Long gamma-ray bursts as binary-driven hypernovae - analysis within the induced gravitational collapse paradigm

The central engine of long gamma-ray bursts (GRBs) is still under debate. The (currently) prevailing theoretical understanding is referred to as the standard fireball model. Here, the prompt emission is attributed to the internal shocks and the afterglow emission is attributed to the external shocks. The GRB outflow contains a wide range of bulk Lorentz factors. When a fast-moving portion overtakes the slower one, an internal shock is generated. On the other hand, the external shocks are caused by the interaction between the outflow and the circum-burst medium. However, data that was accumulated in the last 25 years challenges the overall picture. Some of the observed properties can not be explained within the standard framework. For example, the immense isotropic energy requirements of GRBs can be considerably reduced if one assumes the outflow is collimated. As a consequence, an achromatic break should appear in the afterglow light-curves. However, for the majority of GRBs the break is not achromatic, if present at all. In addition, the model itself does not deal with the exact mechanism of this initial energy release, but only its consequences. space{0.5cm} One of the alternatives to the fireball model is the fireshell model. Its origins can be traced back to the idea which revolves around the energy extraction from a charged black hole. During the years, with the implementation of new available data, the fireshell model evolved into the induced gravitational collapse (IGC) paradigm. This theory emphasizes the importance of binary system interaction for the GRB production mechanism, offering additional channels to study the role these systems have in GRB formation. In it, all GRBs originate from binary systems. Different observational properties are a direct consequence of a wide spectrum of acceptable binary system parameters. According to these observational properties, long and short GRBs are divided into nine different sub-classes. GRBs belonging to the type-I binary driven hypernova (BdHNe-I) class are of main interest in this thesis. The name is referring to GRBs with energies above $sim 10^{52}; m erg$ that originate from a collapse of a neutron star into a black hole. This collapse is initiated by the supernova explosion of its binary companion. space{0.5cm} In the work presented here, the theoretical framework of the IGC paradigm was tested on twelve GRBs. From these, only GRB 160829A is a member of the short GRB class. The remaining ones are long bursts, classified as BdHNe-I on the account of their energetics and GeV emission. Two main tools were put to use in order to analyze and interpret the data: the erb|rmfit| software and the simulation of the fireshell propagation that is available on our group's server. All of the GRBs were detected with the GBM detector onboard the extit{Fermi} satellite. Time-integrated and time-resolved analysis was carried out for each GRB in order to determine their isotropic equivalent energy and to possibly identify the P-GRB signature. The latter is expected to occur in the beginning of the prompt emission and to have a spectrum that shows a presence of thermal signatures. From 11 BdHNe-I, five had an identified P-GRB associated emission: GRB 100728A, GRB151027A, GRB 090618, GRB 110731A and GRB 141028A. A black body component was found in six GRBs. For three of them, the component did not exhibit the expected P-GRB spectral and temporal properties and it was therefore rejected as a possible P-GRB. GRB 110731A, GRB151027A and GRB 090618 were further interpreted within the fireshell model. Average values of circum-burst medium density inferred from the simulations are $sim0.03; m cm ^{-3}$, $sim15; m cm ^{-3}$ and $sim1; m cm ^{-3}$, respectively. Therefore, these bursts occurred in different environments. The averaged value of this sample, $sim$1 baryon per $; m cm ^{3}$, is consistent with previous findings. Baryon load and the relativistic Lorentz gamma factor at transparency point were also consistent with long GRBs, although we find that GRB 110731A shared some of these values with short bursts. In the case of short GRB 160829A, the fireshell simulation up to the transparency point was used in order to evaluate the redshift. Poor S/N ratio constrained its redshift to z<5. This is not particularly helpful considering it is true for all of the short GRBs observed so far. That is, if one does not take into account GRB 080913 at $z=6.7$, which was observed to last longer than $2; m s$ due to its high redshift, but it may be intrinsically short. The difficulties encountered during the analyses played a role in the further development of the IGC paradigm. The ongoing work is also discussed. It was devised with a goal to enable a more consistent and faster analysis. Then, a more complete BdHNe-I catalog with all of the fireshell parameters included would be easier to produce

What can we learn from GRBs?

We review our recent results on the classification of long and short gamma-ray bursts (GRBs) in different subclasses. We provide observational evidences for the binary nature of GRB progenitors. For long bursts the induced gravitational collapse (IGC) paradigm proposes as progenitor a tight binary system composed of a carbon-oxygen core (COcore) and a neutron star (NS) companion; the supernova (SN) explosion of the COcore triggers a hypercritical accretion process onto the companion NS. For short bursts a NS–NS merger is traditionally adopted as the progenitor. We also indicate additional sub-classes originating from different progenitors: (COcore)–black hole (BH), BH–NS, and white dwarf–NS binaries. We also show how the outcomes of the further evolution of some of these sub-classes may become the progenitor systems of other sub-classes

Genomic analyses inform on migration events during the peopling of Eurasia.

