arXiv subjects

M. Steinmetz

Publications and source records attributed to M. Steinmetz.

At least 19 recordsLinked to original sources

arXiv2026

The impact of radial migration on disk galaxy star formation histories: II. Role of bar strength, disk thickness, and merger history

Reconstructing the star formation history (SFH) of disk galaxies is central to understanding their growth and evolution, yet such estimates can be strongly biased by stellar radial migration over cosmic time. Using 186 Milky Way (MW) and Andromeda (M31) analogs from the TNG50 cosmological simulation, we compare star formation rates (SFRs) inferred from present-day stellar positions with those based on stellar birth radii to quantify the magnitude, spatial structure, and temporal evolution of migration-induced biases. We find that radial migration systematically produces artificial star formation in regions that had not yet formed stars. Notably, ~80% of galaxies exhibit outer-disk stars older than 10 Gyr, which we find to have formed at radii interior to the outer disk and to have reached their present locations via substantial outward migration. Similar effects appear in ~45% of galaxies at intermediate radii during early epochs, and in 30% of quenched inner disks within the past 4 Gyr. Migration also smooths SFHs, washing out localized bursts and suppressions by dispersing stars across neighboring radii. The strength and imprint of these distortions depend sensitively on galactic structure and evolutionary history: strong bars drive mean SFR overestimates of up to 75% in the inner disk and 150% in the outskirts; thinner, dynamically cold disks suffer average outer-disk biases up to 160%; while thick disks exhibit typical inner-disk biases up to 125%. Merger timing further modulates these patterns. Our results demonstrate that failing to account for stellar migration can lead to severe misinterpretations of when and where stars formed, with direct implications for the chemical and evolutionary histories of the MW and external galaxies.

astro-ph.GA↗

arXiv2026

Prior-free cosmological parameter estimation of Cosmicflows-4

As tracers of the underlying mass distributions, the peculiar velocities of galaxies are valuable probes of the Universe, allowing us to measure the Hubble constant or to map the large-scale structure and its dynamics. The catalogs of peculiar velocities, however, are noisy, scarce, and prone to various interpretation biases. We measured the radial and bulk flow directly from the largest available sample of peculiar velocities and did not impose a cosmological prior on the velocity field. Furthermore, a minimum assumption on the shape of the radial flow at large distances enabled us to estimate the local Hubble constant. To address these issues, we analyzed the Cosmicflows-4 catalog (CF4), the most extensive catalog of galaxy peculiar velocities, reaching a redshift $z=0.1$. Specifically, we constructed a forward-modeling approach assuming only a flat Universe, which reconstructs the radial and bulk flows of the velocity field directly from measurements of peculiar velocities. Our method was tested on a series of 64 simulated catalogs that mimicked the complex selection function of CF4 in space and in magnitude. Based on our mock data, we propose a simulation-based correction method that we applied to the CF4 data. Our method recovers the radial flow and the direction and magnitude of the bulk flow throughout the covered volume without bias. By estimating the (cosmic) variance of the density field at large distances from the $Λ$CDM model, we were able to extract a value of $75.9\pm1{\rm (stat) km/s/Mpc}$ from the radial inflow. With regard to the bulk flow, a 3$σ$ tension is found with $Λ$CDM around $140\ {\rm Mpc/h}$ and $240\ {\rm Mpc/h}$. In summary, our work confirms the existing tension on the Hubble constant measured locally and a significant tension in the local bulk flow with $Λ$CDM predictions.

astro-ph.CO↗

arXiv2025

The impact of radial migration on disk galaxy star formation histories I. Biases in spatially resolved estimates

