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1. A deep learning model for inter-fraction head and neck anatomical changes

Author

Burlacu, Tiberiu, Hoogeman, Mischa, Lathouwers, Danny, and Perkó, Zoltán

Subjects
Physics - Medical Physics
Abstract

Objective: To assess the performance of a probabilistic deep learning based algorithm for predicting inter-fraction anatomical changes in head and neck patients. Approach: A probabilistic daily anatomy model for head and neck patients $(\mathrm{DAM}_{\mathrm{HN}})$ is built on the variational autoencoder architecture. The model approximates the generative joint conditional probability distribution of the repeat computed tomography (rCT) images and their corresponding masks on the planning CT images (pCT) and their masks. The model outputs deformation vector fields, which are used to produce possible rCTs and associated masks. The dataset is composed of 93 patients (i.e., 367 pCT - rCT pairs), 9 (i.e., 37 pairs) of which were set aside for final testing. The performance of the model is assessed based on the reconstruction accuracy and the generative performance for the set aside patients. Main results: The model achieves a DICE score of 0.92 and an image similarity score of 0.65 on the test set. The generated parotid glands volume change distributions and center of mass shift distributions were also assessed. For both, the medians of the distributions are close to the true ones, and the distributions are broad enough to encompass the real observed changes. Moreover, the generated images display anatomical changes in line with the literature reported ones, such as the medial shifts of the parotids glands. Significance: $(\mathrm{DAM}_{\mathrm{HN}})$ is capable of generating realistic anatomies observed during the course of the treatment and has applications in anatomical robust optimization, treatment planning based on plan library approaches and robustness evaluation against inter-fractional changes.

Published
2024

2. OVERVIEW OF COMPUTER VISION SUPERVISED LEARNING TECHNIQUES FOR LOW-DATA TRAINING

Author

BURLACU, Alexandru

Subjects
knowledge distillation, knowledge transfer, self-supervised learning, semi-supervised learning, weakly-supervised learning, Engineering (General). Civil engineering (General), TA1-2040, Electronic computers. Computer science, QA75.5-76.95
Abstract

In the age of big data and machine learning the costs to turn the data into fuel for the algorithms is prohibitively high. Organizations that can train better models with fewer annotation efforts will have a competitive edge. This work is an overview of techniques of varying complexity and novelty for supervised, or rather weakly supervised learning for computer vision algorithms. The paper starts describing various methods to ease the need for a big labeled dataset with giving some background on supervised, weakly-supervised and then self-supervised learning in general, and in computer vision specifically. The paper describes the importance of these methods in fields such as medical imaging and autonomous driving.

Published
2020
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3. All You Need is a Paraboloid: Quadratic Cuts for Non-Convex MINLP

Author

Göß, Adrian, Burlacu, Robert, and Martin, Alexander

Subjects
Mathematics - Optimization and Control, 90C11, 90C20, 90C26, 90C30
Abstract

It is only half the job to find a good solution for a mathematical optimization problem, as one needs to verify its quality by specifying a dual bound. When it comes to mixed-integer nonlinear programming (MINLP), strong prerequisites such as constraint qualifications appear suitable, but may be difficult to verify computationally. In practice, solvers apply local refinement or convexification strategies to retrieve tight dual bounds. However, these concepts require appropriate big-M formulations, generate new sub-problems, or struggle to represent non-convex characteristics in terms of high accuracy, all of which can lead to long running times. As an alternative, we aim to leverage recent advances in mixed-integer quadratically-constrained programming (MIQCP) and propose a global approximation of constraint functions by paraboloids, \ie, univariate quadratic terms. The approximation is retrieved as a solution to a mixed-integer linear programming (MIP) problem. Further, for each nonlinear constraint function, we solve such MIPs and determine small numbers of paraboloids approximating it from either side. A replacement of the nonlinearities with the corresponding quadratic functions leads to a quadratically-constrained relaxation of the original problem. Solving the MIQCP relaxation then leads to a dual bound whose tightness depends on the approximation guarantee of the paraboloids. In summary, this approach enables solvers that are explicitly tailored for quadratic constraints to solve MINLPs to global optimality., Comment: 29 pages (22 + 7 appendix), 7 figures, currently under review at Mathematical Programming A

Published
2024

4. Yet anOther Dose Algorithm (YODA) for independent computations of dose and dose changes due to anatomical changes

Author

Burlacu, Tiberiu, Lathouwers, Danny, and Perkó, Zoltán

Subjects
Physics - Medical Physics
Abstract

$\textbf{Purpose:}$ To assess the viability of a physics-based, deterministic and adjoint-capable algorithm for performing treatment planning system independent dose calculations and for computing dosimetric differences caused by anatomical changes. $\textbf{Methods:}$ A semi-numerical approach is employed to solve two partial differential equations for the proton phase-space density which determines the deposited dose. Lateral hetereogeneities are accounted for by an optimized (Gaussian) beam splitting scheme. Adjoint theory is applied to approximate the change in the deposited dose caused by a new underlying patient anatomy. $\textbf{Results:}$ The quality of the dose engine was benchmarked through three-dimensional gamma index comparisons against Monte Carlo simulations done in TOPAS. The worst passing rate for the gamma index with (1 mm, 1 %, 10 % dose cut-off) criteria is 95.62 %. The effect of delivering treatment plans on repeat CTs was also tested. For a non-robustly optimized plan the adjoint component was accurate to 6.2 % while for a robustly optimized plan it was accurate to 1 %. $\textbf{Conclusions:}$ YODA is capable of accurate dose computations in both single and multi spot irradiations when compared to TOPAS. Moreover, it is able to compute dosimetric differences due to anatomical changes with small to moderate errors thereby facilitating its use for patient-specific quality assurance in online adaptive proton therapy.

Published
2024

6. Multi-View Symbolic Regression

Author

Russeil, Etienne, de França, Fabrício Olivetti, Malanchev, Konstantin, Burlacu, Bogdan, Ishida, Emille E. O., Leroux, Marion, Michelin, Clément, Moinard, Guillaume, and Gangler, Emmanuel

Subjects
Computer Science - Machine Learning, Astrophysics - Instrumentation and Methods for Astrophysics, Statistics - Applications
Abstract

Symbolic regression (SR) searches for analytical expressions representing the relationship between a set of explanatory and response variables. Current SR methods assume a single dataset extracted from a single experiment. Nevertheless, frequently, the researcher is confronted with multiple sets of results obtained from experiments conducted with different setups. Traditional SR methods may fail to find the underlying expression since the parameters of each experiment can be different. In this work we present Multi-View Symbolic Regression (MvSR), which takes into account multiple datasets simultaneously, mimicking experimental environments, and outputs a general parametric solution. This approach fits the evaluated expression to each independent dataset and returns a parametric family of functions f(x; theta) simultaneously capable of accurately fitting all datasets. We demonstrate the effectiveness of MvSR using data generated from known expressions, as well as real-world data from astronomy, chemistry and economy, for which an a priori analytical expression is not available. Results show that MvSR obtains the correct expression more frequently and is robust to hyperparameters change. In real-world data, it is able to grasp the group behavior, recovering known expressions from the literature as well as promising alternatives, thus enabling the use of SR to a large range of experimental scenarios., Comment: Published in GECCO-2024. 11 pages, 5 figures

Published
2024

7. Control of morphology and luminescense of nanostructured ZnO layers for lasing applications

Author

Burlacu, Alexandru

Subjects
Physics, QC1-999, Electronics, TK7800-8360
Abstract

A comparative analysis of various technological methods for the preparation of nanostructured ZnO films is performed in this work to identify technologies suitable for the development of laser structures. It is shown that arrays of nanorods with a regular hexagonal geometry are readily produced by low-pressure chemical vapor deposition (CVD), chemical bath deposition (CBD), and electrochemical depositions (ECD) and these nanorods can play the role of resonators for guided modes. Irregular nanostructured layers with strong light scattering properties can be prepared by metal-organic chemical vapor deposition (MOCVD), aerosol spray pyrolysis, and by thermal treatment of ZnSe crystals. As concerns the optical quality necessary for stimulated emission and optical gain, low-pressure CVD and MOCVD are most suitable, while CBD and ECD can hardly provide the required quality for lasing action.

Published
2016

8. Thyroid hormones for euthyroid patients with simple goiter growing over time: a survey of European thyroid specialists

Author

Papini, Enrico, Attanasio, Roberto, Žarković, Miloš, Nagy, Endre Vezekenyi, Negro, Roberto, Perros, Petros, Galofré, Juan Carlos, Cohen, Chagit Adler, Akarsu, Ersin, Alevizaki, Maria, Ayvaz, Göksun, Bednarczuk, Tomasz, Beleslin, Biljana Nedeljković, Berta, Eszter, Bodor, Miklos, Borissova, Anna Maria, Boyanov, Mihail, Buffet, Camille, Burlacu, Maria-Cristina, Ćirić, Jasmina, Díez, Juan J., Dobnig, Harald, Fadeyev, Valentin, Field, Benjamin C. T., Führer-Sakel, Dagmar, Hakala, Tommi, Jiskra, Jan, Kopp, Peter Andreas, Krebs, Michael, Kršek, Michal, Lantz, Mikael, Lazúrová, Ivica, Leenhardt, Laurence, Luchytskiy, Vitaliy, Puga, Francisca Marques, McGowan, Anne, Melo, Miguel, Metso, Saara, Moran, Carla, Morgunova, Tatyana, Niculescu, Dan Alexandru, Perić, Božidar, Planck, Tereza, Robenshtok, Eyal, Rosselet, Patrick Olivier, Ruchala, Marek, Riis, Kamilla Ryom, Shepelkevich, Alla, Tronko, Mykola, Unuane, David, Vardarli, Irfan, Visser, W. Edward, Vryonidou, Andromachi, Younes, Younes Ramazan, and Hegedüs, Laszlo

Published
2024
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9. Treatment of Obesity with Thyroid hormones in Europe. Data from the THESIS* Collaboration

Author

Galofré, J. C., Díez, J. J., Attanasio, R., Nagy, E. V., Negro, R., Papini, E., Perros, P., Žarković, M., Akarsu, E., Alevizaki, M., Ayvaz, G., Bednarczuk, T., Beleslin, B. N., Berta, E., Bodor, M., Borissova, A. M., Boyanov, M., Buffet, C., Burlacu, M. C., Dobnig, H., Fadeyev, V., Field, B. C. T., Fliers, E., Führer, D., Hakala, T., Jiskra, J., Kopp, P., Krebs, M., Kršek, M., Kužma, M., Lantz, M., Lazúrová, I., Leenhardt, L., Luchytskiy, V., Puga, F. M., McGowan, A., Metso, S., Moran, C., Morgunova, T., Niculescu, D. A., Perić, B., Planck, T., Poiana, C., Robenshtok, E., Rosselet, P. O., Ruchala, M., Riis, K. R., Shepelkevich, A., Tronko, M., Unuane, D., Vardarli, I., Visser, W. E., Vryonidou, M., Younes, Y. R., and Hegedüs, L.

Published
2024
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10. A precise symbolic emulator of the linear matter power spectrum

Author

Bartlett, Deaglan J., Kammerer, Lukas, Kronberger, Gabriel, Desmond, Harry, Ferreira, Pedro G., Wandelt, Benjamin D., Burlacu, Bogdan, Alonso, David, and Zennaro, Matteo

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing
Abstract

Computing the matter power spectrum, $P(k)$, as a function of cosmological parameters can be prohibitively slow in cosmological analyses, hence emulating this calculation is desirable. Previous analytic approximations are insufficiently accurate for modern applications, so black-box, uninterpretable emulators are often used. We utilise an efficient genetic programming based symbolic regression framework to explore the space of potential mathematical expressions which can approximate the power spectrum and $\sigma_8$. We learn the ratio between an existing low-accuracy fitting function for $P(k)$ and that obtained by solving the Boltzmann equations and thus still incorporate the physics which motivated this earlier approximation. We obtain an analytic approximation to the linear power spectrum with a root mean squared fractional error of 0.2% between $k = 9\times10^{-3} - 9 \, h{\rm \, Mpc^{-1}}$ and across a wide range of cosmological parameters, and we provide physical interpretations for various terms in the expression. Our analytic approximation is 950 times faster to evaluate than camb and 36 times faster than the neural network based matter power spectrum emulator BACCO. We also provide a simple analytic approximation for $\sigma_8$ with a similar accuracy, with a root mean squared fractional error of just 0.1% when evaluated across the same range of cosmologies. This function is easily invertible to obtain $A_{\rm s}$ as a function of $\sigma_8$ and the other cosmological parameters, if preferred. It is possible to obtain symbolic approximations to a seemingly complex function at a precision required for current and future cosmological analyses without resorting to deep-learning techniques, thus avoiding their black-box nature and large number of parameters. Our emulator will be usable long after the codes on which numerical approximations are built become outdated., Comment: 9 pages, 5 figures. Accepted for publication in A&A

Published
2023
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11. Nano- and microscratching as a potential method for texturing the Si surface

Author

Prisăcaru, Andrian, Şikimaka, Olga, Harea, Evghenii, Burlacu, Alexandru, Enachi, Mihail, and Branişte, Fiodor

Subjects
Physics, QC1-999, Electronics, TK7800-8360
Abstract

The possibility of Si texturing through the use of nano- and microscratching with subsequent chemical etching has been investigated. The influence of the scratching speed, orientation of the indenter (face-on, edge-on) and normal load on the mechanism of deformation during scratching has been analyzed to reveal the favorable loading conditions to obtain plastic scratches. It has been found that the surface of scratches has a corrugated structure that was assumed to be the result of the stick-slip behavior during scratching. Different etching patterns displayed after chemical etching, such as inverse pyramids and a lamellar structure, have been attributed to the specific initial relief of the scratches and etching conditions.

