Search

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], Comment: A&A, accepted. 13 pages, 13 figures + references & appendices. Data available at https://data.aip.de/projects/shboost2024.html

For any natural number $d$, a graph $G$ is a (disjoint) $d$-interval graph if it is the intersection graph of (disjoint) $d$-intervals, the union of $d$ (disjoint) intervals on the real line. Two important subclasses of $d$-interval graphs are unit and balanced $d$-interval graphs (where every interval has unit length or all the intervals associated to a same vertex have the same length, respectively). A celebrated result by Roberts gives a simple characterization of unit interval graphs being exactly claw-free interval graphs. Here, we study the generalization of this characterization for $d$-interval graphs. In particular, we prove that for any $d \geq 2$, if $G$ is a $K_{1,2d+1}$-free interval graph, then $G$ is a unit $d$-interval graph. However, somehow surprisingly, under the same assumptions, $G$ is not always a \emph{disjoint} unit $d$-interval graph. This implies that the class of disjoint unit $d$-interval graphs is strictly included in the class of unit $d$-interval graphs. Finally, we study the relationships between the classes obtained under disjoint and non-disjoint $d$-intervals in the balanced case and show that the classes of disjoint balanced 2-intervals and balanced 2-intervals coincide, but this is no longer true for $d>2$.

Interpreting the observations of exoplanet atmospheres to constrain physical and chemical properties is typically done using Bayesian retrieval techniques. Because these methods require many model computations, a compromise is made between model complexity and run time. Reaching this compromise leads to the simplification of many physical and chemical processes (e.g. parameterised temperature structure). Here we implement and test sequential neural posterior estimation (SNPE), a machine learning inference algorithm, for exoplanet atmospheric retrievals. The goal is to speed up retrievals so they can be run with more computationally expensive atmospheric models, such as those computing the temperature structure using radiative transfer. We generate 100 synthetic observations using ARCiS (ARtful Modeling Code for exoplanet Science, an atmospheric modelling code with the flexibility to compute models in varying degrees of complexity) and perform retrievals on them to test the faithfulness of the SNPE posteriors. The faithfulness quantifies whether the posteriors contain the ground truth as often as we expect. We also generate a synthetic observation of a cool brown dwarf using the self-consistent capabilities of ARCiS and run a retrieval with self-consistent models to showcase the possibilities that SNPE opens. We find that SNPE provides faithful posteriors and is therefore a reliable tool for exoplanet atmospheric retrievals. We are able to run a self-consistent retrieval of a synthetic brown dwarf spectrum using only 50,000 forward model evaluations. We find that SNPE can speed up retrievals between $\sim2\times$ and $\geq10\times$ depending on the computational load of the forward model, the dimensionality of the observation, and the signal-to-noise ratio of the observation. We make the code publicly available for the community on Github., Comment: Accepted for publication at A&A

We report the discovery of a previously uncatalogued arch-shaped filamentary nebula at the outer part of the Triangulum galaxy (M33) centred at R.A. = 1h34m25s, Dec = +30d20m17s (ICRS). This discovery stems from meticulous observations employing deep exposures of M33, using both H-alpha and [OIII] narrow-band filters. The nebula, designated as "Roig1 Prades Sky", exhibits an H-alpha surface brightness of 23.9 mag/arcsec2. Its sky projected location is 21 arcmin away from the M33 galactic centre towards the southeast direction with an extent of 120 by 440 pc. Deep spectroscopic observations are required to unveil its real nature., Comment: 4 pages, 1 figure

WASP-107b is a warm ($\sim$740 K) transiting planet with a Neptune-like mass of $\sim$30.5 $M_{\oplus}$ and Jupiter-like radius of $\sim$0.94 $R_{\rm J}$, whose extended atmosphere is eroding. Previous observations showed evidence for water vapour and a thick high-altitude condensate layer in WASP-107b's atmosphere. Recently, photochemically produced sulphur dioxide (SO$_2$) was detected in the atmosphere of a hot ($\sim$1,200 K) Saturn-mass planet from transmission spectroscopy near 4.05 $\mu$m, but for temperatures below $\sim$1,000 K sulphur is predicted to preferably form sulphur allotropes instead of SO$_2$. Here we report the 9$\sigma$-detection of two fundamental vibration bands of SO$_2$, at 7.35 $\mu$m and 8.69 $\mu$m, in the transmission spectrum of WASP-107b using the Mid-Infrared Instrument (MIRI) of the JWST. This discovery establishes WASP-107b as the second irradiated exoplanet with confirmed photochemistry, extending the temperature range of exoplanets exhibiting detected photochemistry from $\sim$1,200 K down to $\sim$740 K. Additionally, our spectral analysis reveals the presence of silicate clouds, which are strongly favoured ($\sim$7$\sigma$) over simpler cloud setups. Furthermore, water is detected ($\sim$12$\sigma$), but methane is not. These findings provide evidence of disequilibrium chemistry and indicate a dynamically active atmosphere with a super-solar metallicity.