Knowledge of the spatially resolved star formation history (SFH) of disk galaxies provides crucial insight into disk assembly, quenching, and chemical evolution. However, most reconstructions, both for the Milky Way and for external galaxies, implicitly assume that stars formed at their present-day radii. Using a range of zoom-in cosmological simulations, we show that stellar radial migration introduces strong and systematic biases in such SFH estimates. In the inner disk (R < h_d), early star formation is typically underestimated by 25-50% and late star formation overestimated, giving the misleading impression of prolonged, moderate activity. An exception occurs in the very central bin considered (~ 0.4h_d), which is consistently overestimated due to a net inflow of inward migrators. At intermediate radii and in the outer disk, migration drives the opposite trend: intermediate-age populations are overestimated by 100-200% as stars born in the inner disk migrate outward, whereas genuinely in-situ populations are underestimated by ~ 50% as they themselves continue to migrate. The net effect is that SFH peaks are suppressed and broadened, and the true rate of inside-out disk growth is systematically underestimated. These distortions affect all galaxies in our sample and have direct implications for interpreting spatially resolved SFHs from IFU surveys such as CALIFA and MaNGA, where present-day radii are often used as proxies for stellar birth sites. Correcting these biases will require accounting for disk mass, bar presence, disk kinematics and morphology, while recent birth-radius estimation techniques for Milky Way stars offer a promising path forward.

astro-ph.GA↗

arXiv2025

Prospects for studying million-degree gas in the Milky Way halo using the forbidden optical [FeX] and [FeXIV] intersystem lines

The Milky Way is surrounded by large amounts of hot gas at temperatures T>10^6 K, which represents a major baryon reservoir. We here explore the prospects of studying the hot coronal gas in Milky Way halo by analyzing the highly forbidden optical coronal lines of [FeX] and [FeXIV] in absorption against bright extragalactic background sources. We use a semi-analytic model of the Milky Way's coronal gas distribution together wih HESTIA simulations of the Local Group and observational constraints to predict the expected FeX and FeXIV column densities as well as the line shapes and strengths. We predict column densities of log N(FeX)=15.40 and log N(FeXIV)=15.23 in the Milky Way's hot halo and estimate that a minimum S/N of 50,000 (25,000) is required to detect [FeX] l6374.5 ([FeXIV] l5302.9) absorption at a 3sigma level. Using archical optical data from an original sample of 739 high resolution AGN spectra from VLT/UVES and KECK/HIRES, we generate a stacked composite spectrum to measure an upper limit for the column densities of FeX and FeXIV in the Milky Way's coronal gas. No [FeX] and [FeXIV] is detected in our composite spectrum, which achieves a maximum S/N= 1,240 near 5300 A. We derive 3sigma upper column-density limits of log N(FeX)<16.27 and log N(FeXIV)<15.85, in line with the above-mentioned predictions. While [FeX] and [FexIX] absorption is too weak to be detected with current optical data, we outline how up-coming extragalactic spectral surveys with millions of medium- to high-resolution optical spectra will provide the necessary sensitivity and spectral resolution to measure velocity-resolved [FeX] and [FeXIV] absorption in the Milky Way's coronal gas (and beyond). This gives the prospect of opening a new window for studying the dominant baryonic mass component of the Milky Way in the form of hot coronal gas via optical spectroscopy.

astro-ph.GA↗

arXiv2024

Empirical derivation of the metallicity evolution with time and radius using TNG50 Milky Way/Andromeda analogues

Recent works have used a linear birth metallicity gradient to estimate the evolution of the [Fe/H] profile in the Galactic disk over time, and infer stellar birth radii (R$_\text{birth}$) from [Fe/H] and age measurements. These estimates rely on the evolution of [Fe/H] at the Galactic center ([Fe/H](0, $τ$)) and the birth metallicity gradient ($\nabla$[Fe/H]($τ)$) over time -- quantities that are unknown and inferred under key assumptions. In this work, we use the sample of Milky Way/Andromeda analogues from the TNG50 simulation to investigate the ability to recover [Fe/H](R, $τ$) and R$_\text{birth}$ in a variety of galaxies. Using stellar disk particles, we test the assumptions required in estimating R$_\text{birth}$, [Fe/H](0, $τ$), and $\nabla$[Fe/H]($τ)$ using recently proposed methods to understand when they are valid. We show that $\nabla$[Fe/H]($τ)$ can be recovered in most galaxies to within 26% from the range in [Fe/H] across age, with better accuracy for more massive and stronger barred galaxies. We also find that the true central metallicity is unrepresentative of the genuine disk [Fe/H] profile; thus we propose to use a projected central metallicity instead. About half of the galaxies in our sample do not have a continuously enriching projected central metallicity, with a dilution in [Fe/H] correlating with mergers. Most importantly, galaxy-specific [Fe/H](R, $τ$) can be constrained and confirmed by requiring the R$_\text{birth}$ distributions of mono-age, solar neighborhood populations to follow inside-out formation. We conclude that examining trends with R$_\text{birth}$ is valid for the Milky Way disk and similarly structured galaxies, where we expect R$_\text{birth}$ can be recovered to within 20% assuming today's measurement uncertainties in TNG50.