Published
2014

13. Role of mucosal-associated invariant T cells dynamics in pathogenesis of Sjögren syndrome

Author

Chauffier, Jeanne, Berger de Gallardo, Henri, Chevalier, Mathieu F., Kante, Aïcha, Lambert, Marion, Cabrol, Xavier, Aldersons, Elisa, Mouly, Stéphane, Champion, Karine, Amador-Borrero, Blanca, Burlacu, Ruxandra, Bigot, William, Adle-Biassete, Homa, Kaci, Rachid, Selvanadin, Aurélie, Cohen-Solal, Martine, Coudert, Amélie, Caillat-Zucman, Sophie, Sène, Damien, and Comarmond, Cloé

Published
2024
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16. Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition

Author

de Franca, F. O., Virgolin, M., Kommenda, M., Majumder, M. S., Cranmer, M., Espada, G., Ingelse, L., Fonseca, A., Landajuela, M., Petersen, B., Glatt, R., Mundhenk, N., Lee, C. S., Hochhalter, J. D., Randall, D. L., Kamienny, P., Zhang, H., Dick, G., Simon, A., Burlacu, B., Kasak, Jaan, Machado, Meera, Wilstrup, Casper, and La Cava, W. G.

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Symbolic regression searches for analytic expressions that accurately describe studied phenomena. The main attraction of this approach is that it returns an interpretable model that can be insightful to users. Historically, the majority of algorithms for symbolic regression have been based on evolutionary algorithms. However, there has been a recent surge of new proposals that instead utilize approaches such as enumeration algorithms, mixed linear integer programming, neural networks, and Bayesian optimization. In order to assess how well these new approaches behave on a set of common challenges often faced in real-world data, we hosted a competition at the 2022 Genetic and Evolutionary Computation Conference consisting of different synthetic and real-world datasets which were blind to entrants. For the real-world track, we assessed interpretability in a realistic way by using a domain expert to judge the trustworthiness of candidate models.We present an in-depth analysis of the results obtained in this competition, discuss current challenges of symbolic regression algorithms and highlight possible improvements for future competitions., Comment: 13 pages, 13 figures, submitted to IEEE Transactions on Evolutionary Computation

Published
2023

17. Study on High Modulus Asphalt Mixtures

Author

Adrian Burlacu and Tudor Simion

Subjects
modulus of rigidity, modulus of elasticity, asphalt mixture, road structure design, Transportation engineering, TA1001-1280
Abstract

In this article will be presented a comparison of a road structure dimensioning between a classical approach, which uses the information existing in the sizing norm in force, and a non-classical approach that uses performances of asphalt mixtures with high rigidity modulus, determined in the laboratory, by specific tests to determine the modules, which will be used in the dimensioning calculation.

Published
2024
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18. Role of mucosal-associated invariant T cells dynamics in pathogenesis of Sjögren syndrome

Author

Jeanne Chauffier, Henri Berger de Gallardo, Mathieu F. Chevalier, Aïcha Kante, Marion Lambert, Xavier Cabrol, Elisa Aldersons, Stéphane Mouly, Karine Champion, Blanca Amador-Borrero, Ruxandra Burlacu, William Bigot, Homa Adle-Biassete, Rachid Kaci, Aurélie Selvanadin, Martine Cohen-Solal, Amélie Coudert, Sophie Caillat-Zucman, Damien Sène, and Cloé Comarmond

Subjects
Medicine, Science
Abstract

Abstract Sjögren syndrome (SS) is an autoimmune disease characterized by chronic inflammatory infiltrates in the salivary and lacrimal glands. Mucosal-associated invariant T (MAIT) cells are a subset of innate-like T-cells, predominantly found in mucosal tissues with crucial role in epithelial homeostasis. Thus, MAIT cells may be implicated in mucosal alterations of SS patients. Activation markers, inflammatory and cytotoxic cytokines were examined in 23 SS patients and compared to 23 healthy controls (HC). Tissular MAIT cells in salivary gland (SG) biopsies were also analyzed. Circulating MAIT cells were decreased in SS patients with a higher expression of CD69 and a higher CD4/CD8 ratio of MAIT cells. MAIT cells showed a higher production of IFNγ, TNFα and GzB in SS compare to HC. Tissular MAIT cells were present within inflamed SG of SS patients, while they were absent in SG of HC. Overall, circulating MAIT cells are decreased in the peripheral blood of SS albeit producing higher amounts of IFNγ, TNFα, and GzB. Tissular MAIT cells are detected in salivary glands from SS with a proinflammatory tissular cytokine environment. MAIT cells with abnormal phenotype, functions and tissular homeostasis may contribute to epithelial damage in SS.

Published
2024
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19. Financial Fraud. Challenges and Solutions for Financial Auditing and Accounting Professionals – a Bibliometric Research

Author

Georgiana BURLACU and Ioan-Bogdan ROBU

Subjects
financial fraud, literature review, bibliometric analysis, voswiewer, Accounting. Bookkeeping, HF5601-5689, Finance, HG1-9999
Abstract

Purpose - Financial fraud, considered an illegal act that aims to obtain financial benefits, has recently become a widespread menace in companies and organizations. Moreover, in recent years, financial fraud scandals of listed companies have occurred frequently, which has attracted great attention from regulatory bodies in various countries. This study aims to present an analysis of the evolution of scientific publications regarding financial fraud in recent years and how much debate and interest this topic has aroused from researchers. Methodology - This study started from a framework that combines bibliometric analysis techniques such as word frequency, co-authorship analysis, which allows the analysis of the relevance of the subject of financial fraud in the specialized literature. This method was used to discover what sub-themes were covered in the financial fraud research articles. Results - This study contributes to the specialized literature as follows: first of all, in this study it was possible to obtain information with reference to the most important journals, articles, authors, keywords that managed to capture aspects related to financial fraud. Secondly, the research results indicate a significant concern on the part of researchers regarding the determining factors of fraud, detection and prevention methods, the role of corporate governance, respectively the financial auditor in detecting and preventing fraudulent acts. Research limitation - The limits of this study can be given by the complexity of the topic, the time period and the number of articles analyzed. Originality - Although bibliometric analysis is quite well-known, there are limited publications that use this type of analysis, in order to review the specialized literature on the subject of financial fraud.

Published
2024
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20. Long-term hypothyroidism in patients started on levothyroxine during pregnancy

Author

Sophie Demartin, Stefan Matei Constantinescu, Kris G Poppe, Dominique Maiter, Raluca Maria Furnica, Orsalia Alexopoulou, Chantal Daumerie, Frederic Debiève, and Maria-Cristina Burlacu

Subjects
levothyroxine, pregnancy, hypothyroidism, follow-up, Diseases of the endocrine glands. Clinical endocrinology, RC648-665
Abstract

Background: Current guidelines recommend different postpartum approaches for patients started on levothyroxine (LT4) during pregnancy. Objective: We studied the postpartum management of these patients and determined factors associated with long-term hypothyroidism. Methods: A retrospective study was conducted at a tertiary center between 2014 and 2020, with LT4 initiation according to 2014 ETA recommendations. We performed multivariate logistic regression (MVR) and a receiver operating characteristic curve analysis to determine variables associated with long-term hypothyroidism and their optimal cutoffs. Results: LT4 was initiated in 177 pregnant women, and 106/177 (60%) were followed at long-term (at least 6 months post partum) (28.5 (9.0–81.9) months). LT4 could have been stopped in 45% of patients who continued it immediately after delivery. Thirty-six out of 106 (34%) patients were long-term hypothyroid. In them, LT4 was initiated earlier during pregnancy than in euthyroid women (11.7 ± 4.7 vs 13.7 ± 6.5 weeks, P = 0.077), at a higher thyroid-stimulating hormone (TSH) level (4.1 (2.2–10.1) vs 3.5 (0.9–6.9) mU/L, P = 0.005), and reached a higher dose during pregnancy (62.8 ± 22.2 vs 50.7 ± 13.9 μg/day, P = 0.005). In the MVR, only the maximal LT4 dose during pregnancy was associated with long-term hypothyroidism (odds ratio (OR) = 1.03, 95% CI: 1.00–1.05, P = 0.003). The optimal cutoffs for predicting long-term hypothyroidism were an LT4 dose of 68.75 μg/day (87% specificity, 42% sensitivity; P = 0.013) and a TSH level ≥ 3.8 mU/L (68.5% specificity, 77% sensitivity; P = 0.019). Conclusion: One-third of the patients who started on LT4 during pregnancy had long-term hypothyroidism. The TSH level at treatment initiation and the LT4 dose during pregnancy could guide the decision for continuing long-term LT4.

Published
2024
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21. Enhancements of Discretization Approaches for Non-Convex Mixed-Integer Quadratically Constraint Quadratic Programming: Part II

Author

Beach, Benjamin, Burlacu, Robert, Barmann, Andreas, Hager, Lukas, and Hildebrand, Robert

Subjects
Mathematics - Optimization and Control
Abstract

This is Part II of a study on mixed-integer programming (MIP) relaxation techniques for the solution of non-convex mixed-integer quadratically constrained quadratic programs (MIQCQPs). We set the focus on MIP relaxation methods for non-convex continuous variable products and extend the well-known MIP relaxation normalized multi-parametric disaggregation technique (NMDT), applying a sophisticated discretization to both variables. We refer to this approach as doubly discretized normalized multiparametric disaggregation technique (D-NMDT). In a comprehensive theoretical analysis, we underline the theoretical advantages of the enhanced method D-NMDT compared to NMDT. Furthermore, we perform a broad computational study to demonstrate its effectiveness in terms of producing tight dual bounds for MIQCQPs. Finally, we compare D-NMDT to the separable MIP relaxations from Part I and a state-of-the-art MIQCQP solver., Comment: arXiv admin note: substantial text overlap with arXiv:2211.00876

Published
2023

22. Why do investors buy shares of actively managed equity mutual funds? Considering the Correct Reference Portfolio from an Uninformed Investor's Perspective 1, 2

Author

Burlacu, Radu, Fontaine, Patrice, and Jimenez-Garcès, Sonia

Subjects
Quantitative Finance - General Finance, Quantitative Finance - Portfolio Management
Abstract

We use the Grossman \& Stiglitz (1980) framework to build a reference portfolio for uninformed investors and employ this portfolio to assess the performance of actively managed equity mutual funds. We propose an empirical methodology to construct this reference portfolio using the information on prices and supply. We show that mutual funds provide, on average, an insignificant alpha of 23 basis points per year when considering this portfolio as a reference. With the stock market index as a proxy for the market portfolio, the average fund alpha is negative and highly significant, --128 basis points per year. The results are robust when considering various subsets of funds based on their characteristics and their degree of selectivity. In line with rational expectations equilibrium models considering asymmetrically informed investors and partially revealing equilibrium prices, our study supports that active management adds value for uniformed investors., Comment: Finance, In press

Published
2022

23. The contribution of excessive or inappropriate speeds to road traffic crashes and fatalities: A review of literature

Author

Stephen Kome Fondzenyuy, Blair Matthew Turner, Alina Florentina Burlacu, and Chris Jurewicz

Subjects
Road traffic crashes, Road fatalities, Speeding, Crash causes, Low- and middle-income countries, High-income countries, Transportation engineering, TA1001-1280
Abstract

Road traffic crashes and fatalities pose a significant global challenge, particularly in low- and middle-income countries (LMICs), where a major cause is consistently linked to speeding (i.e., excessive or inappropriate speeds). However, the existing body of evidence regarding the estimated contribution of speeding to crashes and fatalities, remains limited and is considered outdated. This paper bridges this knowledge gap by reviewing evidence on the contribution of speeding to crashes and fatalities. The review draws on a wide range of sources including peer-reviewed studies on the subject, road safety monitoring-reports and available data summaries (104 sources). Data reliability was confirmed by including studies based on or linked to primary sources like police records, and excluding those with poor quality, implausible results, or lacked references to primary sources. The included sources contained 37 estimates from high-income countries (HICs), and 67 estimates from LMICs. Globally, HIC and LMIC estimates of contribution were calculated by assigning weights based on the proportion of fatalities in each country under this study. The results indicated that speeding contributes to approximately 54 % of fatalities worldwide, 57 % in LMICs, and 28 % in HICs. This translates to a speeding-related death every 49 s, with a 95 % likelihood of occurring in LMICs. These findings carry significant implications for policymakers emphasizing the urgent need to prioritize interventions that reduce speeding and improve road safety. Investigating gaps in LMICs data sources is a critical priority. In-depth studies and speeding intervention evaluations will enhance our current understanding of speeding contribution to crashes and fatalities.