Brown dwarfs serve as ideal laboratories for studying the atmospheres of giant exoplanets on wide orbits as the governing physical and chemical processes in them are nearly identical. Understanding the formation of gas giant planets is challenging, often involving the endeavour to link atmospheric abundance ratios, such as the carbon-to-oxygen (C/O) ratio, to formation scenarios. However, the complexity of planet formation requires additional tracers, as the unambiguous interpretation of the measured C/O ratio is fraught with complexity. Isotope ratios, such as deuterium-to-hydrogen and 14N/15N, offer a promising avenue to gain further insight into this formation process, mirroring their utility within the solar system. For exoplanets only a handful of constraints on 12C/13C exist, pointing to the accretion of 13C-rich ice from beyond the disks' CO iceline. Here we report on the mid-infrared detection of the 14NH3 and 15NH3 isotopologues in the atmosphere of a cool brown dwarf with an effective temperature of 380 K in a spectrum taken with the Mid-InfraRed Instrument of the James Webb Space Telescope. As expected, our results reveal a 14N/15N value consistent with star-like formation by gravitational collapse, demonstrating that this ratio can be accurately constrained. Since young stars and their planets should be more strongly enriched in the 15N isotope, we expect that 15NH3 will be detectable in a number of cold, wide-separation exoplanets., Comment: Accepted by Nature. 28 pages, 7 figures, uses nature3.cls

Multiple interval graphs are a well-known generalization of interval graphs introduced in the 1970s to deal with situations arising naturally in scheduling and allocation. A $d$-interval is the union of $d$ intervals on the real line, and a graph is a $d$-interval graph if it is the intersection graph of $d$-intervals. In particular, it is a unit $d$-interval graph if it admits a $d$-interval representation where every interval has unit length. Whereas it has been known for a long time that recognizing 2-interval graphs and other related classes such as 2-track interval graphs is NP-complete, the complexity of recognizing unit 2-interval graphs remains open. Here, we settle this question by proving that the recognition of unit 2-interval graphs is also NP-complete. Our proof technique uses a completely different approach from the other hardness results of recognizing related classes. Furthermore, we extend the result for unit $d$-interval graphs for any $d\geq 2$, which does not follow directly in graph recognition problems --as an example, it took almost 20 years to close the gap between $d=2$ and $d> 2$ for the recognition of $d$-track interval graphs. Our result has several implications, including that recognizing $(x, \dots, x)$ $d$-interval graphs and depth $r$ unit 2-interval graphs is NP-complete for every $x\geq 11$ and every $r\geq 4$., Comment: Accepted in ISAAC 2023

Despite their relatively high intrinsic brightness and the fact that they are more numerous than younger OB stars and kinematically colder than older red giants, A-type stars have rarely been used as Galactic tracers. They may, in fact, be used to fill the age gap between these two tracers, thereby allowing us to study the transition between them. We analyse Galactic disc structure and kinematic perturbations up to 6 kpc from the Sun based on observations of A-type stars. This work presents a catalogue of A-type stars selected using the IGAPS photometric survey. It covers the Galactic disc within $30^{o}\leq l\leq215^{o}$ and $|b|\leq5^{o}$ up to a magnitude of $r\leq19$ mag with about 3.5 million sources. We used Gaia Data Release 3 parallaxes and proper motions, as well as the line-of-sight velocities, to analyse the large-scale features of the Galactic disc. We carried out a study of the completeness of the detected density distributions, along with a comparison between the $b<0^{o}$ and $b>0^{o}$ regions. Possible biases caused by interstellar extinction or by the usage of some kinematic approximations were examined as well. We find stellar overdensities associated with the Local and the Perseus spiral arms, as well as with the Cygnus region. A-type stars also provide kinematic indications of the Galactic warp towards the anticentre, which displays a median vertical motion of ~6-7 km/s at a Galactocentric radius of R=14 kpc. It starts at R=12 kpc, which supports the scenario where the warp begins at larger radii for younger tracers when compared with other samples in the literature. We also detect a region with downward mean motion extending beyond 2 kpc from the Sun towards $60^{o}