astro-ph.GA↗

arXiv2024

Transferring spectroscopic stellar labels to 217 million Gaia DR3 XP stars with SHBoost

In this paper, we explore the feasibility of using machine learning regression as a method of extracting basic stellar parameters and line-of-sight extinctions from spectro-photometric data. We built a stable gradient-boosted random-forest regressor (xgboost), trained on spectroscopic data, capable of producing output parameters with reliable uncertainties from Gaia DR3 data (most notably the low-resolution XP spectra), without ground-based spectroscopic observations. Using Shapley additive explanations, we interpret how the predictions for each star are influenced by each data feature. For the training and testing of the network, we used high-quality parameters obtained from the StarHorse code for a sample of around eight million stars observed by major spectroscopic stellar surveys, complemented by curated samples of hot stars, very metal-poor stars, white dwarfs, and hot sub-dwarfs. The training data cover the whole sky, all Galactic components, and almost the full magnitude range of the Gaia DR3 XP sample of more than 217 million objects that also have reported parallaxes. We have achieved median uncertainties of 0.20 mag in V-band extinction, 0.01 dex in logarithmic effective temperature, 0.20 dex in surface gravity, 0.18 dex in metallicity, and $12\%$ in mass (over the full Gaia DR3 XP sample, with considerable variations in precision as a function of magnitude and stellar type). We succeeded in predicting competitive results based on Gaia DR3 XP spectra compared to classical isochrone or spectral-energy distribution fitting methods we employed in earlier works, especially for parameters $A_V$ and $T_{\rm eff}$, along with the metallicity values. Finally, we showcase some potential applications of this new catalogue, including extinction maps, metallicity trends in the Milky Way, and extended maps of young massive stars, metal-poor stars, and metal-rich stars). [abridged]

astro-ph.SR↗

arXiv2024

Gaia DR3 data consistent with a short bar connected to a spiral arm

We use numerical simulations to model Gaia DR3 data with the aim of constraining the Milky Way bar and spiral structure parameters. We show that both the morphology and the velocity field in Milky Way-like galactic disc models are strong functions of time, changing dramatically over a few tens of Myr. This suggests that by finding a good match to the observed radial velocity field, v_R(x,y), we can constrain the bar-spiral orientation. Incorporating uncertainties into our models is necessary to match the data; most importantly, a heliocentric distance uncertainty above 10-15% distorts the bar's shape and v_R quadrupole pattern morphology, and decreases its apparent angle with respect to the Sun-Galactocentric line. An excellent match to the Gaia DR3 v_R(x,y) field is found for a simulation with a bar length R_b\approx3.6 kpc. We argue that the data are consistent with a MW bar as short as ~3 kpc, for moderate strength inner disc spiral structure (A_2/A_0\approx0.25) or, alternatively, with a bar length up to ~5.2 kpc, provided that spiral arms are quite weak (A_2/A_0\approx0.1), and is most likely in the process of disconnecting from a spiral arm. We demonstrate that the bar angle and distance uncertainty can similarly affect the match between our models and the data - a smaller bar angle (20 deg instead of 30 deg) requires smaller distance uncertainty (20% instead of 30%) to explain the observations. Fourier components of the face-on density distribution of our models suggest that the MW does not have strong m=1 and/or m=3 spirals near the solar radius.

astro-ph.GA↗

arXiv2023

Beyond Gaia DR3: Tracing the [$α$/M]-[M/H] bimodality from the inner to the outer Milky Way disc with Gaia-RVS and convolutional neural networks