Published
2024
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27. Enhancing the Computational Efficiency of Genetic Programming Through Alternative Floating-Point Primitives

Author

Crary, Christopher, Burlacu, Bogdan, Banzhaf, Wolfgang, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Affenzeller, Michael, editor, Winkler, Stephan M., editor, Kononova, Anna V., editor, Trautmann, Heike, editor, Tušar, Tea, editor, Machado, Penousal, editor, and Bäck, Thomas, editor

Published
2024
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28. An Evaluation of the Dual Vectors Based Solutions for Robot-World/Hand-Eye Calibration

Author

Ganciu, Aura, Burlacu, Adrian, Ceccarelli, Marco, Series Editor, Corves, Burkhard, Advisory Editor, Glazunov, Victor, Advisory Editor, Hernández, Alfonso, Advisory Editor, Huang, Tian, Advisory Editor, Jauregui Correa, Juan Carlos, Advisory Editor, Takeda, Yukio, Advisory Editor, Agrawal, Sunil K., Advisory Editor, Doroftei, Ioan, editor, Kiss, Balint, editor, Baudoin, Yvan, editor, Taqvi, Zafar, editor, and Keller Fuchter, Simone, editor

Published
2024
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29. Soft Robotics: A Numerical Evaluation of Model-Based PneuNet Simulation

Author

Brașoveanu, Florian-Alexandru, Burlacu, Adrian, Ceccarelli, Marco, Series Editor, Corves, Burkhard, Advisory Editor, Glazunov, Victor, Advisory Editor, Hernández, Alfonso, Advisory Editor, Huang, Tian, Advisory Editor, Jauregui Correa, Juan Carlos, Advisory Editor, Takeda, Yukio, Advisory Editor, Agrawal, Sunil K., Advisory Editor, Doroftei, Ioan, editor, Kiss, Balint, editor, Baudoin, Yvan, editor, Taqvi, Zafar, editor, and Keller Fuchter, Simone, editor

Published
2024
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30. Incorporating Eco-Friendly Materials in Wall Construction: Enhancing Thermal Performance and Sustainability

Author

Mihai, Radu Gabriel, Barbuta, Marinela, Burlacu, Andrei, Vizitiu, Ștefănica Eliza, Vizitiu, Robert Ștefan, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Moldovan, Liviu, editor, and Gligor, Adrian, editor

Published
2024
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31. PV Panel Emulator Based on Arduino and LabVIEW

Author

Ichim-Burlacu, Catalin, Rat, Cezara-Liliana, Cuntan, Corina, Rob, Raluca, Baciu, Ioan, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Ben Ahmed, Mohamed, editor, Boudhir, Anouar Abdelhakim, editor, El Meouche, Rani, editor, and Karaș, İsmail Rakıp, editor

Published
2024
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33. Nanostructured ZnO produced from ZnTe for random laser applications

Author

Zalamai, Victor, Burlacu, Alexandru, Postolache, Vitalie, Rusu, Emil, Ursachi, Veaceslav, and Tighineanu, Ion

Subjects
Physics, QC1-999, Electronics, TK7800-8360
Abstract

We propose to produce ZnO nanostructured material by thermal treatment in oxygen ambient of bulk ZnTe crystals and nanowires obtained by electrochemical treatment. Bulk ZnTe crystals are transformed into granular ZnO media by thermal treatment in a range of 700–800°C. This technology ensures a high optical quality of the produced ZnO nanostructured material to act as a gain medium for stimulated emission in the ultraviolet spectral region. The produced structures are expected to find applications in microlaser technologies for optoelectronics and photonics.

Published
2010

34. Enhancements of Discretization Approaches for Non-Convex Mixed-Integer Quadratically Constraint Quadratic Programming: Part I

Author

Beach, Benjamin, Burlacu, Robert, Bärmann, Andreas, Hager, Lukas, and Hildebrand, Robert

Subjects
Mathematics - Optimization and Control
Abstract

We study mixed-integer programming (MIP) relaxation techniques for the solution of non convex mixed-integer quadratically constrained quadratic programs (MIQCQPs). We present MIP relaxation methods for non convex continuous variable products. In Part I, we consider MIP relaxations based on separable reformulation. The main focus is the introduction of the enhanced separable MIP relaxation for nonconvex quadratic products of the form z=xy, called hybrid separable (HybS). Additionally, we introduce a logarithmic MIP relaxation for univariate quadratic terms, called sawtooth relaxation. We combine the latter with HybS and existing separable reformulations to derive MIP relaxations of MIQCQPs. We provide a comprehensive theoretical analysis of these techniques, underlining the theoretical advantages of HybS compared to its predecessors. We perform a broad computational study to demonstrate the effectiveness of the enhanced MIP relaxation in terms of producing tight dual bounds for MIQCQPs. In Part II, we study MIP relaxations that extend the well-known MIP relaxation normalized multiparametric disaggregation technique (NMDT) and present further theoretical and computational analyses.

Published
2022

35. On an eigenvalue problem associated with the (p, q) − Laplacian

Author

Barbu Luminiţa, Burlacu Andreea, and Moroşanu Gheorghe

Subjects
eigenvalues, (p, q)−laplacian, parametric boundary condition, nehari manifold, variational methods, primary 35j60, 35d30, secondary 35p30, Mathematics, QA1-939
Abstract

Let Ω ⊂ ℝN, N ≥ 2, be a bounded domain with smooth boundary ∂Ω. Consider the following generalized Robin-Steklov eigenvalue problem associated with the operator 𝒜u = − Δpu − Δqu {𝒜u+ρ1(x)|u|p-2u+ρ2(x)|u|q-2u=λα(x)|u|r-2u, x∈Ω,∂u∂vA+γ1(x)|u|p-2u+γ2(x)|u|q-2u=λβ(x)|u|r-2u, x∈∂Ω,\left\{ {\matrix{ {\mathcal{A}u + {\rho _1}\left( x \right){{\left| u \right|}^{p - 2}}u + {\rho _2}\left( x \right){{\left| u \right|}^{q - 2}}u = \lambda \alpha \left( x \right){{\left| u \right|}^{r - 2}}u,\,\,\,x \in \Omega ,} \cr {{{\partial u} \over {\partial {v_A}}} + {\gamma _1}\left( x \right){{\left| u \right|}^{p - 2}}u + {\gamma _2}\left( x \right){{\left| u \right|}^{q - 2}}u = \lambda \beta \left( x \right){{\left| u \right|}^{r - 2}}u,\,\,x \in \partial \Omega ,} \cr } } \right. where p, q, r ∈ (1, ∞), p < q; α, ρi ∈ L∞(Ω) and β, γi ∈ L∞(∂Ω) are nonnegative functions satisfying ∫Ω α dx + ∫∂Ω β dσ > 0 and ∫Ω ρi dx + ∫∂Ω γi dσ > 0, i = 1, 2.

Published
2024
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36. Experimental Investigations on Wear Phenomena Specific to Rotary Dryer Flights (Blades)

Author

Andrei Burlacu, Marius Gabriel Petrescu, Răzvan George Rîpeanu, Teodor Dumitru, Eugen Victor Laudacescu, Ibrahim Naim Ramadan, and Adrian Niță

Subjects
flights, baroid tribometer, wear granite roller, wear, Mechanical engineering and machinery, TJ1-1570
Abstract

The experimental research was carried out to study the tribological behaviour of materials used in the manufacture of rotary dryer blades (based on abrasive/abrasive-erosive wear analysis of the blades) using the Baroid tester. The test program included 2 series of tests consisting of subjecting the specimens to a wear process, generated by friction between their test surface and a rotating granite roller. Each set of tests was by specific parameters characterized: test medium, rotational speed of the granite roller, pressing force of the roller on the specimen surface, test time, number of test cycles. The test results highlight the erosion phenomenon (abrasive erosion) responsible for the damage of the rotary dryer blades and the conditions of accentuation of specific wear phenomena.

Published
2024
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37. Analysis of accruals earnings management using the Jones Model. The case of Romania listed companies

Author

Georgiana Burlacu

Subjects
earnings management, discretionary accruals, capital market, financial statement, Business, HF5001-6182, Accounting. Bookkeeping, HF5601-5689
Abstract

Research Question: To what extent earnings management operations are used by Romanian entities listed on a regulated market and what is the impact of these operations on the quality of reported financial information? Motivation: The financial statements reported by each entity are intended to provide useful information all the users, they are intended to describe clearly and honestly presents the financial position and performance and cash flows respectively. There are cases where, out of a desire to gain the trust of stakeholders (investors and other equity providers), some entities tend to commit manipulations in financial reporting using earnings management. Idea: The study aims to assess the extent to which the operations of earnings management are used by the Romanian entities listed on a regulated market and the impact of these operations on the quality assurance of the reported financial information. Data: This study was conducted based on the information collected from the annual financial statements of the entities that are listed on the Bucharest Stock Exchange, for the period between 2019-2021 Tools: The collected data were analyzed using the Jonse model based on the linear regression analysis. Findings: Using the Jones model based on the linear regression analysis, the results of the study indicate a distortion of the results presented by the analyzed companies, a solid reason being given by the negative effects of the Covid-19 pandemic with a strong impact on the economic environment. Contribution: This study provides an overview of the concept of earnings management based on information from the specialized literature. The analysis of earnings management practices was undertaken on the listed entities in Romania.

Published
2024
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38. Road Safety for All Road Users – Current Situation and Perspectives for 2030

Author

Alina Burlacu F., Adrian Burlacu, and Pop Horațiu

Subjects
road safety, safe roads, road crashes, road traffic, Transportation engineering, TA1001-1280
Abstract

This article presents a brief overview of Romania’s road infrastructure and road safety situation in general, and outlines perspectives for reducing the number of lives lost in road crashes by 2030. Romania’s road fatality rate per 100,000 population has improved overall from a 2008 high of around 15 to the current 2020 value of 8,5. However, despite a 30,8% reduction in road deaths during the past decade, Romania’s total annual number of road deaths has remained at an average of around 1890 fatalities per annum over this period, and compared to the EU average of 36,7%, Romania is positioned among the worst performing countries in the European Union (EU) in recent years in terms of road safety. A World Bank road safety management capacity review performed in 2016 found a series of factors to be contributing to this high number of road deaths, such as inadequate political commitment to effective actions to reduce road fatalities, fragmented government road safety activities across a number of regulatory entities, speed limits set at levels that exceed internationally accepted survivable limits, weak traffic law enforcement including a lack of speed enforcement cameras resulting in a failure of drivers to comply with speed limits, and a lack of structured programs to implement human error tolerant road infrastructure constructed according to Safe System principles. A series of recommendations from the capacity review were adopted since 2016, although much remains to improve road safety in Romania, especially in light of the Second Decade of Action for Road Safety 2021-2030 and the United Nations Sustainable Goals.

Published
2023
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39. A deterministic adjoint-based semi-analytical algorithm for fast response change computations in proton therapy

Author

Burlacu, Tiberiu, Lathouwers, Danny, and Perkó, Zoltán

Subjects
Physics - Medical Physics, Physics - Applied Physics, Physics - Computational Physics
Abstract

In this paper we propose a solution to the need for a fast particle transport algorithm in Online Adaptive Proton Therapy capable of cheaply, but accurately computing the changes in patient dose metrics as a result of changes in the system parameters. We obtain the proton phase-space density through the product of the numerical solution to the one-dimensional Fokker-Planck equation and the analytical solution to the Fermi-Eyges equation. Moreover, a corresponding adjoint system was derived and solved for the adjoint flux. The proton phase-space density together with the adjoint flux and the metric (chosen as the energy deposited by the beam in a variable region of interest) allowed assessing the accuracy of our algorithm to different perturbation ranges in the system parameters and regions of interest. The algorithm achieved negligible errors ($1.1 \times 10^{-6}$ $\%$ to $3.6 \times 10^{-3}$ $\%$) for small perturbation ranges ($-40 \text{ HU to } 40 \text{ HU}$) and small to moderate errors ($3 \text{ $\%$ to } 17 \text{ $\%$}$) -- in line with the well-known limitation of adjoint approaches -- for large perturbation ranges ($-400 \text{ HU to } 400 \text{ HU}$) in the case of most clinical interest where the region of interest surrounds the Bragg peak. Given these results coupled with the capability of further improving the timing performance it can be concluded that our algorithm presents a viable solution for the specific purpose of Online Adaptive Proton Therapy.