This work examines the conformational ensemble involved in $\beta$-hairpin folding by means of advanced molecular dynamics simulations and dimensionality reduction. A fully atomistic description of the protein and the surrounding solvent molecules is used and this complex energy landscape is sampled by means of parallel tempering metadynamics simulations. The ensemble of configurations explored is analysed using the recently proposed sketch-map algorithm. Further simulations allow us to probe how mutations affect the structures adopted by this protein. We find that many of the configurations adopted by a mutant are the same as those adopted by the wild type protein. Furthermore, certain mutations destabilize secondary structure containing configurations by preventing the formation of hydrogen bonds or by promoting the formation of new intramolecular contacts. Our analysis demonstrates that machine-learning techniques can be used to study the energy landscapes of complex molecules and that the visualizations that are generated in this way provide a natural basis for examining how the stabilities of particular configurations of the molecule are affected by factors such as temperature or structural mutations., Comment: Pre-print of doi:10.1021/ct500950z, reproduced with permission from the ACS

Given a network property or a data structure, a local certification is a labeling that allows to efficiently check that the property is satisfied, or that the structure is correct. The quality of a certification is measured by the size of its labels: the smaller, the better.This notion plays a central role in self-stabilization, because the size of the certification is a lower bound (and often an upper bound) on the memory needed for silent self-stabilizing construction of distributed data structures. From the point of view of distributed computing in general, it is also a measure of the locality of a property (e.g. properties of the network itself, such as planarity). When it comes to the size of the certification labels, one can identify three important regimes: the properties for which the optimal size is polynomial in the number of vertices of the graph, the ones that require only polylogarithmic size, and the ones that can be certified with a constant number of bits. The first two regimes are well studied, with several upper and lower bounds, specific techniques, and active research questions. On the other hand, the constant regime has never been really explored, at least on the lower bound side. The main contribution of this paper is the first non-trivial lower bound for this low regime. More precisely, we show that by using certification on just one bit (a binary certification), one cannot certify $k$-colorability for $k\geq 3$. To do so, we develop a new technique, based on the notion of score, and both local symmetry arguments and a global parity argument. We hope that this technique will be useful for establishing stronger results. We complement this result by a discussion of the implication of lower bounds for this constant-size regime, and with an upper bound for a related problem, illustrating that in some cases one can do better than the natural upper bound.

Exoplanet observations are currently analysed with Bayesian retrieval techniques. Due to the computational load of the models used, a compromise is needed between model complexity and computing time. Analysis of data from future facilities, will need more complex models which will increase the computational load of retrievals, prompting the search for a faster approach for interpreting exoplanet observations. Our goal is to compare machine learning retrievals of exoplanet transmission spectra with nested sampling, and understand if machine learning can be as reliable as Bayesian retrievals for a statistically significant sample of spectra while being orders of magnitude faster. We generate grids of synthetic transmission spectra and their corresponding planetary and atmospheric parameters, one using free chemistry models, and the other using equilibrium chemistry models. Each grid is subsequently rebinned to simulate both HST/WFC3 and JWST/NIRSpec observations, yielding four datasets in total. Convolutional neural networks (CNNs) are trained with each of the datasets. We perform retrievals on a 1,000 simulated observations for each combination of model type and instrument with nested sampling and machine learning. We also use both methods to perform retrievals on real WFC3 transmission spectra. Finally, we test how robust machine learning and nested sampling are against incorrect assumptions in our models. CNNs reach a lower coefficient of determination between predicted and true values of the parameters. Nested sampling underestimates the uncertainty in ~8% of retrievals, whereas CNNs estimate them correctly. For real WFC3 observations, nested sampling and machine learning agree within $2\sigma$ for ~86% of spectra. When doing retrievals with incorrect assumptions, nested sampling underestimates the uncertainty in ~12% to ~41% of cases, whereas this is always below ~10% for the CNN., Comment: Accepted for publication in A&A

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., Comment: A&A, accepted, 27 pages, 25 figures. Fig. 5 has changed with respect to v1 (post-processing bug corrected). For various options to access the data see https://data.aip.de/projects/starhorse2021.html