In June 2022, Gaia DR3 has provided the astronomy community with about one million spectra from the Radial Velocity Spectrometer (RVS) covering the CaII triplet region. However, one-third of the published spectra have 15<S/N<25 per pixel such that they pose problems for classical spectral analysis pipelines, and therefore, alternative ways to tap into these large datasets need to be devised. We aim to leverage the versatility and capabilities of machine learning techniques for supercharged stellar parametrisation by combining Gaia-RVS spectra with the full set of Gaia products and high-resolution, high-quality ground-based spectroscopic reference datasets. We developed a hybrid convolutional neural network (CNN) that combines the Gaia DR3 RVS spectra, photometry (G, G_BP, G_RP), parallaxes, and XP coefficients to derive atmospheric parameters (Teff, log(g) as well as overall [M/H]) and chemical abundances ([Fe/H] and [α/M]). We trained the CNN with a high-quality training sample based on APOGEE DR17 labels. With this CNN, we derived homogeneous atmospheric parameters and abundances for 886080 RVS stars that show remarkable precision and accuracy compared to external datasets (such as GALAH and asteroseismology). The CNN is robust against noise in the RVS data, and we derive very precise labels down to S/N=15. We managed to characterise the [α/M]-[M/H] bimodality from the inner regions to the outer parts of the Milky Way, which has never been done using RVS spectra or similar datasets. This work is the first to combine machine learning with such diverse datasets and paves the way for large-scale machine learning analysis of Gaia-RVS spectra from future data releases. Large, high-quality datasets can be optimally combined thanks to the CNN, thereby realising the full power of spectroscopy, astrometry, and photometry.

astro-ph.GA↗

arXiv2023

A portrait of the Vast Polar Structure as a young phenomenon: hints from its member satellites

It has been observed that several Milky Way (MW) satellite dwarf galaxies are distributed along a coherent planar distribution known as the Vast Polar Structure (VPOS). Here we investigate whether MW satellites located on the VPOS have different physical and orbital properties from those not associated with it. Using the proper motion measurements of the MW satellites from the \textit{Gaia} mission and literature values for their observational parameters, we first discriminate between systems that may or may not be associated with the VPOS, and then compare their chemical and dynamical properties. Comparing the luminosity distributions of the on-plane and off-plane samples, we find an excess of bright satellites observed on the VPOS. Despite this luminosity gap, we do not observe a significant preference for on-plane and off-plane systems to follow different scaling relations. The on-plane systems also show a striking pattern in their radial velocities and orbital phases: co-orbiting satellites are almost all approaching their pericenters, while both counter-orbiting satellites are leaving their last pericenters. This contrasts with the more random distribution of the off-plane sample. The on-plane systems also tend to have the lowest orbital energies for a given value of angular momentum. These results are robust to the assumed MW potential, even in the case of a potential perturbed by the arrival of a massive LMC. Considering them a significant property of the VPOS, we explore several scenarios, all related to the late accretion of satellite systems, which interpret the VPOS as a young structure. We hypothesise that the VPOS formed as a result of the accretion of a group of dwarf galaxies. More accurate proper motions and dedicated studies in the context of cosmological simulations are needed to confirm this scenario.

astro-ph.GA↗

arXiv2023

The Gaia-ESO Survey: Preparing the ground for 4MOST & WEAVE galactic surveys. Chemical evolution of lithium with machine learning

With its origin coming from several sources (Big Bang, stars, cosmic rays) and given its strong depletion during its stellar lifetime, the lithium element is of great interest as its chemical evolution in the Milky Way is not well understood at present. To help constrain stellar and galactic chemical evolution models, numerous and precise lithium abundances are necessary for a large range of evolutionary stages, metallicities, and Galactic volume. In the age of stellar parametrization on industrial scales, spectroscopic surveys such as APOGEE, GALAH, RAVE, and LAMOST have used data-driven methods to rapidly and precisely infer stellar labels (atmospheric parameters and abundances). To prepare the ground for future spectroscopic surveys such as 4MOST and WEAVE, we aim to apply machine learning techniques to lithium measurements and analyses. We trained a convolution neural network (CNN), coupling Gaia-ESO Survey iDR6 stellar labels (Teff, log(g), [Fe/H], and A(Li)) and GIRAFFE HR15N spectra, to infer the atm parameters and lithium abundances for ~40,000 stars. We show that the CNN properly learns the physics of the stellar labels, from relevant spectral features through a broad range of evolutionary stages and stellar parameters. The Li feature at 6707.8 A is successfully singled out by our CNN, among the thousands of lines. Rare objects such as Li-rich giants are found in our sample. This level of performance is achieved thanks to a meticulously built, high-quality, and homogeneous training sample. The CNN approach is very well adapted for the next generations of spectroscopic surveys aimed at studying (among other elements) lithium, such as the 4MIDABLE-LR/HR (4MOST Milky Way disk and bulge low- and high-resolution) surveys. In this context, the caveats of ML applications should be appropriately investigated, along with the realistic label uncertainties and upper limits for abundances.