Published
2022

40. Gaia Data Release 3: Summary of the content and survey properties

Author

Gaia Collaboration, Vallenari, A., Brown, A. G. A., Prusti, T., de Bruijne, J. H. J., Arenou, F., Babusiaux, C., Biermann, M., Creevey, O. L., Ducourant, C., Evans, D. W., Eyer, L., Guerra, R., Hutton, A., Jordi, C., Klioner, S. A., Lammers, U. L., Lindegren, L., Luri, X., Mignard, F., Panem, C., Pourbaix, D., Randich, S., Sartoretti, P., Soubiran, C., Tanga, P., Walton, N. A., Bailer-Jones, C. A. L., Bastian, U., Drimmel, R., Jansen, F., Katz, D., Lattanzi, M. G., van Leeuwen, F., Bakker, J., Cacciari, C., Castañeda, J., De Angeli, F., Fabricius, C., Fouesneau, M., Frémat, Y., Galluccio, L., Guerrier, A., Heiter, U., Masana, E., Messineo, R., Mowlavi, N., Nicolas, C., Nienartowicz, K., Pailler, F., Panuzzo, P., Riclet, F., Roux, W., Seabroke, G. M., Sordoørcit, R., Thévenin, F., Gracia-Abril, G., Portell, J., Teyssier, D., Altmann, M., Andrae, R., Audard, M., Bellas-Velidis, I., Benson, K., Berthier, J., Blomme, R., Burgess, P. W., Busonero, D., Busso, G., Cánovas, H., Carry, B., Cellino, A., Cheek, N., Clementini, G., Damerdji, Y., Davidson, M., de Teodoro, P., Campos, M. Nuñez, Delchambre, L., Dell'Oro, A., Esquej, P., Fernández-Hernández, J., Fraile, E., Garabato, D., García-Lario, P., Gosset, E., Haigron, R., Halbwachs, J. -L., Hambly, N. C., Harrison, D. L., Hernández, J., Hestroffer, D., Hodgkin, S. T., Holl, B., Janßen, K., de Fombelle, G. Jevardat, Jordan, S., Krone-Martins, A., Lanzafame, A. C., Löffler, W., Marchal, O., Marrese, P. M., Moitinho, A., Muinonen, K., Osborne, P., Pancino, E., Pauwels, T., Recio-Blanco, A., Reylé, C., Riello, M., Rimoldini, L., Roegiers, T., Rybizki, J., Sarro, L. M., Siopis, C., Smith, M., Sozzetti, A., Utrilla, E., van Leeuwen, M., Abbas, U., Ábrahám, P., Aramburu, A. Abreu, Aerts, C., Aguado, J. J., Ajaj, M., Aldea-Montero, F., Altavilla, G., Álvarez, M. A., Alves, J., Anders, F., Anderson, R. I., Varela, E. Anglada, Antoja, T., Baines, D., Baker, S. G., Balaguer-Núñez, L., Balbinot, E., Balog, Z., Barache, C., Barbato, D., Barros, M., Barstow, M. A., Bartolomé, S., Bassilana, J. -L., Bauchet, N., Becciani, U., Bellazzini, M., Berihuete, A., Bernet, M., Bertone, S., Bianchi, L., Binnenfeld, A., Blanco-Cuaresma, S., Blazere, A., Boch, T., Bombrun, A., Bossini, D., Bouquillon, S., Bragaglia, A., Bramante, L., Breedt, E., Bressan, A., Brouillet, N., Brugaletta, E., Bucciarelli, B., Burlacu, A., Butkevich, A. G., Buzzi, R., Caffau, E., Cancelliere, R., Cantat-Gaudin, T., Carballo, R., Carlucci, T., Carnerero, M. I., Carrasco, J. M., Casamiquela, L., Castellani, M., Castro-Ginard, A., Chaoul, L., Charlot, P., Chemin, L., Chiaramida, V., Chiavassa, A., Chornay, N., Comoretto, G., Contursi, G., Cooper, W. J., Cornez, T., Cowell, S., Crifo, F., Cropper, M., Crosta, M., Crowley, C., Dafonte, C., Dapergolas, A., David, M., David, P., de Laverny, P., De Luise, F., De March, R., De Ridder, J., de Souza, R., de Torres, A., del Peloso, E. F., del Pozo, E., Delbo, M., Delgado, A., Delisle, J. -B., Demouchy, C., Dharmawardena, T. E., Di Matteo, P., Diakite, S., Diener, C., Distefano, E., Dolding, C., Edvardsson, B., Enke, H., Fabre, C., Fabrizio, M., Faigler, S., Fedorets, G., Fernique, P., Fienga, A., Figueras, F., Fournier, Y., Fouron, C., Fragkoudi, F., Gai, M., Garcia-Gutierrez, A., Garcia-Reinaldos, M., García-Torres, M., Garofalo, A., Gavel, A., Gavras, P., Gerlach, E., Geyer, R., Giacobbe, P., Gilmore, G., Girona, S., Giuffrida, G., Gomel, R., Gomez, A., González-Núñez, J., González-Santamaría, I., González-Vidal, J. J., Granvik, M., Guillout, P., Guiraud, J., Gutiérrez-Sánchez, R., Guy, L. P., Hatzidimitriou, D., Hauser, M., Haywood, M., Helmer, A., Helmi, A., Sarmiento, M. H., Hidalgo, S. L., Hilger, T., Hładczuk, N., Hobbs, D., Holland, G., Huckle, H. E., Jardine, K., Jasniewicz, G., Piccolo, A. Jean-Antoine, Jiménez-Arranz, Ó., Jorissen, A., Campillo, J. Juaristi, Julbe, F., Karbevska, L., Kervella, P., Khanna, S., Kontizas, M., Kordopatis, G., Korn, A. J., Kóspál, Á, Kostrzewa-Rutkowska, Z., Kruszyńska, K., Kun, M., Laizeau, P., Lambert, S., Lanza, A. F., Lasne, Y., Campion, J. -F. Le, Lebreton, Y., Lebzelter, T., Leccia, S., Leclerc, N., Lecoeur-Taibi, I., Liao, S., Licata, E. L., Lindstrøm, H. E. P., Lister, T. A., Livanou, E., Lobel, A., Lorca, A., Loup, C., Pardo, P. Madrero, Romeo, A. Magdaleno, Managau, S., Mann, R. G., Manteiga, M., Marchant, J. M., Marconi, M., Marcos, J., Santos, M. M. S. Marcos, Pina, D. Marín, Marinoni, S., Marocco, F., Marshall, D. J., Polo, L. Martin, Martín-Fleitas, J. M., Marton, G., Mary, N., Masip, A., Massari, D., Mastrobuono-Battisti, A., Mazeh, T., McMillan, P. J., Messina, S., Michalik, D., Millar, N. R., Mints, A., Molina, D., Molinaro, R., Molnár, L., Monari, G., Monguió, M., Montegriffo, P., Montero, A., Mor, R., Mora, A., Morbidelli, R., Morel, T., Morris, D., Muraveva, T., Murphy, C. P., Musella, I., Nagy, Z., Noval, L., Ocaña, F., Ogden, A., Ordenovic, C., Osinde, J. O., Pagani, C., Pagano, I., Palaversa, L., Palicio, P. A., Pallas-Quintela, L., Panahi, A., Payne-Wardenaar, S., Esteller, X. Peñalosa, Penttilä, A., Pichon, B., Piersimoni, A. M., Pineau, F. -X., Plachy, E., Plum, G., Poggio, E., Prša, A., Pulone, L., Racero, E., Ragaini, S., Rainer, M., Raiteri, C. M., Rambaux, N., Ramos, P., Ramos-Lerate, M., Fiorentin, P. Re, Regibo, S., Richards, P. J., Diaz, C. Rios, Ripepi, V., Riva, A., Rix, H. -W., Rixon, G., Robichon, N., Robin, A. C., Robin, C., Roelens, M., Rogues, H. R. O., Rohrbasser, L., Romero-Gómez, M., Rowell, N., Royer, F., Mieres, D. Ruz, Rybicki, K. A., Sadowski, G., Núñez, A. Sáez, Sellés, A. Sagristà, Sahlmann, J., Salguero, E., Samaras, N., Gimenez, V. Sanchez, Sanna, N., Santoveña, R., Sarasso, M., Schultheis, M., Sciacca, E., Segol, M., Segovia, J. C., Ségransan, D., Semeux, D., Shahaf, S., Siddiqui, H. I., Siebert, A., Siltala, L., Silvelo, A., Slezak, E., Slezak, I., Smart, R. L., Snaith, O. N., Solano, E., Solitro, F., Souami, D., Souchay, J., Spagna, A., Spina, L., Spoto, F., Steele, I. A., Steidelmüller, H., Stephenson, C. A., Süveges, M., Surdej, J., Szabados, L., Szegedi-Elek, E., Taris, F., Taylo, M. B., Teixeira, R., Tolomei, L., Tonello, N., Torra, F., Torra, J., Elipe, G. Torralba, Trabucchi, M., Tsounis, A. T., Turon, C., Ulla, A., Unger, N., Vaillant, M. V., van Dillen, E., van Reeven, W., Vanel, O., Vecchiato, A., Viala, Y., Vicente, D., Voutsinas, S., Weiler, M., Wevers, T., Wyrzykowski, L., Yoldas, A., Yvard, P., Zhao, H., Zorec, J., Zucker, S., and Zwitter, T.

Subjects
Astrophysics - Astrophysics of Galaxies
Abstract

We present the third data release of the European Space Agency's Gaia mission, GDR3. The GDR3 catalogue is the outcome of the processing of raw data collected with the Gaia instruments during the first 34 months of the mission by the Gaia Data Processing and Analysis Consortium. The GDR3 catalogue contains the same source list, celestial positions, proper motions, parallaxes, and broad band photometry in the G, G$_{BP}$, and G$_{RP}$ pass-bands already present in the Early Third Data Release. GDR3 introduces an impressive wealth of new data products. More than 33 million objects in the ranges $G_{rvs} < 14$ and $3100

Published
2022
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41. Gaia Data Release 3: Reflectance spectra of Solar System small bodies

Author

Gaia Collaboration, Galluccio, L., Delbo, M., De Angeli, F., Pauwels, T., Tanga, P., Mignard, F., Cellino, A., Brown, A. G. A., Muinonen, K., Penttila, A., Jordan, S., Vallenari, A., Prusti, T., de Bruijne, J. H. J., Arenou, F., Babusiaux, C., Biermann, M., Creevey, O. L., Ducourant, C., Evans, D. W., Eyer, L., Guerra, R., Hutton, A., Jordi, C., Klioner, S. A., Lammers, U. L., Lindegren, L., Luri, X., Panem, C., Pourbaix, D., Randich, S., Sartoretti, P., Soubiran, C., Walton, N. A., Bailer-Jones, C. A. L., Bastian, U., Drimmel, R., Jansen, F., Katz, D., Lattanzi, M. G., van Leeuwen, F., Bakker, J., Cacciari, C., Castaneda, J., Fabricius, C., Fouesneau, M., Frémat, Y., Guerrier, A., Heiter, U., Masana, E., Messineo, R., Mowlavi, N., Nicolas, C., Nienartowicz, K., Pailler, F., Panuzzo, P., Riclet, F., Roux, W., Seabroke, G. M., Sordo, R., Thévenin, F., Gracia-Abril, G., Portell, J., Teyssier, D., Altmann, M., Andrae, R., Audard, M., Bellas-Velidis, I., Benson, K., Berthier, J., Blomme, R., Burgess, P. W., Busonero, D., Busso, G., Cánovas, H., Carry, B., Cheek, N., Clementini, G., Damerdji, Y., Davidson, M., de Teodoro, P., Campos, M. Nunez, Delchambre, L., Oro, A. Dell, Esquej, P., Fernández-Hernández, J., Fraile, E., Garabato, D., García-Lario, P., Gosset, E., Haigron, R., Halbwachs, J. -L., Hambly, N. C., Harrison, D. L., Hernández, J., Hestroffer, D., Hodgkin, S. T., Holl, B., Janssen, K., de Fombelle, G. Jevardat, Krone-Martins, A., Lanzafame, A. C., Löffler, W., Marchal, O., Marrese, P. M., Moitinho, A., Osborne, P., Pancino, E., Recio-Blanco, A., Reylé, C., Riello, M., Rimoldini, L., Roegiers, T., Rybizki, J., Sarro, L. M., Siopis, C., Smith, M., Sozzetti, A., Utrilla, E., van Leeuwen, M., Abbas, U., Ábrahám, P., Aramburu, A. Abreu, Aerts, C., Aguado, J. J., Ajaj, M., Aldea-Montero, F., Altavilla, G., Álvarez, M. A., Alves, J., Anderson, R. I., Varela, E. Anglada, Antoja, T., Baines, D., Baker, S. G., Balaguer-Núnez, L., Balbinot, E., Balog, Z., Barache, C., Barbato, D., Barros, M., Barstow, M. A., Bartolomé, S., Bassilana, J. -L., Bauchet, N., Becciani, U., Bellazzini, M., Berihuete, A., Bernet, M., Bertone, S., Bianchi, L., Binnenfeld, A., Blanco-Cuaresma, S., Boch, T., Bombrun, A., Bossini, D., Bouquillon, S., Bragaglia, A., Bramante, L., Breedt, E., Bressan, A., Brouillet, N., Brugaletta, E., Bucciarelli, B., Burlacu, A., Butkevich, A. G., Buzzi, R., Caffau, E., Cancelliere, R., Cantat-Gaudin, T., Carballo, R., Carlucci, T., Carnerero, M. I., Carrasco, J. M., Casamiquela, L., Castellani, M., Castro-Ginard, A., Chaoul, L., Charlot, P., Chemin, L., Chiaramida, V., Chiavassa, A., Chornay, N., Comoretto, G., Contursi, G., Cooper, W. J., Cornez, T., Cowell, S., Crifo, F., Cropper, M., Crosta, M., Crowley, C., Dafonte, C., Dapergolas, A., David, P., de Laverny, P., De Luise, F., De March, R., De Ridder, J., de Souza, R., de Torres, A., del Peloso, E. F., del Pozo, E., Delgado, A., Delisle, J. -B., Demouchy, C., Dharmawardena, T. E., Diakite, S., Diener, C., Distefano, E., Dolding, C., Enke, H., Fabre, C., Fabrizio, M., Faigler, S., Fedorets, G., Fernique, P., Figueras, F., Fournier, Y., Fouron, C., Fragkoudi, F., Gai, M., Garcia-Gutierrez, A., Garcia-Reinaldos, M., García-Torres, M., Garofalo, A., Gavel, A., Gavras, P., Gerlach, E., Geyer, R., Giacobbe, P., Gilmore, G., Girona, S., Giuffrida, G., Gomel, R., Gomez, A., González-Núnez, J., González-Santamaría, I., González-Vidal, J. J., Granvik, M., Guillout, P., Guiraud, J., Gutiérrez-Sánchez, R., Guy, L. P., Hatzidimitriou, D., Hauser, M., Haywood, M., Helmer, A., Helmi, A., Sarmiento, M. H., Hidalgo, S. L., Hadczuk, N., Hobbs, D., Holland, G., Huckle, H. E., Jardine, K., Jasniewicz, G., Piccolo, A. Jean-Antoine, Jiménez-Arranz, Ó., Campillo, J. Juaristi, Julbe, F., Karbevska, L., Kervella, P., Khanna, S., Kordopatis, G., Korn, A. J., Kospál, A, Kostrzewa-Rutkowska, Z., Kruszynska, K., Kun, M., Laizeau, P., Lambert, S., Lanza, A. F., Lasne, Y., Campion, J. -F. Le, Lebreton, Y., Lebzelter, T., Leccia, S., Leclerc, N., Lecoeur-Taibi, I., Liao, S., Licata, E. L., Lindstrom, H. E. P., Lister, T. A., Livanou, E., Lobel, A., Lorca, A., Loup, C., Pardo, P. Madrero, Romeo, A. Magdaleno, Managau, S., Mann, R. G., Manteiga, M., Marchant, J. M., Marconi, M., Marcos, J., Santos, M. M. S. Marcos, Pina, D. Marín, Marinoni, S., Marocco, F., Marshall, D. J., Polo, L. Martin, Martín-Fleitas, J. M., Marton, G., Mary, N., Masip, A., Massari, D., Mastrobuono-Battisti, A., Mazeh, T., McMillan, P. J., Messina, S., Michalik, D., Millar, N. R., Mints, A., Molina, D., Molinaro, R., Molnár, L., Monari, G., Monguió, M., Montegriffo, P., Montero, A., Mor, R., Mora, A., Morbidelli, R., Morel, T., Morris, D., Muraveva, T., Murphy, C. P., Musella, I., Nagy, Z., Noval, L., Ocana, F., Ogden, A., Ordenovic, C., Osinde, J. O., Pagani, C., Pagano, I., Palaversa, L., Palicio, P. A., Pallas-Quintela, L., Panahi, A., Payne-Wardenaar, S., Esteller, X. Penalosa, Petit, J. -M., Pichon, B., Piersimoni, A. M., Pineau, F. -X., Plachy, E., Plum, G., Poggio, E., Prsa, A., Pulone, L., Racero, E., Ragaini, S., Rainer, M., Raiteri, C. M., Ramos, P., Ramos-Lerate, M., Fiorentin, P. Re, Regibo, S., Richards, P. J., Diaz, C. Rios, Ripepi, V., Riva, A., Rix, H. -W., Rixon, G., Robichon, N., Robin, A. C., Robin, C., Roelens, M., Rogues, H. R. O., Rohrbasser, L., Romero-Gómez, M., Rowell, N., Royer, F., Mieres, D. Ruz, Rybicki, K. A., Sadowski, G., Núnez, A. Sáez, Sellés, A. Sagristà, Sahlmann, J., Salguero, E., Samaras, N., Gimenez, V. Sanchez, Sanna, N., Santovena, R., Sarasso, M., Schultheis, M., Sciacca, E., Segol, M., Segovia, J. C., Ségransan, D., Semeux, D., Shahaf, S., Siddiqui, H. I., Siebert, A., Siltala, L., Silvelo, A., Slezak, E., Slezak, I., Smart, R. L., Snaith, O. N., Solano, E., Solitro, F., Souami, D., Souchay, J., Spagna, A., Spina, L., Spoto, F., Steele, I. A., Steidelmüller, H., Stephenson, C. A., Süveges, M., Surdej, J., Szabados, L., Szegedi-Elek, E., Taris, F., Taylor, M. B., Teixeira, R., Tolomei, L., Tonello, N., Torra, F., Torra, J., Elipe, G. Torralba, Trabucchi, M., Tsounis, A. T., Turon, C., Ulla, A., Unger, N., Vaillant, M. V., van Dillen, E., van Reeven, W., Vanel, O., Vecchiato, A., Viala, Y., Vicente, D., Voutsinas, S., Weiler, M., Wevers, T., Wyrzykowski, L., Yoldas, A., Yvard, P., Zhao, H., Zorec, J., Zucker, S., and Zwitter, T.

