dataesr/scientific-paragraphs-categorization huggingface dataset:
This allows to probe this region with the highest sensitivity ever, and search for the potential signature of a high-energy non-thermal component as sometimes seen in other sources. Methods. Our work is based on state classification carried out in previous studies with data from the Rossi X-Ray Timing Explorer. We extend this classification to the whole INTEGRAL data set in order to perform a long-term state-resolved spectral analysis. Six stacked spectra were obtained using 16 years of data from JEM-X (3-25 keV), ISGRI (25-300 keV), and SPI (20-400 keV). Results. We extract stacked images in three different energy bands, and detect the source up to 200 keV. In the hardest states, our purely phenomenological approach clearly reveals the presence of an additonnal component > 50 keV in addition to the component usually interpreted as thermal Comptonization.
,  Szostek et al. 2008, K10). We see a strong presence of the disk in the spectrum while no emission above 80 keV has been reported so far (H09, K10). The hypersoft state corresponds to the quenched radio state. Despite a huge number of observations both in soft X-rays (1-10 keV) and hard (10-150 keV) X-rays (notably with RXTE), and contrary to many other bright XRBs (e. g. ,  Grove et al. 1998; Joinet et al. 2007; Rodriguez et al. 2008a; Laurent et al. 2011; Del Santo et al. 2016, Cangemi et al. , in prep. ) , only one detection between 100 and 200 keV has been reported so far in Cyg X-3 (H09). Here we make use of INTEGRAL to probe the properties of this peculiar source over the full 3-1000 keV range covered by the observatory. We use the spectral classification of K10 to separate the data into the six spectral states defined therein to extract state-resolved stacked spectra.
Data selection We consider all INTEGRAL individual pointings or science windows 2 (scws) of Cyg X-3 since the launch of INTEGRAL in 2002. We restrict our selection to scws where the source is in the field of view of the Joint European X-ray Monitors (JEM-X,  Lund et al. 2003 ) in order to be able to use the soft X-ray classification with JEM-X data, that is, where the source is less than 5 • off-axis. As JEM-X is composed of two units that have not always been observing at the same time, this selection results in 1518 scws for JEM-X 1 and 185 scws for JEM-X 2. For scws with both JEM-X 1 and JEM-X 2 on, we only select the JEM-X 1 spectrum in order to avoid accumulating double scws. In this paper, all the following scientific products were extracted on a scw basis, before being stacked in a state-dependent fashion.
INTEGRAL/JEM-X spectral extraction After the selection of the scws according to the criteria defined in Sect. 2. 1, the data from JEM-X are reduced with version 11 of INTEGRAL Off-line Scientific Analysis (OSA) software. We follow the standard steps described in the JEM-X user manual 3. Although, as mentioned above, most of the data are obtained with JEM-X unit 1, we nevertheless extract all data products from any of the units that were turned on during a given scw. Spectra are extracted in each scw where the source is automatically detected by the software at the image creation and fitting step. Spectra are computed over 32 spectral channels using the standard binning definition. The individual spectra are then combined with the OSA spe_pick tool according to the classification scheme described hereafter in Sect.
INTEGRAL/IBIS/ISGRI spectral extraction To probe the behavior of source in the hard X-rays we make use of data from the first detector layer of the Imager on Board the INTEGRAL Satellite (IBIS), the INTEGRAL Soft Gamma-ray Imager (ISGRI), which is sensitive between ∼20 and ∼600 keV  (Lebrun et al. 2003). As OSA version 11. 0 is valid for ISGRI data taken since January 1, 2016 (MJD 57388), we divide our analysis in two parts 4. The first part, which is analyzed with OSA 10. 2 extends from MJD 52799 (INTEGRAL revolution 80) to MJD 57361 (rev 1618), while the second part is analyzed with OSA 11. 0 and extends from MJD 57536 (rev 1684) to MJD 58639 (rev 2098). Light curves and spectra are extracted following standard procedures 5. For each scw, we create the sky model and reconstruct the sky image and the source count rates by deconvolving the shadowgrams projected onto the detector plane.
For the data analyzed with OSA 11. 0, we extract spectra with 60 logarithmically spaced channels between 13 keV and 1000 keV. For the OSA 10. 2 extraction, we create a response matrix with a binning that matches the one automatically generated by running the OSA 11. 0 spectral extraction as closely as possible. Response matrix channels differ at most by 0. 25 keV between the OSA 10. 2 and OSA 11. 0. We then use the OSA 10. 2 spe_pick tool to create stacked spectra for each spectral state according to our state classification (see Sect. 3. 2 below). We add 1. 5 % of the systematic error to both OSA 10. 2 and OSA 11. 0 stacked spectra. The ISGRI spectra obtained with OSA 10. 2 are analyzed in the 25-300 keV energy range, while those obtained with OSA 11. 0 are analyzed over the 30-300 keV energy range.