We present a coherent study of the impact of neutrino interactions on the r-process element nucleosynthesis and the heating rate produced by the radioactive elements synthesised in the dynamical ejecta of neutron star-neutron star (NS-NS) mergers. We have studied the material ejected from four NS-NS merger systems based on hydrodynamical simulations which handle neutrino effects in an elaborate way by including neutrino equilibration with matter in optically thick regions and re-absorption in optically thin regions. We find that the neutron richness of the dynamical ejecta is significantly affected by the neutrinos emitted by the post-merger remnant, in particular when compared to a case neglecting all neutrino interactions. Our nucleosynthesis results show that a solar-like distribution of r- process elements with mass numbers $A \gtrsim 90$ is produced, including a significant enrichment in Sr and a reduced production of actinides compared to simulations without inclusion of the nucleonic weak processes. The composition of the dynamically ejected matter as well as the corresponding rate of radioactive decay heating are found to be rather independent of the system mass asymmetry and the adopted equation of state. This approximate degeneracy in abundance pattern and heating rates can be favourable for extracting the ejecta properties from kilonova observations, at least if the dynamical component dominates the overall ejecta. Part II of this work will study the light curve produced by the dynamical ejecta of our four NS merger models., Comment: 18 pages, 21 figures, 2 Tables, accepted by MNRAS 2021-11-19

Neutrinos emitted in the coalescence of two neutron stars affect the dynamics of the outflow ejecta and the nucleosynthesis of heavy elements. In this work, we analyze the neutrino emission properties and the conditions leading to the growth of flavor instabilities in merger remnants consisting of a hypermassive neutron star and an accretion disk during the first 10 ms after the merger. The analyses are based on hydrodynamical simulations that include a modeling of neutrino emission and absorption effects via the "Improved Leakage-Equilibration-Absorption Scheme" (ILEAS). We also examine the nucleosynthesis of the heavy elements via the rapid neutron-capture process (r-process) inside the material ejected during this phase. The dominant emission of $\bar\nu_e$ over $\nu_e$ from the merger remnant leads to favorable conditions for the occurrence of fast pairwise flavor conversions of neutrinos, independent of the chosen equation of state or the mass ratio of the binary. The nucleosynthesis outcome is very robust, ranging from the first to the third r-process peaks. In particular, more than $10^{-5}$ $M_\odot$ of strontium are produced in these early ejecta that may account for the GW170817 kilonova observation. We find that the amount of ejecta containing free neutrons after the $r$-process freeze-out, which may power early-time UV emission, is reduced by roughly a factor of 10 when compared to simulations that do not include weak interactions. Finally, the potential flavor equipartition between all neutrino flavors is mainly found to affect the nucleosynthesis outcome in the polar ejecta within $\lesssim 30^\circ$, by changing the amount of the produced iron-peak and first-peak nuclei, but it does not alter the lanthanide mass fraction therein., Comment: matches the published version

We present a new, computationally efficient, energy-integrated approximation for neutrino effects in hot and dense astrophysical environments such as supernova cores and compact binary mergers and their remnants. Our new method, termed ILEAS for Improved Leakage-Equilibration-Absorption Scheme, improves the lepton-number and energy losses of traditional leakage descriptions by a novel prescription of the diffusion time-scale based on a detailed energy integral of the flux-limited diffusion equation. The leakage module is supplemented by a neutrino-equilibration treatment that ensures the proper evolution of the total lepton number and medium plus neutrino energies as well as neutrino-pressure effects in the neutrino-trapping domain. Moreover, we employ a simple and straightforwardly applicable ray-tracing algorithm for including re-absorption of escaping neutrinos especially in the decoupling layer and during the transition to semi-transparent conditions. ILEAS is implemented on a three-dimensional (3D) Cartesian grid with a minimum of free and potentially case-dependent parameters and exploits the basic physics constraints that should be fulfilled in the neutrino-opaque and free-streaming limits. We discuss a suite of tests for stationary and time-dependent proto-neutron star models and post-merger black-hole-torus configurations, for which 3D ILEAS results are demonstrated to agree with energy-dependent 1D and 2D two-moment (M1) neutrino transport on the level of 10--15 percent in basic neutrino properties. This also holds for the radial profiles of the neutrino luminosities and of the electron fraction. Even neutrino absorption maps around torus-like neutrino sources are qualitatively similar without any fine-tuning, confirming that ILEAS can satisfactorily reproduce local losses and re-absorption of neutrinos as found in sophisticated transport calculations., Comment: 39 pages, 19 figures; revised version incl. referee requests; new Sect.3.4 with first NS-NS merger models added; accepted by MNRAS