Subjects
Astrophysics - Earth and Planetary Astrophysics
Abstract

The Gaia mission of the European Space Agency (ESA) has been routinely observing Solar System objects (SSOs) since the beginning of its operations in August 2014. The Gaia data release three (DR3) includes, for the first time, the mean reflectance spectra of a selected sample of 60 518 SSOs, primarily asteroids, observed between August 5, 2014, and May 28, 2017. Each reflectance spectrum was derived from measurements obtained by means of the Blue and Red photometers (BP/RP), which were binned in 16 discrete wavelength bands. We describe the processing of the Gaia spectral data of SSOs, explaining both the criteria used to select the subset of asteroid spectra published in Gaia DR3, and the different steps of our internal validation procedures. In order to further assess the quality of Gaia SSO reflectance spectra, we carried out external validation against SSO reflectance spectra obtained from ground-based and space-borne telescopes and available in the literature. For each selected SSO, an epoch reflectance was computed by dividing the calibrated spectrum observed by the BP/RP at each transit on the focal plane by the mean spectrum of a solar analogue. The latter was obtained by averaging the Gaia spectral measurements of a selected sample of stars known to have very similar spectra to that of the Sun. Finally, a mean of the epoch reflectance spectra was calculated in 16 spectral bands for each SSO. The agreement between Gaia mean reflectance spectra and those available in the literature is good for bright SSOs, regardless of their taxonomic spectral class. We identify an increase in the spectral slope of S-type SSOs with increasing phase angle. Moreover, we show that the spectral slope increases and the depth of the 1 um absorption band decreases for increasing ages of S-type asteroid families., Comment: 30 pages, 26 figures

Published
2022
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42. Gaia DR3: Apsis III -- Non-stellar content and source classification

Author

Delchambre, L., Bailer-Jones, C. A. L., Bellas-Velidis, I., Drimmel, R., Garabato, D., Carballo, R., Hatzidimitriou, D., Marshall, D. J., Andrae, R., Dafonte, C., Livanou, E., Fouesneau, M., Licata, E. L., Lindstrom, H. E. P., Manteiga, M., Robin, C., Silvelo, A., Aramburu, A. Abreu, Alvarez, M. A., Bakker, J., Bijaoui, A., Brouillet, N., Brugaletta, E., Burlacu, A., Casamiquela, L., Chaoul, L., Chiavassa, A., Contursi, G., Cooper, W. J., Creevey, O. L., Dapergolas, A., de Laverny, P., Demouchy, C., Dharmawardena, T. E., Edvardsson, B., Fremat, Y., Garcia-Lario, P., Garcia-Torres, M., Gavel, A., Gomez, A., Gonzalez-Santamaria, I., Heiter, U., Piccolo, A. Jean-Antoine, Kontizas, M., Kordopatis, G., Korn, A. J., Lanzafame, A. C., Lebreton, Y., Lobel, A., Lorca, A., Romeo, A. Magdaleno, Marocco, F., Mary, N., Nicolas, C., Ordenovic, C., Pailler, F., Palicio, P. A., Pallas-Quintela, L., Panem, C., Pichon, B., Poggio, E., Recio-Blanco, A., Riclet, F., Rybizki, J., Santovena, R., Sarro, L. M., Schultheis, M. S., Segol, M., Slezak, I., Smart, R. L., Sordo, R., Soubiran, C., Suveges, M., Thevenin, F., Elipe, G. Torralba, Ulla, A., Utrilla, E., Vallenari, A., van Dillen, E., Zhao, H., and Zorec, J.

Subjects
Astrophysics - Astrophysics of Galaxies, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

Context. As part of the third Gaia data release, we present the contributions of the non-stellar and classification modules from the eighth coordination unit (CU8) of the Data Processing and Analysis Consortium, which is responsible for the determination of source astrophysical parameters using Gaia data. This is the third in a series of three papers describing the work done within CU8 for this release. Aims. For each of the five relevant modules from CU8, we summarise their objectives, the methods they employ, their performance, and the results they produce for Gaia DR3. We further advise how to use these data products and highlight some limitations. Methods. The Discrete Source Classifier (DSC) module provides classification probabilities associated with five types of sources: quasars, galaxies, stars, white dwarfs, and physical binary stars. A subset of these sources are processed by the Outlier Analysis (OA) module, which performs an unsupervised clustering analysis, and then associates labels with the clusters to complement the DSC classification. The Quasi Stellar Object Classifier (QSOC) and the Unresolved Galaxy Classifier (UGC) determine the redshifts of the sources classified as quasar and galaxy by the DSC module. Finally, the Total Galactic Extinction (TGE) module uses the extinctions of individual stars determined by another CU8 module to determine the asymptotic extinction along all lines of sight for Galactic latitudes |b| > 5 deg. Results. Gaia DR3 includes 1591 million sources with DSC classifications; 56 million sources to which the OA clustering is applied; 1.4 million sources with redshift estimates from UGC; 6.4 million sources with QSOC redshift; and 3.1 million level 9 HEALPixes of size 0.013 squared degree, where the extinction is evaluated by TGE., Comment: Accepted by A&A. 36 pages, 29 figures, 9 tables

Published
2022
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43. Symbolic Regression in Materials Science: Discovering Interatomic Potentials from Data

Author

Burlacu, Bogdan, Kommenda, Michael, Kronberger, Gabriel, Winkler, Stephan, and Affenzeller, Michael

Subjects
Condensed Matter - Materials Science, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing
Abstract

Particle-based modeling of materials at atomic scale plays an important role in the development of new materials and understanding of their properties. The accuracy of particle simulations is determined by interatomic potentials, which allow to calculate the potential energy of an atomic system as a function of atomic coordinates and potentially other properties. First-principles-based ab initio potentials can reach arbitrary levels of accuracy, however their aplicability is limited by their high computational cost. Machine learning (ML) has recently emerged as an effective way to offset the high computational costs of ab initio atomic potentials by replacing expensive models with highly efficient surrogates trained on electronic structure data. Among a plethora of current methods, symbolic regression (SR) is gaining traction as a powerful "white-box" approach for discovering functional forms of interatomic potentials. This contribution discusses the role of symbolic regression in Materials Science (MS) and offers a comprehensive overview of current methodological challenges and state-of-the-art results. A genetic programming-based approach for modeling atomic potentials from raw data (consisting of snapshots of atomic positions and associated potential energy) is presented and empirically validated on ab initio electronic structure data., Comment: Submitted to the GPTP XIX Workshop, June 2-4 2022, University of Michigan, Ann Arbor, Michigan

Published
2022
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44. Gaia Data Release 3: Mapping the asymmetric disc of the Milky Way

Author

Gaia Collaboration, Drimmel, R., Romero-Gomez, M., Chemin, L., Ramos, P., Poggio, E., Ripepi, V., Andrae, R., Blomme, R., Cantat-Gaudin, T., Castro-Ginard, A., Clementini, G., Figueras, F., Fouesneau, M., Fremat, Y., Jardine, K., Khanna, S., Lobel, A., Marshall, D. J., Muraveva, T., Brown, A. G. A., Vallenari, A., Prusti, T., de Bruijne, J. H. J., Arenou, F., Babusiaux, C., Biermann, M., Creevey, O. L., Ducourant, C., Evans, D. W., Eyer, L., Guerra, R., Hutton, A., Jordi, C., Klioner, S. A., Lammers, U. L., Lindegren, L., Luri, X., Mignard, F., Panem, C., Pourbaix, D., Randich, S., Sartoretti, P., Soubiran, C., Tanga, P., Walton, N. A., Bailer-Jones, C. A. L., Bastian, U., Jansen, F., Katz, D., Lattanzi, M. G., van Leeuwen, F., Bakker, J., Cacciari, C., Castañeda, J., De Angeli, F., Fabricius, C., Galluccio, L., Guerrier, A., Heiter, U., Masana, E., Messineo, R., Mowlavi, N., Nicolas, C., Nienartowicz, K., Pailler, F., Panuzzo, P., Riclet, F., Roux, W., Seabroke, G. M., Sordoørcit, R., Thévenin, F., Gracia-Abril, G., Portell, J., Teyssier, D., Altmann, M., Audard, M., Bellas-Velidis, I., Benson, K., Berthier, J., Burgess, P. W., Busonero, D., Busso, G., Cánovas, H., Carry, B., Cellino, A., Cheek, N., Damerdji, Y., Davidson, M., de Teodoro, P., Campos, M. Nuñez, Delchambre, L., Dell'Oro, A., Esquej, P., Fernández-Hernández, J., Fraile, E., Garabato, D., García-Lario, P., Gosset, E., Haigron, R., Halbwachs, J. -L., Hambly, N. C., Harrison, D. L., Hernández, J., Hestroffer, D., Hodgkin, S. T., Holl, B., Janßen, K., de Fombelle, G. Jevardat, Jordan, S., Krone-Martins, A., Lanzafame, A. C., Löffler, W., Marchal, O., Marrese, P. M., Moitinho, A., Muinonen, K., Osborne, P., Pancino, E., Pauwels, T., Recio-Blanco, A., Reylé, C., Riello, M., Rimoldini, L., Roegiers, T., Rybizki, J., Sarro, L. M., Siopis, C., Smith, M., Sozzetti, A., Utrilla, E., van Leeuwen, M., Abbas, U., Ábrahám, P., Aramburu, A. Abreu, Aerts, C., Aguado, J. J., Ajaj, M., Aldea-Montero, F., Altavilla, G., Álvarez, M. A., Alves, J., Anders, F., Anderson, R. I., Varela, E. Anglada, Antoja, T., Baines, D., Baker, S. G., Balaguer-Núñez, L., Balbinot, E., Balog, Z., Barache, C., Barbato, D., Barros, M., Barstow, M. A., Bartolomé, S., Bassilana, J. -L., Bauchet, N., Becciani, U., Bellazzini, M., Berihuete, A., Bernet, M., Bertone, S., Bianchi, L., Binnenfeld, A., Blanco-Cuaresma, S., Blazere, A., Boch, T., Bombrun, A., Bossini, D., Bouquillon, S., Bragaglia, A., Bramante, L., Breedt, E., Bressan, A., Brouillet, N., Brugaletta, E., Bucciarelli, B., Burlacu, A., Butkevich, A. G., Buzzi, R., Caffau, E., Cancelliere, R., Carballo, R., Carlucci, T., Carnerero, M. I., Carrasco, J. M., Casamiquela, L., Castellani, M., Chaoul, L., Charlot, P., Chiaramida, V., Chiavassa, A., Chornay, N., Comoretto, G., Contursi, G., Cooper, W. J., Cornez, T., Cowell, S., Crifo, F., Cropper, M., Crosta, M., Crowley, C., Dafonte, C., Dapergolas, A., David, M., David, P., de Laverny, P., De Luise, F., De March, R., De Ridder, J., de Souza, R., de Torres, A., del Peloso, E. F., del Pozo, E., Delbo, M., Delgado, A., Delisle, J. -B., Demouchy, C., Dharmawardena, T. E., Di Matteo, P., Diakite, S., Diener, C., Distefano, E., Dolding, C., Edvardsson, B., Enke, H., Fabre, C., Fabrizio, M., Faigler, S., Fedorets, G., Fernique, P., Fienga, A., Fournier, Y., Fouron, C., Fragkoudi, F., Gai, M., Garcia-Gutierrez, A., Garcia-Reinaldos, M., García-Torres, M., Garofalo, A., Gavel, A., Gavras, P., Gerlach, E., Geyer, R., Giacobbe, P., Gilmore, G., Girona, S., Giuffrida, G., Gomel, R., Gomez, A., González-Núñez, J., González-Santamaría, I., González-Vidal, J. J., Granvik, M., Guillout, P., Guiraud, J., Gutiérrez-Sánchez, R., Guy, L. P., Hatzidimitriou, D., Hauser, M., Haywood, M., Helmer, A., Helmi, A., Sarmiento, M. H., Hidalgo, S. L., Hilger, T., Hładczuk, N., Hobbs, D., Holland, G., Huckle, H. E., Jasniewicz, G., Piccolo, A. Jean-Antoine, Jiménez-Arranz, Ó., Jorissen, A., Campillo, J. Juaristi, Julbe, F., Karbevska, L., Kervella, P., Kontizas, M., Kordopatis, G., Korn, A. J., Kóspál, Á, Kostrzewa-Rutkowska, Z., Kruszyńska, K., Kun, M., Laizeau, P., Lambert, S., Lanza, A. F., Lasne, Y., Campion, J. -F. Le, Lebreton, Y., Lebzelter, T., Leccia, S., Leclerc, N., Lecoeur-Taibi, I., Liao, S., Licata, E. L., Lindstrøm, H. E. P., Lister, T. A., Livanou, E., Lorca, A., Loup, C., Pardo, P. Madrero, Romeo, A. Magdaleno, Managau, S., Mann, R. G., Manteiga, M., Marchant, J. M., Marconi, M., Marcos, J., Santos, M. M. S. Marcos, Pina, D. Marín, Marinoni, S., Marocco, F., Polo, L. Martin, Martín-Fleitas, J. M., Marton, G., Mary, N., Masip, A., Massari, D., Mastrobuono-Battisti, A., Mazeh, T., McMillan, P. J., Messina, S., Michalik, D., Millar, N. R., Mints, A., Molina, D., Molinaro, R., Molnár, L., Monari, G., Monguió, M., Montegriffo, P., Montero, A., Mor, R., Mora, A., Morbidelli, R., Morel, T., Morris, D., Murphy, C. P., Musella, I., Nagy, Z., Noval, L., Ocaña, F., Ogden, A., Ordenovic, C., Osinde, J. O., Pagani, C., Pagano, I., Palaversa, L., Palicio, P. A., Pallas-Quintela, L., Panahi, A., Payne-Wardenaar, S., Esteller, X. Peñalosa, Penttilä, A., Pichon, B., Piersimoni, A. M., Pineau, F. -X., Plachy, E., Plum, G., Prša, A., Pulone, L., Racero, E., Ragaini, S., Rainer, M., Raiteri, C. M., Rambaux, N., Ramos-Lerate, M., Fiorentin, P. Re, Regibo, S., Richards, P. J., Diaz, C. Rios, Riva, A., Rix, H. -W., Rixon, G., Robichon, N., Robin, A. C., Robin, C., Roelens, M., Rogues, H. R. O., Rohrbasser, L., Rowell, N., Royer, F., Mieres, D. Ruz, Rybicki, K. A., Sadowski, G., Núñez, A. Sáez, Sellés, A. Sagristà, Sahlmann, J., Salguero, E., Samaras, N., Gimenez, V. Sanchez, Sanna, N., Santoveña, R., Sarasso, M., Schultheis, M., Sciacca, E., Segol, M., Segovia, J. C., Ségransan, D., Semeux, D., Shahaf, S., Siddiqui, H. I., Siebert, A., Siltala, L., Silvelo, A., Slezak, E., Slezak, I., Smart, R. L., Snaith, O. N., Solano, E., Solitro, F., Souami, D., Souchay, J., Spagna, A., Spina, L., Spoto, F., Steele, I. A., Steidelmüller, H., Stephenson, C. A., Süveges, M., Surdej, J., Szabados, L., Szegedi-Elek, E., Taris, F., Taylo, M. B., Teixeira, R., Tolomei, L., Tonello, N., Torra, F., Torra, J., Elipe, G. Torralba, Trabucchi, M., Tsounis, A. T., Turon, C., Ulla, A., Unger, N., Vaillant, M. V., van Dillen, E., van Reeven, W., Vanel, O., Vecchiato, A., Viala, Y., Vicente, D., Voutsinas, S., Weiler, M., Wevers, T., Wyrzykowski, L., Yoldas, A., Yvard, P., Zhao, H., Zorec, J., Zucker, S., and Zwitter, T.