High-coverage whole-genome sequence studies have so far focused on a limited number of geographically restricted populations, or been targeted at specific diseases, such as cancer. Nevertheless, the availability of high-resolution genomic data has led to the development of new methodologies for inferring population history and refuelled the debate on the mutation rate in humans. Here we present the Estonian Biocentre Human Genome Diversity Panel (EGDP), a dataset of 483 high-coverage human genomes from 148 populations worldwide, including 379 new genomes from 125 populations, which we group into diversity and selection sets. We analyse this dataset to refine estimates of continent-wide patterns of heterozygosity, long- and short-distance gene flow, archaic admixture, and changes in effective population size through time as well as for signals of positive or balancing selection. We find a genetic signature in present-day Papuans that suggests that at least 2% of their genome originates from an early and largely extinct expansion of anatomically modern humans (AMHs) out of Africa. Together with evidence from the western Asian fossil record, and admixture between AMHs and Neanderthals predating the main Eurasian expansion, our results contribute to the mounting evidence for the presence of AMHs out of Africa earlier than 75,000 years ago.Support was provided by: Estonian Research Infrastructure Roadmap grant no 3.2.0304.11-0312; Australian Research Council Discovery grants (DP110102635 and DP140101405) (D.M.L., M.W. and E.W.); Danish National Research Foundation; the Lundbeck Foundation and KU2016 (E.W.); ERC Starting Investigator grant (FP7 - 261213) (T.K.); Estonian Research Council grant PUT766 (G.C. and M.K.); EU European Regional Development Fund through the Centre of Excellence in Genomics to Estonian Biocentre (R.V.; M.Me. and A.Me.), and Centre of Excellence for Genomics and Translational Medicine Project No. 2014-2020.4.01.15-0012 to EGC of UT (A.Me.) and EBC (M.Me.); Estonian Institutional Research grant IUT24-1 (L.S., M.J., A.K., B.Y., K.T., C.B.M., Le.S., H.Sa., S.L., D.M.B., E.M., R.V., G.H., M.K., G.C., T.K. and M.Me.) and IUT20-60 (A.Me.); French Ministry of Foreign and European Affairs and French ANR grant number ANR-14-CE31-0013-01 (F.-X.R.); Gates Cambridge Trust Funding (E.J.); ICG SB RAS (No. VI.58.1.1) (D.V.L.); Leverhulme Programme grant no. RP2011-R-045 (A.B.M., P.G. and M.G.T.); Ministry of Education and Science of Russia; Project 6.656.2014/K (S.A.F.); NEFREX grant funded by the European Union (People Marie Curie Actions; International Research Staff Exchange Scheme; call FP7-PEOPLE-2012-IRSES-number 318979) (M.Me., G.H. and M.K.); NIH grants 5DP1ES022577 05, 1R01DK104339-01, and 1R01GM113657-01 (S.Tis.); Russian Foundation for Basic Research (grant N 14-06-00180a) (M.G.); Russian Foundation for Basic Research; grant 16-04-00890 (O.B. and E.B); Russian Science Foundation grant 14-14-00827 (O.B.); The Russian Foundation for Basic Research (14-04-00725-a), The Russian Humanitarian Scientific Foundation (13-11-02014) and the Program of the Basic Research of the RAS Presidium “Biological diversity” (E.K.K.); Wellcome Trust and Royal Society grant WT104125AIA & the Bristol Advanced Computing Research Centre (http://www.bris.ac.uk/acrc/) (D.J.L.); Wellcome Trust grant 098051 (Q.A.; C.T.-S. and Y.X.); Wellcome Trust Senior Research Fellowship grant 100719/Z/12/Z (M.G.T.); Young Explorers Grant from the National Geographic Society (8900-11) (C.A.E.); ERC Consolidator Grant 647787 ‘LocalAdaptatio’ (A.Ma.); Program of the RAS Presidium “Basic research for the development of the Russian Arctic” (B.M.); Russian Foundation for Basic Research grant 16-06-00303 (E.B.); a Rutherford Fellowship (RDF-10-MAU-001) from the Royal Society of New Zealand (M.P.C.)

