machine Learning techniques

The Hubble Tarantula Treasury Project (HTTP) has provided an unprecedented photometric coverage of the entire star-burst region of 30 Doradus down to the half Solar mass limit. We use the deep stellar catalogue of HTTP to identify all the pre--main-sequence (PMS) stars of the region, i.e., stars that have not started their lives on the main-sequence yet. The photometric distinction of these stars from the more evolved populations is not a trivial task due to several factors that alter their colour-magnitude diagram positions. The identification of PMS stars requires, thus, sophisticated statistical methods. We employ Machine Learning Classification techniques on the HTTP survey of more than 800,000 sources to identify the PMS stellar content of the observed field. Our methodology consists of 1) carefully selecting the most probable low-mass PMS stellar population of the star-forming cluster NGC 2070, 2) using this sample to train classification algorithms to build a predictive model for PMS stars, and 3) applying this model in order to identify the most probable PMS content across the entire Tarantula Nebula. We employ Decision Tree, Random Forest and Support Vector Machine classifiers to categorise the stars as PMS and Non-PMS. The Random Forest and Support Vector Machine provided the most accurate models, predicting about 20,000 sources with a candidateship probability higher than 50 percent, and almost 10,000 PMS candidates with a probability higher than 95 percent. This is the richest and most accurate photometric catalogue of extragalactic PMS candidates across the extent of a whole star-forming complex.

astro-ph.SR↗