Subjects
Astrophysics - Astrophysics of Galaxies
Abstract

With the most recent Gaia data release the number of sources with complete 6D phase space information (position and velocity) has increased to well over 33 million stars, while stellar astrophysical parameters are provided for more than 470 million sources, in addition to the identification of over 11 million variable stars. Using the astrophysical parameters and variability classifications provided in Gaia DR3, we select various stellar populations to explore and identify non-axisymmetric features in the disc of the Milky Way in both configuration and velocity space. Using more about 580 thousand sources identified as hot OB stars, together with 988 known open clusters younger than 100 million years, we map the spiral structure associated with star formation 4-5 kpc from the Sun. We select over 2800 Classical Cepheids younger than 200 million years, which show spiral features extending as far as 10 kpc from the Sun in the outer disc. We also identify more than 8.7 million sources on the red giant branch (RGB), of which 5.7 million have line-of-sight velocities, allowing the velocity field of the Milky Way to be mapped as far as 8 kpc from the Sun, including the inner disc. The spiral structure revealed by the young populations is consistent with recent results using Gaia EDR3 astrometry and source lists based on near infrared photometry, showing the Local (Orion) arm to be at least 8 kpc long, and an outer arm consistent with what is seen in HI surveys, which seems to be a continuation of the Perseus arm into the third quadrant. Meanwhile, the subset of RGB stars with velocities clearly reveals the large scale kinematic signature of the bar in the inner disc, as well as evidence of streaming motions in the outer disc that might be associated with spiral arms or bar resonances. (abridged), Comment: 35 pages, 27 figures, accepted for publication in A&A special Gaia DR3 issue. V2: abstract completed. V3: complete author list and link to data: https://drive.google.com/drive/u/1/folders/1yOJPjYmM7QK5XVsqaiSOTuwDQNti2LlZ

Published
2022
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45. Gaia Data Release 3: Analysis of the Gaia BP/RP spectra using the General Stellar Parameterizer from Photometry

Author

Andrae, R., Fouesneau, M., Sordo, R., Bailer-Jones, C. A. L., Dharmawardena, T. E., Rybizki, J., De Angeli, F., Lindstrøm, H. E. P., Marshall, D. J., Drimmel, R., Korn, A. J., Soubiran, C., Brouillet, N., Casamiquela, L., Rix, H. -W., Aramburu, A. Abreu, Álvarez, M. A., Bakker, J., Bellas-Velidis, I., Bijaoui, A., Brugaletta, E., Burlacu, A., Carballo, R., Chaoul, L., Chiavassa, A., Contursi, G., Cooper, W. J., Creevey, O. L., Dafonte, C., Dapergolas, A., de Laverny, P., Delchambre, L., Demouchy, C., Edvardsson, B., Frémat, Y., Garabato, D., García-Lario, P., García-Torres, M., Gavel, A., Gomez, A., González-Santamaría, I., Hatzidimitriou, D., Heiter, U., Piccolo, A. Jean-Antoine, Kontizas, M., Kordopatis, G., Lanzafame, A. C., Lebreton, Y., Licata, E. L., Livanou, E., Lobel, A., Lorca, A., Romeo, A. Magdaleno, Manteiga, M., Marocco, F., Mary, N., Nicolas, C., Ordenovic, C., Pailler, F., Palicio, P. A., Pallas-Quintela, L., Panem, C., Pichon, B., Poggio, E., Recio-Blanco, A., Riclet, F., Robin, C., Santoveña, R., Sarro, L. M., Schultheis, M. S., Segol, M., Silvelo, A., Slezak, I., Smart, R. L., Süveges, M., Thévenin, F., Elipe, G. Torralba, Ulla, A., Utrilla, E., Vallenari, A., van Dillen, E., Zhao, H., and Zorec, J.

Subjects
Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Astrophysics of Galaxies
Abstract

We present the General Stellar Parameterizer from Photometry (GSP-Phot), which is part of the astrophysical parameters inference system (Apsis). GSP-Phot is designed to produce a homogeneous catalogue of parameters for hundreds of millions of single non-variable stars based on their astrometry, photometry, and low-resolution BP/RP spectra. These parameters are effective temperature, surface gravity, metallicity, absolute $M_G$ magnitude, radius, distance, and extinction for each star. GSP-Phot uses a Bayesian forward-modelling approach to simultaneously fit the BP/RP spectrum, parallax, and apparent $G$ magnitude. A major design feature of GSP-Phot is the use of the apparent flux levels of BP/RP spectra to derive, in combination with isochrone models, tight observational constraints on radii and distances. We carefully validate the uncertainty estimates by exploiting repeat Gaia observations of the same source. The data release includes GSP-Phot results for 471 million sources with $G<19$. Typical differences to literature values are 110 K for $T_{\rm eff}$ and 0.2-0.25 for $\log g$, but these depend strongly on data quality. In particular, GSP-Phot results are significantly better for stars with good parallax measurements ($\varpi/\sigma_varpi>20$), mostly within 2kpc. Metallicity estimates exhibit substantial biases compared to literature values and are only useful at a qualitative level. However, we provide an empirical calibration of our metallicity estimates that largely removes these biases. Extinctions $A_0$ and $A_{\rm BP}$ show typical differences from reference values of 0.07-0.09 mag. MCMC samples of the parameters are also available for 95% of the sources. GSP-Phot provides a homogeneous catalogue of stellar parameters, distances, and extinctions that can be used for various purposes, such as sample selections (OB stars, red giants, solar analogues etc.)., Comment: 23 pages, 19 figures

Published
2022
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46. Gaia Data Release 3: Pulsations in main sequence OBAF-type stars