Identifikacija izvora iz 1SXPS kataloga otkrivenih pomoću Swift-XRT

Studying compact objects (CO) such as white dwarfs (WDs), neutron stars (NSs) and stellar-mass black holes (BH) with masses typically ranging from 3−20 M is important for understanding the endpoints of stellar evolution and accretion/ejection processes, ubiquitous phenomena in the Universe. It provides further insights into the gas state and distribution in the early Universe. Studying the cosmic history of supermassive BH (with masses ranging from 106−10 M) growth could shed light on the formation and evolution of galaxies. It tells us more about their role in the reionization of the Universe and their impact on their (interstellar and/or intergalactic) environment. COs are also excellent laboratories to study matter in extreme conditions, such as matter at super-nuclear density in the NS interior or matter under the effect of strong gravity and magnetic fields in the vicinity of BHs and NSs, respectively. Due to accretion of matter that goes on in their vicinity, most COs were first detected in X-rays. For these reasons, all-sky X-ray surveys are most suitable for finding and studying such sources. The Swift observatory, inoperation since 2004, carries an X-ray telescope (XRT) and two other co-aligned instruments, the Burst Alert Telescope (BAT) and the UV/Optical Telescope (UVOT). This enables a multi-wavelength study of CO behaviour from optical to hard X-rays. Swift is dedicated to the study of gammaray bursts that appear randomly on the sky. This implies that Swift pointings have been performed all over the sky,covering1905 square degrees with the XRT, with many fields observed several times within a day,over a period from many days to weeks/months (or even years),allowing to probe variability on various time scales. In this work, I investigated the nature of the X-ray sources contained in the Swift-XRT catalog in order to isolate interesting objects possibly harbouring an accreting compact object (e.g. Xray binaries, active galaxy nuclei – AGN, ultra-luminous X-rays sources, tidal disruption events). To do so, I cross-correlated a subsample of the Swift X-ray Telescope Point Source (1SXPS) catalog, containing 98,762 sources with detections of best quality, with 16 external multi-wavelength catalogs which provide source type identification (active galactic nuclei, stars, X-ray binaries etc.) using the Topcat software. This enabled me to build a golden sample of known objects, divided into three main classes (AGNs, COs and stars) that represent the main types of objects observed in the X-ray sky. Within this subsample, I found that it consists of 4929 AGNs, 1125 stars and 231 COs. I studied their temporal, spectral and spatial properties in order to define selection criteria which would enable me to classify the rest of the 1SXPS sources. I found that COs are the most variable group, followed by AGNs and stars. Using spectral indicators from the 1SXPS catalog (power-law photon index ΓPL and hardness ratios), I found that the ΓPL-distributions of COs, AGNs and stars differ. For AGNs, the ΓPL-distribution is clustering around 1.71,which is consistent with typical values derived for these objects. For COs, the distribution of ΓPL-values is more widely spread, likely corresponding to the different spectral states that can be seen in X-ray binaries. Stars also display scattered ΓPL-distribution, but with generally high values, implying that the PL model is not physical and that thermal emission is more likely. I found that most stars can be isolated from the rest of AGNs and COs thanks to their low X-ray to optical or IR flux ratio. After establishing the selection criteria, I applied them to the golden sample of identified sources, in order to investigate the reliability of the defined selection scheme. 73% of the stars, 35% of the COs, and 95% of the AGNs were retrieved, with false classification probability of 8%, 78% and 15%, respectively. The low number of retrieved COs and their high probability of false identification is due both to the large fraction of AGN with respect to COs in the golden sample and the fact COs and AGNs share similar properties (ΓPL-distribution and X-ray to optical/IR distribution intervals coincide). Finally, the selection criteria were applied back to the rest of the 1SXPS source high quality sample, resulting in 78,918 AGN candidates (86%), 9294 star candidates (10%) and 3752 CO candidates (4%). I discuss the caveats of the method used and I propose possible improvements, in particular for helping decreasing the high probability of false identification rate for COs. I also discuss further interesting works that could be done from the obtained results