astro-ph.GA↗

arXiv2021

Photo-astrometric distances, extinctions, and astrophysical parameters for Gaia EDR3 stars brighter than G=18.5

We present a catalogue of 362 million stellar parameters, distances, and extinctions derived from Gaia's early third data release (EDR3) cross-matched with the photometric catalogues of Pan-STARRS1, SkyMapper, 2MASS, and AllWISE. The higher precision of the Gaia EDR3 data, combined with the broad wavelength coverage of the additional photometric surveys and the new stellar-density priors of the {\tt StarHorse} code allow us to substantially improve the accuracy and precision over previous photo-astrometric stellar-parameter estimates. At magnitude $G=14\, (17)$, our typical precisions amount to 3% (15%) in distance, 0.13 mag (0.15 mag) in $V$-band extinction, and 140 K (180 K) in effective temperature. Our results are validated by comparisons with open clusters, as well as with asteroseismic and spectroscopic measurements, indicating systematic errors smaller than the nominal uncertainties for the vast majority of objects. We also provide distance- and extinction-corrected colour-magnitude diagrams, extinction maps, and extensive stellar density maps that reveal detailed substructures in the Milky Way and beyond. The new density maps now probe a much greater volume, extending to regions beyond the Galactic bar and to Local Group galaxies, with a larger total number density. We publish our results through an ADQL query interface ({\tt gaia.aip.de}) as well as via tables containing approximations of the full posterior distributions. Our multi-wavelength approach and the deep magnitude limit make our results useful also beyond the next Gaia release, DR3.

astro-ph.GA↗

arXiv2021

The Milky Way bar and bulge revealed by APOGEE DR16 and Gaia EDR3

We investigate the inner regions of the Milky Way with a sample of unprecedented size and coverage thanks to APOGEE DR16 and Gaia EDR3 data. Our inner Galactic sample has more than 26,000 stars within $|X_{\rm Gal}| <5$ kpc, $|Y_{\rm Gal}| <3.5$ kpc, $|Z_{\rm Gal}| <1$ kpc, and we also make the analysis for a foreground-cleaned sub-sample of 8,000 stars more representative of the bulge-bar populations. The inner Galaxy shows a clear chemical discontinuity in key abundance ratios [$α$/Fe], [C/N], and [Mn/O], probing different enrichment timescales, which suggests a star formation gap (quenching) between the high- and low-$α$ populations. For the first time, we are able to fully characterize the different populations co-existing in the innermost regions of the Galaxy via joint analysis of the distributions of rotational velocities, metallicities, orbital parameters and chemical abundances. The chemo-kinematic analysis reveals the presence of the bar; of an inner thin disk; of a thick disk, and of a broad metallicity population, with a large velocity dispersion, indicative of a pressure supported component. We find and characterize chemically and kinematically a group of counter-rotating stars, which could be the result of a gas-rich merger event or just the result of clumpy star formation during the earliest phases of the early disk, which migrated into the bulge. Finally, based on the 6D information we assign stars a probability value of being on a bar orbit and find that most of the stars with large bar orbit probabilities come from the innermost 3 kpcs. Even stars with a high probability of belonging to the bar show the chemical bimodality in the [$α$/Fe] vs. [Fe/H] diagram. This suggests bar trapping to be an efficient mechanism, explaining why stars on bar orbits do not show a significant distinct chemical abundance ratio signature.

astro-ph.GA↗

arXiv2021

LOFAR imaging of Cygnus A -- Direct detection of a turnover in the hotspot radio spectra