Proportional Counter Array data reduction and classification We consider all Rossi X-ray Timing Explorer (RXTE,  Bradt et al. 1993 ) Proportional Counter Array (PCA,  Glasser et al. 1994)  Standard-2 observations of Cyg X-3, that is to say 262 observations from 1996 to 2011. This adds three years of new observations compared to the work done by K10. The data are reduced with the version 6. 24 of the HEASOFT. We followed a procedure very similar to the one followed by  Rodriguez et al. (2008a,b)  to filter out the data from bad time intervals and to obtain PCA light curves and spectra for GRS 1915+105, another peculiar and very variable microquasar. We consider the data from all layers of the Proportional Counter Unit #2, which is the best calibrated and the one that is always turned on. Background maps are estimated with pcabackest using the bright model.
Extension of the RXTE/PCA state classification to INTEGRAL When evaluating the hardness ratio, the observed fluxes are convolved with the instrument response matrices, and thus are instrument dependent. Therefore, we cannot simply use the PCA state boundaries to classify the JEM-X data. The differences are illustrated in Table  A. 1 with 16 quasi-simultaneous JEM-X/PCA (i. e. , within 0. 1 MJD) observations. These examples show that there is no one-to-one correspondence between the JEM-X and PCA values of the HR. We therefore need to convert the PCA boundary values to those of JEM-X. To do so, and to thus extend the classification of K10 to the JEM-X data, we proceed as follows. For each division (quiescent/transition, transition/FHXR, FHXR/FIM, FIM/FXSR, FSXR/hypersoft), we select the closest PCA observations from both sides of the state division line, and then we search the best functions that fit the data 7  to the spectrum.
Subsequently, we simulate JEM-X data using that model and the appropriate redistribution matrix which allows us to finally calculate the countrate of the same energy range as used in K10. 2. HID of Cyg X-3. Left: HID of Cyg X-3 using PCA data in black dots. Each colored dot corresponds to an observation which has already been classified by K10. Vertical black-dashed lines correspond to our division into six states: Quiescent (light blue), Transition (dark blue), FHXR (purple), FIM (pink), FSXR (red), and hypersoft (orange). Right: HID with PCA and JEM-X data superposed. Gray dots correspond to the same data as in the left plot and colored dots correspond to classified JEM-X scws using the same color code as in the left figure. Vertical gray bands correspond to the JEM-X state divisions using the method described in the text.
We use xspec version 12. 9. 1p for all data modeling  (Arnaud 1996). Thanks to the show rates 8  command in xspec, we can find the corresponding hardness ratio in JEM-X. This allows us to draw new state divisions which can be used for JEM-X. Figure 2 (right)  shows the JEM-X HID; the vertical gray bands correspond to the new JEM-X state divisions using the described method above. As for PCA, JEM-X count rates are normalized to the maximum value. For our accumulated JEM-X spectra, we only select scws that are outside the state division line (i. e. , colored dot in the plot) in order to be sure that our accumulated spectra are not polluted by scws with different spectral classification. After this selection, a total of 1501 classified scws remain. As the SPI determination background is based on the dithering pattern  (Vedrenne et al.
2011; Jourdain et al. 2014; Rodriguez et al. 2015b) , another high-mass microquasar, while hybrid corona could be at the origin of the high-energy emission in softer states  (Cangemi et al. submitted). To carry out a basic test of this possibility in Cyg X-3 we gather infrared data from  Fender et al. (1996)  and a radio spectrum from  Zdziarski et al. (2016). The infrared data were collected using the United Kingdom Infra-Red Telescope (UKIRT) on August 7, 1984, when the source was in its quiescent state. Fluxes are dereddened using the λ -1. 7 extinction law of  Mathis (1990 ), and, following Fender et al. (1996) , we use A j = 6. 0 as the extinction value. A radio spectrum from  Zdziarski et al. (2016)  was obtained by averaging the "hard state" data from three measurements at 2. 25, 8.
Acknowledgements. The authors thank the referee for constructive comments that halped to improved the quality of the manuscript. The authors also warmly thank  E. Jourdain  and  J. P Roques  for their help on SPIDAI,  C. -A. Oxborrow  and  J. Chenevez  for their help with the JEM-X calibration issues, and  S. Corbel  for useful discussions and suggestions. FC, JR, PL & RB acknowledge partial funding from the  French Space Agency (CNES). FC, JR & RB acknowledge partial fundings from the  French Programme National des Hautes Energies (PNHE). VG is supported through the  Margarete von Wrangell fellowship  by the  ESF  and the  Ministry of Science, Research and the Arts Baden-Württemberg. Based on observations with INTEGRAL, an  ESA  project with instruments and science data centre funded by  ESA  member states (especially the PI countries:  Denmark, France, Germany, Italy, Switzerland, Spain ) and with the participation of Russia and the  USA.