Identifikacija izvora iz 1SXPS kataloga otkrivenih pomoću Swift-XRT

Studying compact objects (CO) such as white dwarfs (WDs), neutron stars (NSs) and stellar-mass black holes (BH) with masses typically ranging from 3−20 M is important for understanding the endpoints of stellar evolution and accretion/ejection processes, ubiquitous phenomena in the Universe. It provides further insights into the gas state and distribution in the early Universe. Studying the cosmic history of supermassive BH (with masses ranging from 106−10 M) growth could shed light on the formation and evolution of galaxies. It tells us more about their role in the reionization of the Universe and their impact on their (interstellar and/or intergalactic) environment. COs are also excellent laboratories to study matter in extreme conditions, such as matter at super-nuclear density in the NS interior or matter under the effect of strong gravity and magnetic fields in the vicinity of BHs and NSs, respectively. Due to accretion of matter that goes on in their vicinity, most COs were first detected in X-rays. For these reasons, all-sky X-ray surveys are most suitable for finding and studying such sources. The Swift observatory, inoperation since 2004, carries an X-ray telescope (XRT) and two other co-aligned instruments, the Burst Alert Telescope (BAT) and the UV/Optical Telescope (UVOT). This enables a multi-wavelength study of CO behaviour from optical to hard X-rays. Swift is dedicated to the study of gammaray bursts that appear randomly on the sky. This implies that Swift pointings have been performed all over the sky,covering1905 square degrees with the XRT, with many fields observed several times within a day,over a period from many days to weeks/months (or even years),allowing to probe variability on various time scales. In this work, I investigated the nature of the X-ray sources contained in the Swift-XRT catalog in order to isolate interesting objects possibly harbouring an accreting compact object (e.g. Xray binaries, active galaxy nuclei – AGN, ultra-luminous X-rays sources, tidal disruption events). To do so, I cross-correlated a subsample of the Swift X-ray Telescope Point Source (1SXPS) catalog, containing 98,762 sources with detections of best quality, with 16 external multi-wavelength catalogs which provide source type identification (active galactic nuclei, stars, X-ray binaries etc.) using the Topcat software. This enabled me to build a golden sample of known objects, divided into three main classes (AGNs, COs and stars) that represent the main types of objects observed in the X-ray sky. Within this subsample, I found that it consists of 4929 AGNs, 1125 stars and 231 COs. I studied their temporal, spectral and spatial properties in order to define selection criteria which would enable me to classify the rest of the 1SXPS sources. I found that COs are the most variable group, followed by AGNs and stars. Using spectral indicators from the 1SXPS catalog (power-law photon index ΓPL and hardness ratios), I found that the ΓPL-distributions of COs, AGNs and stars differ. For AGNs, the ΓPL-distribution is clustering around 1.71,which is consistent with typical values derived for these objects. For COs, the distribution of ΓPL-values is more widely spread, likely corresponding to the different spectral states that can be seen in X-ray binaries. Stars also display scattered ΓPL-distribution, but with generally high values, implying that the PL model is not physical and that thermal emission is more likely. I found that most stars can be isolated from the rest of AGNs and COs thanks to their low X-ray to optical or IR flux ratio. After establishing the selection criteria, I applied them to the golden sample of identified sources, in order to investigate the reliability of the defined selection scheme. 73% of the stars, 35% of the COs, and 95% of the AGNs were retrieved, with false classification probability of 8%, 78% and 15%, respectively. The low number of retrieved COs and their high probability of false identification is due both to the large fraction of AGN with respect to COs in the golden sample and the fact COs and AGNs share similar properties (ΓPL-distribution and X-ray to optical/IR distribution intervals coincide). Finally, the selection criteria were applied back to the rest of the 1SXPS source high quality sample, resulting in 78,918 AGN candidates (86%), 9294 star candidates (10%) and 3752 CO candidates (4%). I discuss the caveats of the method used and I propose possible improvements, in particular for helping decreasing the high probability of false identification rate for COs. I also discuss further interesting works that could be done from the obtained results

Classification probabiliste des sources de rayons X appliquées aux catalogues de Swift-XRT et XMM-Newton

International audienceContext. Serendipitous X-ray surveys have proven to be an efficient way to find rare objects e.g. tidal disruption events, changing-look AGN, binary quasars, ultraluminous X-ray sources, intermediate mass black holes. With the advent of very large X-ray surveys, an automated classification of X-ray sources becomes increasingly valuable. Aims. This work proposes a revisited Naive Bayes Classification of the X-ray sources in the Swift-XRT and XMM-Newton catalogs into 4 classes − AGN, star, X-ray binary (XRB) and cataclysmic variable (CV) − based on their spatial, spectral and timing properties and their multiwavelength counterparts. An outlier measure is used to identify objects of other nature. The classifier is optimized to maximize the classification performance of a chosen class (here X-ray binaries) and it is adapted to data mining purposes. Methods. We augmented the X-ray catalogs with multiwavelength data, source class, and variability properties. We then built a reference sample of about 25000 X-ray sources of known nature. From this sample the distribution of each property is carefully estimated and taken as reference to assign probabilities of belonging to each class. The classification is then performed on the whole catalog, combining the information from each property. Results. Using the algorithm on the Swift reference sample we retrieved 99%, 98%, 92% and 34% of AGN, stars, XRBs and CVs respectively, and the false positive rates are 3%, 1%, 9% and 15%. Similar results are obtained on XMM sources. When applied to a carefully selected test sample, representing 55% of the X-ray catalog, the classification gives consistent results in terms of distributions of source properties. A substantial fraction of sources not belonging to any class is efficiently retrieved using the outlier measure, as well as AGN and stars with properties deviating from the bulk of their class. Our algorithm is then compared to a Random Forest method, showing similar performance but the algorithm presented in this paper improved insight into the grounds of each classification. Conclusions. This robust classification method can be tailored to include additional or different source classes and applied to other X-ray catalogs. The transparency of the classification compared to other methods makes it a useful tool in the search for homogeneous populations or rare source types, including multi-messenger events. Such a tool will be increasingly valuable with the development of surveys of unprecedented size, such as LSST, SKA and Athena, and the search for counterparts of multi-messenger events