Author

Gaia Collaboration, De Ridder, J., Ripepi, V., Aerts, C., Palaversa, L., Eyer, L., Holl, B., Audard, M., Rimoldini, L., Brown, A. G. A., Vallenari, A., Prusti, T., de Bruijne, J. H. J., Arenou, F., Babusiaux, C., Biermann, M., Creevey, O. L., Ducourant, C., Evans, D. W., Guerra, R., Hutton, A., Jordi, C., Klioner, S. A., Lammers, U. L., Lindegren, L., Luri, X., Mignard, F., Panem, C., Pourbaix, D., Randich, S., Sartoretti, P., Soubiran, C., Tanga, P., Walton, N. A., Bailer-Jones, C. A. L., Bastian, U., Drimmel, R., Jansen, F., Katz, D., Lattanzi, M. G., van Leeuwen, F., Bakker, J., Cacciari, C., Castañeda, J., De Angeli, F., Fabricius, C., Fouesneau, M., Frémat, Y., Galluccio, L., Guerrier, A., Heiter, U., Masana, E., Messineo, R., Mowlavi, N., Nicolas, C., Nienartowicz, K., Pailler, F., Panuzzo, P., Riclet, F., Roux, W., Seabroke, G. M., Sordo, R., Thévenin, F., Gracia-Abril, G., Portell, J., Teyssier, D., Altmann, M., Andrae, R., Bellas-Velidis, I., Benson, K., Berthier, J., Blomme, R., Burgess, P. W., Busonero, D., Busso, G., Cánovas, H., Carry, B., Cellino, A., Cheek, N., Clementini, G., Damerdji, Y., Davidson, M., de Teodoro, P., Campos, M. Nuñez, Delchambre, L., Dell'Oro, A., Esquej, P., Fernández-Hernández, J., Fraile, E., Garabato, D., García-Lario, P., Gosset, E., Haigron, R., Halbwachs, J. -L., Hambly, N. C., Harrison, D. L., Hernández, J., Hestroffer, D., Hilger, T., Hodgkin, S. T., Janßen, K., de Fombelle, G. Jevardat, Jordan, S., Krone-Martins, A., Lanzafame, A. C., Löffler, W., Marchal, O., Marrese, P. M., Moitinho, A., Muinonen, K., Osborne, P., Pancino, E., Pauwels, T., Recio-Blanco, A., Reylé, C., Riello, M., Roegiers, T., Rybizki, J., Sarro, L. M., Siopis, C., Smith, M., Sozzetti, A., Utrilla, E., van Leeuwen, M., Abbas, U., Ábrahám, P., Aramburu, A. Abreu, Aguado, J. J., Ajaj, M., Aldea-Montero, F., Altavilla, G., Álvarez, M. A., Alves, J., Anders, F., Anderson, R. I., Varela, E. Anglada, Antoja, T., Baines, D., Baker, S. G., Balaguer-Núñez, L., Balbinot, E., Balog, Z., Barache, C., Barbato, D., Barros, M., Barstow, M. A., Bartolomé, S., Bassilana, J. -L., Bauchet, N., Becciani, U., Bellazzini, M., Berihuete, A., Bernet, M., Bertone, S., Bianchi, L., Binnenfeld, A., Blanco-Cuaresma, S., Boch, T., Bombrun, A., Bossini, D., Bouquillon, S., Bragaglia, A., Bramante, L., Breedt, E., Bressan, A., Brouillet, N., Brugaletta, E., Bucciarelli, B., Burlacu, A., Butkevich, A. G., Buzzi, R., Caffau, E., Cancelliere, R., Cantat-Gaudin, T., Carballo, R., Carlucci, T., Carnerero, M. I., Carrasco, J. M., Casamiquela, L., Castellani, M., Castro-Ginard, A., Chaoul, L., Charlot, P., Chemin, L., Chiaramida, V., Chiavassa, A., Chornay, N., Comoretto, G., Contursi, G., Cooper, W. J., Cornez, T., Cowell, S., Crifo, F., Cropper, M., Crosta, M., Crowley, C., Dafonte, C., Dapergolas, A., David, P., de Laverny, P., De Luise, F., De March, R., de Souza, R., de Torres, A., del Peloso, E. F., del Pozo, E., Delbo, M., Delgado, A., Delisle, J. -B., Demouchy, C., Dharmawardena, T. E., Diakite, S., Diener, C., Distefano, E., Dolding, C., Enke, H., Fabre, C., Fabrizio, M., Faigler, S., Fedorets, G., Fernique, P., Figueras, F., Fournier, Y., Fouron, C., Fragkoudi, F., Gai, M., Garcia-Gutierrez, A., Garcia-Reinaldos, M., García-Torres, M., Garofalo, A., Gavel, A., Gavras, P., Gerlach, E., Geyer, R., Giacobbe, P., Gilmore, G., Girona, S., Giuffrida, G., Gomel, R., Gomez, A., González-Núñez, J., González-Santamaría, I., González-Vidal, J. J., Granvik, M., Guillout, P., Guiraud, J., Gutiérrez-Sánchez, R., Guy, L. P., Hatzidimitriou, D., Hauser, M., Haywood, M., Helmer, A., Helmi, A., Sarmiento, M. H., Hidalgo, S. L., Hładczuk, N., Hobbs, D., Holland, G., Huckle, H. E., Jardine, K., Jasniewicz, G., Piccolo, A. Jean-Antoine, Jiménez-Arranz, Ó., Campillo, J. Juaristi, Julbe, F., Karbevska, L., Kervella, P., Khanna, S., Kordopatis, G., Korn, A. J., Kóspál, Á, Kostrzewa-Rutkowska, Z., Kruszyńska, K., Kun, M., Laizeau, P., Lambert, S., Lanza, A. F., Lasne, Y., Campion, J. -F. Le, Lebreton, Y., Lebzelter, T., Leccia, S., Leclerc, N., Lecoeur-Taibi, I., Liao, S., Licata, E. L., Lindstrøm, H. E. P., Lister, T. A., Livanou, E., Lobel, A., Lorca, A., Loup, C., Pardo, P. Madrero, Romeo, A. Magdaleno, Managau, S., Mann, R. G., Manteiga, M., Marchant, J. M., Marconi, M., Marcos, J., Santos, M. M. S. Marcos, Pina, D. Marín, Marinoni, S., Marocco, F., Marshall, D. J., Polo, L. Martin, Martín-Fleitas, J. M., Marton, G., Mary, N., Masip, A., Massari, D., Mastrobuono-Battisti, A., Mazeh, T., McMillan, P. J., Messina, S., Michalik, D., Millar, N. R., Mints, A., Molina, D., Molinaro, R., Molnár, L., Monari, G., Monguió, M., Montegriffo, P., Montero, A., Mor, R., Mora, A., Morbidelli, R., Morel, T., Morris, D., Muraveva, T., Murphy, C. P., Musella, I., Nagy, Z., Noval, L., Ocaña, F., Ogden, A., Ordenovic, C., Osinde, J. O., Pagani, C., Pagano, I., Palicio, P. A., Pallas-Quintela, L., Panahi, A., Payne-Wardenaar, S., Esteller, X. Peñalosa, Penttilä, A., Pichon, B., Piersimoni, A. M., Pineau, F. -X., Plachy, E., Plum, G., Poggio, E., Prša, A., Pulone, L., Racero, E., Ragaini, S., Rainer, M., Raiteri, C. M., Ramos, P., Ramos-Lerate, M., Fiorentin, P. Re, Regibo, S., Richards, P. J., Diaz, C. Rios, Riva, A., Rix, H. -W., Rixon, G., Robichon, N., Robin, A. C., Robin, C., Roelens, M., Rogues, H. R. O., Rohrbasser, L., Romero-Gómez, M., Rowell, N., Royer, F., Mieres, D. Ruz, Rybicki, K. A., Sadowski, G., Núñez, A. Sáez, Sellés, A. Sagristà, Sahlmann, J., Salguero, E., Samaras, N., Gimenez, V. Sanchez, Sanna, N., Santoveña, R., Sarasso, M., Schultheis, M., Sciacca, E., Segol, M., Segovia, J. C., Ségransan, D., Semeux, D., Shahaf, S., Siddiqui, H. I., Siebert, A., Siltala, L., Silvelo, A., Slezak, E., Slezak, I., Smart, R. L., Snaith, O. N., Solano, E., Solitro, F., Souami, D., Souchay, J., Spagna, A., Spina, L., Spoto, F., Steele, I. A., Steidelmüller, H., Stephenson, C. A., Süveges, M., Surdej, J., Szabados, L., Szegedi-Elek, E., Taris, F., Taylor, M. B., Teixeira, R., Tolomei, L., Tonello, N., Torra, F., Torra, J., Elipe, G. Torralba, Trabucchi, M., Tsounis, A. T., Turon, C., Ulla, A., Unger, N., Vaillant, M. V., van Dillen, E., van Reeven, W., Vanel, O., Vecchiato, A., Viala, Y., Vicente, D., Voutsinas, S., Weiler, M., Wevers, T., Wyrzykowski, Ł., Yoldas, A., Yvard, P., Zhao, H., Zorec, J., Zucker, S., and Zwitter, T.

Subjects
Astrophysics - Solar and Stellar Astrophysics
Abstract

The third Gaia data release provides photometric time series covering 34 months for about 10 million stars. For many of those stars, a characterisation in Fourier space and their variability classification are also provided. This paper focuses on intermediate- to high-mass (IHM) main sequence pulsators M >= 1.3 Msun) of spectral types O, B, A, or F, known as beta Cep, slowly pulsating B (SPB), delta Sct, and gamma Dor stars. These stars are often multi-periodic and display low amplitudes, making them challenging targets to analyse with sparse time series. All datasets used in this analysis are part of the Gaia DR3 data release. The photometric time series were used to perform a Fourier analysis, while the global astrophysical parameters necessary for the empirical instability strips were taken from the Gaia DR3 gspphot tables, and the vsini data were taken from the Gaia DR3 esphs tables. We show that for nearby OBAF-type pulsators, the Gaia DR3 data are precise and accurate enough to pinpoint them in the Hertzsprung-Russell diagram. We find empirical instability strips covering broader regions than theoretically predicted. In particular, our study reveals the presence of fast rotating gravity-mode pulsators outside the strips, as well as the co-existence of rotationally modulated variables inside the strips as reported before in the literature. We derive an extensive period-luminosity relation for delta Sct stars and provide evidence that the relation features different regimes depending on the oscillation period. Finally, we demonstrate how stellar rotation attenuates the amplitude of the dominant oscillation mode of delta Sct stars.

Published
2022
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47. Gaia Data Release 3: Apsis II -- Stellar Parameters

Author

Fouesneau, M., Frémat, Y., Andrae, R., Korn, A. J., Soubiran, C., Kordopatis, G., Vallenari, A., Heiter, U., Creevey, O. L., Sarro, L. M., de Laverny, P., Lanzafame, A. C., Lobel, A., Sordo, R., Rybizki, J., Slezak, I., Álvarez, M. A., Drimmel, R., Garabato, D., Delchambre, L., Bailer-Jones, C. A. L., Hatzidimitriou, D., Lorca, A., Fustec, Y. Le, Pailler, F., Mary, N., Robin, C., Utrilla, E., Aramburu, A. Abreu, Bakker, J., Bellas-Velidis, I., Bijaoui, A., Blomme, R., Bouret, J. -C., Brouillet, N., Brugaletta, E., Burlacu, A., Carballo, R., Casamiquela, L., Chaoul, L., Chiavassa, A., Contursi, G., Cooper, W. J., Dafonte, C., Demouchy, C., Dharmawardena, T. E., García-Lario, P., García-Torres, M., Gomez, A., González-Santamaría, I., Piccolo, A. Jean-Antoine, Kontizas, M., Lebreton, Y., Licata, E. L., Lindstrøm, H. E. P., Livanou, E., Romeo, A. Magdaleno, Manteiga, M., Marocco, F., Martayan, C., Marshall, D. J., Nicolas, C., Ordenovic, C., Palicio, P. A., Pallas-Quintela, L., Pichon, B., Poggio, E., Recio-Blanco, A., Riclet, F., Santoveña, R., Schultheis, M. S., Segol, M., Silvelo, A., Smart, R. L., Süveges, M., Thévenin, F., Elipe, G. Torralba, Ulla, A., van Dillen, E., Zhao, H., and Zorec, J.

Subjects
Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Astrophysics of Galaxies
Abstract

The third Gaia data release contains, beyond the astrometry and photometry, dispersed light for hundreds of millions of sources from the Gaia prism spectra (BP and RP) and the spectrograph (RVS). This data release opens a new window on the chemo-dynamical properties of stars in our Galaxy, essential knowledge for understanding the structure, formation, and evolution of the Milky Way. To provide insight into the physical properties of Milky Way stars, we used these data to produce a uniformly-derived, all-sky catalog of stellar astrophysical parameters (APs): Teff, logg, [M/H], [$\alpha$/Fe], activity index, emission lines, rotation, 13 chemical abundance estimates, radius, age, mass, bolometric luminosity, distance, and dust extinction. We developed the Apsis pipeline to infer APs of Gaia objects by analyzing their astrometry, photometry, BP/RP, and RVS spectra. We validate our results against other literature works, including benchmark stars, interferometry, and asteroseismology. Here we assessed the stellar analysis performance from Apsis statistically. We describe the quantities we obtained, including our results' underlying assumptions and limitations. We provide guidance and identify regimes in which our parameters should and should not be used. Despite some limitations, this is the most extensive catalog of uniformly-inferred stellar parameters to date. These comprise Teff, logg, and [M/H] (470 million using BP/RP, 6 million using RVS), radius (470 million), mass (140 million), age (120 million), chemical abundances (5 million), diffuse interstellar band analysis (1/2 million), activity indices (2 million), H{$\alpha$} equivalent widths (200 million), and further classification of spectral types (220 million) and emission-line stars (50 thousand). More precise and detailed astrophysical parameters based on epoch BP, RP, and RVS are planned for the next Gaia data release., Comment: Gaia DR3 paper, 37 pages, 38 figures, catalog is available from the Gaia Archive and partner data centers; Accepted for publication in A&A

Published
2022
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48. Gaia Data Release 3: A Golden Sample of Astrophysical Parameters