The low-frequency radio spectra of the hotspots within powerful radio galaxies can provide valuable information about the physical processes operating at the site of the jet termination. These processes are responsible for the dissipation of jet kinetic energy, particle acceleration, and magnetic-field generation. Here we report new observations of the powerful radio galaxy Cygnus A using the Low Frequency Array (LOFAR) between 109 and 183 MHz, at an angular resolution of ~3.5 arcsec. The radio emission of the lobes is found to have a complex spectral index distribution, with a spectral steepening found towards the centre of the source. For the first time, a turnover in the radio spectrum of the two main hotspots of Cygnus A has been directly observed. By combining our LOFAR imaging with data from the Very Large Array at higher frequencies, we show that the very rapid turnover in the hotspot spectra cannot be explained by a low-energy cut-off in the electron energy distribution, as has been previously suggested. Thermal (free-free) absorption or synchrotron self absorption models are able to describe the low-frequency spectral shape of the hotspots, however, as with previous studies, we find that the implied model parameters are unlikely, and interpreting the spectra of the hotspots remains problematic.

astro-ph.GA↗

arXiv2021

MOSAIC on the ELT: high-multiplex spectroscopy to unravel the physics of stars and galaxies from the dark ages to the present-day

The powerful combination of the cutting-edge multi-object spectrograph MOSAIC with the world largest telescope, the ELT, will allow us to probe deeper into the Universe than was possible. MOSAIC is an extremely efficient instrument in providing spectra for the numerous faint sources in the Universe, including the very first galaxies and sources of cosmic reionization. MOSAIC has a high multiplex in the NIR and in the VIS, in addition to multi-Integral Field Units (Multi-IFUs) in NIR. As such it is perfectly suited to carry out an inventory of dark matter (from rotation curves) and baryons in the cool-warm gas phases in galactic haloes at z=3-4. MOSAIC will enable detailed maps of the intergalactic medium at z=3, the evolutionary history of dwarf galaxies during a Hubble time, the chemistry directly measured from stars up to several Mpc. Finally, it will measure all faint features seen in cluster gravitational lenses or in streams surrounding nearby galactic halos, providing MOSAIC to be a powerful instrument with an extremely large space of discoveries. The preliminary design of MOSAIC is expected to begin next year, and its level of readiness is already high, given the instrumental studies made by the team.

astro-ph.GA↗

arXiv2020

Optical emission lines in the most massive galaxies: morphology, kinematics and ionisation properties

To better characterize the upper end of the galaxy stellar mass range, the MUSE Most Massive Galaxies (M3G) Survey targeted the most massive galaxies (M$>10^{12}$ M$_{\odot}$) found in the densest known clusters of galaxies at $z\sim0.046$. The sample is composed by 25 early-type galaxies: 14 BCGs, of which 3 are in the densest region of the Shapley Super Cluster (SSC), and 11 massive satellites in the SSC. In this work we aim at deriving the spatial distribution and kinematics of the gas, and discussing its ionisation mechanism and origin in the optical wavelength range with MUSE data. We fit the continuum of the spectra using an extensive library of single stellar population models and model the emission lines employing up to three Gaussian functions. In the M3G sample, ionized-gas was detected in 5 BCGs, of which one is in the densest region of the SSC, and 6 massive satellites in the SSC. Among these objects, [OI] and [NI] were detected in 3 BCGs and one satellite. The gas is centrally concentrated in almost all objects, except for 2 BCGs that show filaments and 2 massive satellites with extended emission. The emission line profiles of 3 BCGs present red/blueshifted components. The presence of dust was revealed by analysing Balmer line ratios obtaining a mean $E(B-V)$ of 0.2-0.3. The emission-line diagnostic diagrams show predominately LINER line ratios with little contamination from star formation. The gas was detected in 80% of fast rotators and 35% of slow rotators. The orientations of stellar and gaseous rotations are aligned with respect to each other for 60% of satellites and 25% of BCGs. The presence of misalignments points to an external origin of the gas for 3 BCGs and 2 satellites. On the other hand, some of these systems are characterized by triaxial and prolate-like stellar rotation that could support an internal origin of the gas even in case of misalignments.

astro-ph.GA↗

arXiv2020

The RAdial Velocity Experiment (RAVE): Parameterisation of RAVE spectra based on convolutional neural networks