Probabilistic classification of X-ray sources applied to Swift-XRT and XMM-Newton catalogs

International audienceContext. Serendipitous X-ray surveys have proven to be an efficient way to find rare objects, for example tidal disruption events, changing-look active galactic nuclei (AGN), binary quasars, ultraluminous X-ray sources, and intermediate mass black holes. With the advent of very large X-ray surveys, an automated classification of X-ray sources becomes increasingly valuable.Aims. This work proposes a revisited naive Bayes classification of the X-ray sources in the Swift-XRT and XMM-Newton catalogs into four classes – AGN, stars, X-ray binaries (XRBs), and cataclysmic variables (CVs) – based on their spatial, spectral, and timing properties and their multiwavelength counterparts. An outlier measure is used to identify objects of other natures. The classifier is optimized to maximize the classification performance of a chosen class (here XRBs), and it is adapted to data mining purposes.Methods. We augmented the X-ray catalogs with multiwavelength data, source class, and variability properties. We then built a reference sample of about 25 000 X-ray sources of known nature. From this sample, the distribution of each property was carefully estimated and taken as reference to assign probabilities of belonging to each class. The classification was then performed on the whole catalog, combining the information from each property.Results. Using the algorithm on the Swift reference sample, we retrieved 99%, 98%, 92%, and 34% of AGN, stars, XRBs, and CVs, respectively, and the false positive rates are 3%, 1%, 9%, and 15%. Similar results are obtained on XMM sources. When applied to a carefully selected test sample, representing 55% of the X-ray catalog, the classification gives consistent results in terms of distributions of source properties. A substantial fraction of sources not belonging to any class is efficiently retrieved using the outlier measure, as well as AGN and stars with properties deviating from the bulk of their class. Our algorithm is then compared to a random forest method; the two showed similar performances, but the algorithm presented in this paper improved insight into the grounds of each classification.Conclusions. This robust classification method can be tailored to include additional or different source classes and can be applied to other X-ray catalogs. The transparency of the classification compared to other methods makes it a useful tool in the search for homogeneous populations or rare source types, including multi-messenger events. Such a tool will be increasingly valuable with the development of surveys of unprecedented size, such as LSST, SKA, and Athena, and the search for counterparts of multi-messenger events

Antibody seroprevalence against SARS-CoV-2 within the Canton of Sarajevo, Bosnia and Herzegovina—One year later

Background: Serostudies are important resources when following pandemics and predicting their further spread, as well as determining the length of protection against reinfection and vaccine development. The aim of this study was to update data on the prevalence of seropositive individuals in Canton Sarajevo, Bosnia and Herzegovina (B&H) from September 2020 to May 2021. Methods: Anti-SARS-CoV-2 antibodies were quantified using an electrochemiluminescence immunoassay. Results: Compared to the period April-July 2020, when anti-SARS-CoV-2 antibodies were detected in 3.77% of samples, one year later (May 2021) the estimated percentage within the same population of the urban Canton Sarajevo was 29.9% (5,406/18,066). Of all anti-SARS-CoV-2 Ig-positive individuals, 53.27% were men, and 69.00% were of 50 years of age or younger. Also, the current update found the individuals 50 years of age or younger to be more frequently anti-SARS-CoV-2 Ig positive compared to older individuals. On the other hand, higher median anti-SARS-CoV-2 Ig levels were found in individuals > 50 years old than in younger individuals, as well as in men compared to women. Seropositivity gradually increased from September 2020 to May 2021, with the lowest frequency of positive cases (3.5%) observed in September 2020, and the highest frequency (77.7%) in January 2021. Conclusion: Our results provided important seroprevalence data that could help in planning restrictive local public health measures to protect the population of Sarajevo Canton, especially considering that at the time of the study the vaccines were virtually inaccessible to the general population not belonging to any of the high-priority groups for vaccination