Author

Gaia Collaboration, Creevey, O. L., Sarro, L. M., Lobel, A., Pancino, E., Andrae, R., Smart, R. L., Clementini, G., Heiter, U., Korn, A. J., Fouesneau, M., Frémat, Y., De Angeli, F., Vallenari, A., Harrison, D. L., Thévenin, F., Reylé, C., Sordo, R., Garofalo, A., Brown, A. G. A., Eyer, L., Prusti, T., de Bruijne, J. H. J., Arenou, F., Babusiaux, C., Biermann, M., Ducourant, C., Evans, D. W., Guerra, R., Hutton, A., Jordi, C., Klioner, S. A., Lammers, U. L., Lindegren, L., Luri, X., Mignard, F., Panem, C., Pourbaix, D., Randich, S., Sartoretti, P., Soubiran, C., Tanga, P., Walton, N. A., Bailer-Jones, C. A. L., Bastian, U., Drimmel, R., Jansen, F., Katz, D., Lattanzi, M. G., van Leeuwen, F., Bakker, J., Cacciari, C., Castañeda, J., Fabricius, C., Galluccio, L., Guerrier, A., Masana, E., Messineo, R., Mowlavi, N., Nicolas, C., Nienartowicz, K., Pailler, F., Panuzzo, P., Riclet, F., Roux, W., Seabroke, G. M., Gracia-Abril, G., Portell, J., Teyssier, D., Altmann, M., Audard, M., Bellas-Velidis, I., Benson, K., Berthier, J., Blomme, R., Burgess, P. W., Busonero, D., Busso, G., Cánovas, H., Carry, B., Cellino, A., Cheek, N., Damerdji, Y., Davidson, M., de Teodoro, P., Campos, M. Nuñez, Delchambre, L., Dell'Oro, A., Esquej, P., Fernández-Hernández, J., Fraile, E., Garabato, D., García-Lario, P., Gosset, E., Haigron, R., Halbwachs, J. -L., Hambly, N. C., Hernández, J., Hestroffer, D., Hodgkin, S. T., Holl, B., Janßen, K., de Fombelle, G. Jevardat, Jordan, S., Krone-Martins, A., Lanzafame, A. C., Löffler, W., Marchal, O., Marrese, P. M., Moitinho, A., Muinonen, K., Osborne, P., Pauwels, T., Recio-Blanco, A., Riello, M., Rimoldini, L., Roegiers, T., Rybizki, J., Siopis, C., Smith, M., Sozzetti, A., Utrilla, E., van Leeuwen, M., Abbas, U., Ábrahám, P., Aramburu, A. Abreu, Aerts, C., Aguado, J. J., Ajaj, M., Aldea-Montero, F., Altavilla, G., Álvarez, M. A., Alves, J., Anders, F., Anderson, R. I., Varela, E. Anglada, Antoja, T., Baines, D., Baker, S. G., Balaguer-Núñez, L., Balbinot, E., Balog, Z., Barache, C., Barbato, D., Barros, M., Barstow, M. A., Bartolomé, S., Bassilana, J. -L., Bauchet, N., Becciani, U., Bellazzini, M., Berihuete, A., Bernet, M., Bertone, S., Bianchi, L., Binnenfeld, A., Blanco-Cuaresma, S., Boch, T., Bombrun, A., Bossini, D., Bouquillon, S., Bragaglia, A., Bramante, L., Breedt, E., Bressan, A., Brouillet, N., Brugaletta, E., Bucciarelli, B., Burlacu, A., Butkevich, A. G., Buzzi, R., Caffau, E., Cancelliere, R., Cantat-Gaudin, T., Carballo, R., Carlucci, T., Carnerero, M. I., Carrasco, J. M., Casamiquela, L., Castellani, M., Castro-Ginard, A., Chaoul, L., Charlot, P., Chemin, L., Chiaramida, V., Chiavassa, A., Chornay, N., Comoretto, G., Contursi, G., Cooper, W. J., Cornez, T., Cowell, S., Crifo, F., Cropper, M., Crosta, M., Crowley, C., Dafonte, C., Dapergolas, A., David, P., de Laverny, P., De Luise, F., De March, R., De Ridder, J., de Souza, R., de Torres, A., del Peloso, E. F., del Pozo, E., Delbo, M., Delgado, A., Delisle, J. -B., Demouchy, C., Dharmawardena, T. E., Di Matteo, P., Diakite, S., Diener, C., Distefano, E., Dolding, C., Enke, H., Fabre, C., Fabrizio, M., Faigler, S., Fedorets, G., Fernique, P., Figueras, F., Fournier, Y., Fouron, C., Fragkoudi, F., Gai, M., Garcia-Gutierrez, A., Garcia-Reinaldos, M., García-Torres, M., Gavel, A., Gavras, P., Gerlach, E., Geyer, R., Giacobbe, P., Gilmore, G., Girona, S., Giuffrida, G., Gomel, R., Gomez, A., González-Núñez, J., González-Santamaría, I., González-Vidal, J. J., Granvik, M., Guillout, P., Guiraud, J., Gutiérrez-Sánchez, R., Guy, L. P., Hatzidimitriou, D., Hauser, M., Haywood, M., Helmer, A., Helmi, A., Sarmiento, M. H., Hidalgo, S. L., Hładczuk, N., Hobbs, D., Holland, G., Huckle, H. E., Jardine, K., Jasniewicz, G., Piccolo, A. Jean-Antoine, Jiménez-Arranz, Ó., Campillo, J. Juaristi, Julbe, F., Karbevska, L., Kervella, P., Khanna, S., Kordopatis, G., Kóspál, Á, Kostrzewa-Rutkowska, Z., Kruszyńska, K., Kun, M., Laizeau, P., Lambert, S., Lanza, A. F., Lasne, Y., Campion, J. -F. Le, Lebreton, Y., Lebzelter, T., Leccia, S., Leclerc, N., Lecoeur-Taibi, I., Liao, S., Licata, E. L., Lindstrøm, H. E. P., Lister, T. A., Livanou, E., Lorca, A., Loup, C., Pardo, P. Madrero, Romeo, A. Magdaleno, Managau, S., Mann, R. G., Manteiga, M., Marchant, J. M., Marconi, M., Marcos, J., Santos, M. M. S. Marcos, Pina, D. Marín, Marinoni, S., Marocco, F., Marshall, D. J., Polo, L. Martin, Martín-Fleitas, J. M., Marton, G., Mary, N., Masip, A., Massari, D., Mastrobuono-Battisti, A., Mazeh, T., McMillan, P. J., Messina, S., Michalik, D., Millar, N. R., Mints, A., Molina, D., Molinaro, R., Molnár, L., Monari, G., Monguió, M., Montegriffo, P., Montero, A., Mor, R., Mora, A., Morbidelli, R., Morel, T., Morris, D., Muraveva, T., Murphy, C. P., Musella, I., Nagy, Z., Noval, L., Ocaña, F., Ogden, A., Ordenovic, C., Osinde, J. O., Pagani, C., Pagano, I., Palaversa, L., Palicio, P. A., Pallas-Quintela, L., Panahi, A., Payne-Wardenaar, S., Esteller, X. Peñalosa, Penttilä, A., Pichon, B., Piersimoni, A. M., Pineau, F. -X., Plachy, E., Plum, G., Poggio, E., Prša, A., Pulone, L., Racero, E., Ragaini, S., Rainer, M., Raiteri, C. M., Ramos, P., Ramos-Lerate, M., Fiorentin, P. Re, Regibo, S., Richards, P. J., Diaz, C. Rios, Ripepi, V., Riva, A., Rix, H. -W., Rixon, G., Robichon, N., Robin, A. C., Robin, C., Roelens, M., Rogues, H. R. O., Rohrbasser, L., Romero-Gómez, M., Rowell, N., Royer, F., Mieres, D. Ruz, Rybicki, K. A., Sadowski, G., Núñez, A. Sáez, Sellés, A. Sagristà, Sahlmann, J., Salguero, E., Samaras, N., Gimenez, V. Sanchez, Sanna, N., Santoveña, R., Sarasso, M., Schultheis, M., Sciacca, E., Segol, M., Segovia, J. C., Ségransan, D., Semeux, D., Shahaf, S., Siddiqui, H. I., Siebert, A., Siltala, L., Silvelo, A., Slezak, E., Slezak, I., Snaith, O. N., Solano, E., Solitro, F., Souami, D., Souchay, J., Spagna, A., Spina, L., Spoto, F., Steele, I. A., Steidelmüller, H., Stephenson, C. A., Süveges, M., Surdej, J., Szabados, L., Szegedi-Elek, E., Taris, F., Taylor, M. B., Teixeira, R., Tolomei, L., Tonello, N., Torra, F., Torra, J., Elipe, G. Torralba, Trabucchi, M., Tsounis, A. T., Turon, C., Ulla, A., Unger, N., Vaillant, M. V., van Dillen, E., van Reeven, W., Vanel, O., Vecchiato, A., Viala, Y., Vicente, D., Voutsinas, S., Weiler, M., Wevers, T., Wyrzykowski, Ł., Yoldas, A., Yvard, P., Zhao, H., Zorec, J., Zucker, S., and Zwitter, T.

Subjects
Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Astrophysics of Galaxies, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

Gaia Data Release 3 (DR3) provides a wealth of new data products for the astronomical community to exploit, including astrophysical parameters for a half billion stars. In this work we demonstrate the high quality of these data products and illustrate their use in different astrophysical contexts. We query the astrophysical parameter tables along with other tables in Gaia DR3 to derive the samples of the stars of interest. We validate our results by using the Gaia catalogue itself and by comparison with external data. We have produced six homogeneous samples of stars with high quality astrophysical parameters across the HR diagram for the community to exploit. We first focus on three samples that span a large parameter space: young massive disk stars (~3M), FGKM spectral type stars (~3M), and UCDs (~20K). We provide these sources along with additional information (either a flag or complementary parameters) as tables that are made available in the Gaia archive. We furthermore identify 15740 bone fide carbon stars, 5863 solar-analogues, and provide the first homogeneous set of stellar parameters of the Spectro Photometric Standard Stars. We use a subset of the OBA sample to illustrate its usefulness to analyse the Milky Way rotation curve. We then use the properties of the FGKM stars to analyse known exoplanet systems. We also analyse the ages of some unseen UCD-companions to the FGKM stars. We additionally predict the colours of the Sun in various passbands (Gaia, 2MASS, WISE) using the solar-analogue sample., Comment: 35 pages, (incl 6 pages references, acknowledgements, affiliations), 37 figures, A&A accepted

Published
2022
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49. Gaia Data Release 3: Astrophysical parameters inference system (Apsis) I -- methods and content overview

Author

Creevey, O. L., Sordo, R., Pailler, F., Frémat, Y., Heiter, U., Thévenin, F., Andrae, R., Fouesneau, M., Lobel, A., Bailer-Jones, C. A. L., Garabato, D., Bellas-Velidis, I., Brugaletta, E., Lorca, A., Ordenovic, C., Palicio, P. A., Sarro, L. M., Delchambre, L., Drimmel, R., Rybizki, J., Elipe, G. Torralba, Korn, A. J., Recio-Blanco, A., Schultheis, M. S., De Angeli, F., Montegriffo, P., Aramburu, A. Abreu, Accart, S., Álvarez, M. A., Bakker, J., Brouillet, N., Burlacu, A., Carballo, R., Casamiquela, L., Chiavassa, A., Contursi, G., Cooper, W. J., Dafonte, C., Dapergolas, A., de Laverny, P., Dharmawardena, T. E., Edvardsson, B., Fustec, Y. Le, García-Lario, P., García-Torres, M., Gomez, A., González-Santamaría, I., Hatzidimitriou, D., Piccolo, A. Jean-Antoine, Kontizas, M., Kordopatis, G., Lanzafame, A. C., Lebreton, Y., Licata, E. L., Lindstrøm, H. E. P., Livanou, E., Romeo, A. Magdaleno, Manteiga, M., Marocco, F., Marshall, D. J., Mary, N., Nicolas, C., Pallas-Quintela, L., Panem, C., Pichon, B., Poggio, E., Riclet, F., Robin, C., Santoveña, R., Silvelo, A., Slezak, I., Smart, R. L., Soubiran, C., Süveges, M., Ulla, A., Utrilla, E., Vallenari, A., Zhao, H., Zorec, J., Barrado, D., Bijaoui, A., Bouret, J. -C., Blomme, R., Brott, I., Cassisi, S., Kochukhov, O., Martayan, C., Shulyak, D., and Silvester, J.

Subjects
Astrophysics - Astrophysics of Galaxies, Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Solar and Stellar Astrophysics
Abstract

Gaia Data Release 3 contains a wealth of new data products for the community. Astrophysical parameters are a major component of this release. They were produced by the Astrophysical parameters inference system (Apsis) within the Gaia Data Processing and Analysis Consortium. The aim of this paper is to describe the overall content of the astrophysical parameters in Gaia Data Release 3 and how they were produced. In Apsis we use the mean BP/RP and mean RVS spectra along with astrometry and photometry, and we derive the following parameters: source classification and probabilities for 1.6 billion objects, interstellar medium characterisation and distances for up to 470 million sources, including a 2D total Galactic extinction map, 6 million redshifts of quasar candidates and 1.4 million redshifts of galaxy candidates, along with an analysis of 50 million outlier sources through an unsupervised classification. The astrophysical parameters also include many stellar spectroscopic and evolutionary parameters for up to 470 million sources. These comprise Teff, logg, and m_h (470 million using BP/RP, 6 million using RVS), radius (470 million), mass (140 million), age (120 million), chemical abundances (up to 5 million), diffuse interstellar band analysis (0.5 million), activity indices (2 million), H-alpha equivalent widths (200 million), and further classification of spectral types (220 million) and emission-line stars (50 thousand). This catalogue is the most extensive homogeneous database of astrophysical parameters to date, and it is based uniquely on Gaia data., Comment: 35 pages (incl 7 pages references, appendix, affiliations, acknowledgements), 29 figures, A&A, accepted

Published
2022
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50. Gaia Data Release 3. Stellar chromospheric activity and mass accretion from Ca II IRT observed by the Radial Velocity Spectrometer

Author

Lanzafame, A. C., Brugaletta, E., Frémat, Y., Sordo, R., Creevey, O. L., Andretta, V., Scandariato, G., Busà, I., Distefano, E., Korn, A. J., de Laverny, P., Recio-Blanco, A., Aramburu, A. Abreu, Álvarez, M. A., Andrae, R., Bailer-Jones, C. A. L., Bakker, J., Bellas-Velidis, I., Bijaoui, A., Brouillet, N., Burlacu, A., Carballo, R., Casamiquela, L., Chaoul, L., Chiavassa, A., Contursi, G., Cooper, W. J., Dafonte, C., Dapergolas, A., Delchambre, L., Demouchy, C., Dharmawardena, T. E., Drimmel, R., Edvardsson, B., Fouesneau, M., Garabato, D., García-Lario, P., García-Torres, M., Gavel, A., Gomez, A., González-Santamaría, I., Hatzidimitriou, D., Heiter, U., Piccolo, A. Jean-Antoine, Kontizas, M., Kordopatis, G., Lebreton, Y., Licata, E. L., Lindstrøm, H. E. P., Livanou, E., Lobel, A., Lorca, A., Romeo, A. Magdaleno, Manteiga, M., Marocco, F., Marshall, D. J., Mary, N., Nicolas, C., Ordenovic, C., Pailler, F., Palicio, P. A., Pallas-Quintela, L., Panem, C., Pichon, B., Poggio, E., Riclet, F., Robin, C., Rybizki, J., Santoveña, R., Sarro, L. M., Schultheis, M. S., Segol, M., Silvelo, A., Slezak, I., Smart, R. L., Soubiran, C., Süveges, M., Thévenin, F., Elipe, G. Torralba, Ulla, A., Utrilla, E., Vallenari, A., van Dillen, E., Zhao, H., and Zorec, J.

Subjects
Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Astrophysics of Galaxies
Abstract

The Gaia Radial Velocity Spectrometer provides the unique opportunity of a spectroscopic analysis of millions of stars at medium-resolution in the near-infrared. This wavelength range includes the Ca II infrared triplet (IRT), which is a good diagnostics of magnetic activity in the chromosphere of late-type stars. Here we present the method devised for inferring the Gaia stellar activity index together with its scientific validation. A sample of well studied PMS stars is considered to identify the regime in which the Gaia stellar activity index may be affected by mass accretion. The position of these stars in the colour-magnitude diagram and the correlation with the amplitude of the photometric rotational modulation is also scrutinised. Three regimes of the chromospheric stellar activity are identified, confirming suggestions made by previous authors on much smaller $R'_{\rm HK}$ datasets. The highest stellar activity regime is associated with PMS stars and RS CVn systems, in which activity is enhanced by tidal interaction. Some evidence of a bimodal distribution in MS stars with $T_{\rm eff}\ge$ 5000 K is also found, which defines the two other regimes, without a clear gap in between. Stars with 3500 K$\le T_{\rm eff} \le$ 5000 K are found to be either very active PMS stars or active MS stars with a unimodal distribution in chromospheric activity. A dramatic change in the activity distribution is found for $T_{\rm eff}\le$3500 K, with a dominance of low activity stars close to the transition between partially- and fully-convective stars and a rise in activity down into the fully-convective regime., Comment: 18 pages, 10 figures, 2 tables, submitted to A&A

Published
2022
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