In the context of large spectroscopic surveys of stars, data-driven methods are key in deducing physical parameters for millions of spectra in a short time. Convolutional neural networks (CNNs) enable us to connect observables (e.g. spectra, stellar magnitudes) to physical properties (atmospheric parameters, chemical abundances, or labels in general). We trained a CNN, adopting stellar atmospheric parameters and chemical abundances from APOGEE DR16 (resolution R=22500) data as training set labels. As input, we used parts of the intermediate-resolution RAVE DR6 spectra (R~7500) overlapping with the APOGEE DR16 data as well as broad-band ALL_WISE and 2MASS photometry, together with Gaia DR2 photometry and parallaxes. We derived precise atmospheric parameters Teff, log(g), and [M/H] along with the chemical abundances of [Fe/H], [alpha/M], [Mg/Fe], [Si/Fe], [Al/Fe], and [Ni/Fe] for 420165 RAVE spectra. The precision typically amounts to 60K in Teff, 0.06 in log(g) and 0.02-0.04 dex for individual chemical abundances. Incorporating photometry and astrometry as additional constraints substantially improves the results in terms of the accuracy and precision of the derived labels. We provide a catalogue of CNN-trained atmospheric parameters and abundances along with their uncertainties for 420165 stars in the RAVE survey. CNN-based methods provide a powerful way to combine spectroscopic, photometric, and astrometric data without the need to apply any priors in the form of stellar evolutionary models. The developed procedure can extend the scientific output of RAVE spectra beyond DR6 to ongoing and planned surveys such as Gaia RVS, 4MOST, and WEAVE. We call on the community to place a particular collective emphasis and on efforts to create unbiased training samples for such future spectroscopic surveys.

astro-ph.GA↗

arXiv2020

The eROSITA X-ray telescope on SRG

eROSITA (extended ROentgen Survey with an Imaging Telescope Array) is the primary instrument on the Spectrum-Roentgen-Gamma (SRG) mission, which was successfully launched on July 13, 2019, from the Baikonour cosmodrome. After the commissioning of the instrument and a subsequent calibration and performance verification phase, eROSITA started a survey of the entire sky on December 13, 2019. By the end of 2023, eight complete scans of the celestial sphere will have been performed, each lasting six months. At the end of this program, the eROSITA all-sky survey in the soft X-ray band (0.2--2.3\,keV) will be about 25 times more sensitive than the ROSAT All-Sky Survey, while in the hard band (2.3--8\,keV) it will provide the first ever true imaging survey of the sky. The eROSITA design driving science is the detection of large samples of galaxy clusters up to redshifts $z>1$ in order to study the large-scale structure of the universe and test cosmological models including Dark Energy. In addition, eROSITA is expected to yield a sample of a few million AGNs, including obscured objects, revolutionizing our view of the evolution of supermassive black holes. The survey will also provide new insights into a wide range of astrophysical phenomena, including X-ray binaries, active stars, and diffuse emission within the Galaxy. Results from early observations, some of which are presented here, confirm that the performance of the instrument is able to fulfil its scientific promise. With this paper, we aim to give a concise description of the instrument, its performance as measured on ground, its operation in space, and also the first results from in-orbit measurements.

astro-ph.HE↗

arXiv2020

Fluctuations in galactic bar parameters due to bar-spiral interaction

We study the late-time evolution of the central regions of two Milky Way-like simulations of galaxies formed in a cosmological context, one hosting a fast bar and the other a slow one. We find that bar length, R_b, measurements fluctuate on a dynamical timescale by up to 100%, depending on the spiral structure strength and measurement threshold. The bar amplitude oscillates by about 15%, correlating with R_b. The Tremaine-Weinberg-method estimates of the bars' instantaneous pattern speeds show variations around the mean of up to ~20%, typically anti-correlating with the bar length and strength. Through power spectrum analyses, we establish that these bar pulsations, with a period in the range ~60-200 Myr, result from its interaction with multiple spiral modes, which are coupled with the bar. Because of the presence of odd spiral modes, the two bar halves typically do not connect at exactly the same time to a spiral arm, and their individual lengths can be significantly offset. We estimated that in about 50% of bar measurements in Milky Way-mass external galaxies, the bar lengths of SBab type galaxies are overestimated by ~15% and those of SBbc types by ~55%. Consequently, bars longer than their corotation radius reported in the literature, dubbed "ultra-fast bars", may simply correspond to the largest biases. Given that the Scutum-Centaurus arm is likely connected to the near half of the Milky Way bar, recent direct measurements may be overestimating its length by 1-1.5 kpc, while its present pattern speed may be 5-10 km/s/kpc smaller than its time-averaged value.

astro-ph.GA↗