Admixture is a fundamental evolutionary process that has influenced genetic patterns in numerous species. Maximum-likelihood approaches based on allele frequencies and linkage-disequilibrium have been extensively used to infer admixture processes from genome-wide data sets, mostly in human populations. Nevertheless, complex admixture histories, beyond one or two pulses of admixture, remain methodologically challenging to reconstruct. We developed an Approximate Bayesian Computation (ABC) framework to reconstruct highly complex admixture histories from independent genetic markers. We built the software package MetHis to simulate independent SNPs or microsatellites in a two-way admixed population for scenarios with multiple admixture pulses, monotonically decreasing or increasing recurring admixture, or combinations of these scenarios.
| AnABCframeworkforreconstructing complex admixture histories In this paper, we show how ABC can be successfully applied to reconstruct, from genetic data, highly complex admixture histories beyond models with a single or two pulses of admixture classically explored with ML methods. To do so, we propose a novel forwardin-time genetic data simulator and a set of parameter-generator and summary statistic calculation tools, embedded in an open source C software package called MetHis. It simulates genetic data from independent SNP or microsatellite loci under any two source-populations versions of the  Verdu and Rosenberg (2011)  general model of admixture; and is adapted to conduct ABC inferences with existing machine-learning ABC tools implemented in R (R Development Core Team, 2020). We show that our MetHis-ABC framework can accurately distinguish major classes of complex historical admixture models, involving multiple admixture-pulses, recurring increasing or decreasing admixture over time, or combinations of these models, and provides conservative posterior parameter inference under the chosen models.
| MATERIAL SANDME THODS We evaluated how ABC scenario-choice and posterior parameter estimation performed for reconstructing highly complex historical admixture processes from genetic data. To do so, we chose to work under the two source-populations version of the general mechanistic model of  Verdu and Rosenberg (2011)  briefly presented in Figure  S1. We introduce a novel software, MetHis, for genetic data simulation and summary statistic calculation for machine-learning ABC inference under this general model (Note S1). We conducted our proof of concept considering nine competing scenarios of complex admixture histories involving multiple admixture pulses, recurring decreasing or increasing admixture, and combinations of these processes (Figure  1 , Table  1 ). We explored  Afr. source introgression rates of pulses Afr,p1 and Afr,p2  Parameter list corresponds to the nine competing historical admixture scenarios described in Figure  1  and Section 2.
For users interested in microsatellite variation and longer admixture histories, MetHis readily implements a standard general stepwise mutation model allowing for insertion or deletion  (Estoup et al. , 2002) , with parameters set by the user (Note S1). To focus on the admixture process itself without excessively inflating the parameter space, we considered, for each of the nine competing scenarios, the admixed population H with constant effective population size N g = 1000 diploid individuals. Nevertheless, note that MetHis readily allows the user to parametrize, instead, stepwise or continuous changes in effective population size over time (Note S1). After each simulation, we randomly drew individual samples matching sample-sizes in our observed data set (see Section 2. 4. 3). We sampled individuals until our sample set contained no individuals related at the first degree cousin within each population and between population H and either source population, based on explicit parental flagging during the last two generations of the simulations.
| Simulating source populations MetHis, in its current form, does not allow simulating the source populations for the admixture process modeled in  Verdu and Rosenberg (2011). Simulating source populations can be done separately using existing genetic data simulation software such as fastsimcoal2 sequential coalescent  (Excoffier et al. , 2013; Excoffier & Foll, 2011). Another possibility to simulate source populations emerges if genetic data is already available for the known source populations, as it is the case in our case studies of enslaved-African descendants in the Americas (see Section 2. 4. 3). We considered here that the African and European source populations were very large populations at the drift-mutation equilibrium, accurately represented by the Yoruban YRI and British GBR data sets here investigated (see Section 2.
4. 3). Therefore, we first built two separate data sets each comprising 20,000 haploid genomes of 100,000 independent SNPs, each SNP being randomly drawn in the site frequency spectrum (SFS) observed for the YRI and GBR data sets respectively. These two data sets were used as fixed gamete reservoirs for the African and European sources separately, at each generation of the forward-in-time admixture process. From these reservoirs, we built an effective individual gene pool of diploid size N g , by randomly pairing gametes avoiding selfing. These virtual source populations provided the parental pool for simulating individuals in the admixed population H with MetHis, at each generation. Thus, while our gamete reservoirs were fixed, the parental genetic pools were randomly built anew at each generation.
| The distribution of admixture fractions as a set of summary statistics Most methods developed to estimate individual admixture fractions from genetic data (e. g. ,  Alexander et al. , 2009) , are computationally intensive, and are thus difficult to iterate over large sets of simulated genetic data. This explains why they have not been routinely used in ABC in the past, despite being theoretically highly informative for admixture inference  (Gravel, 2012; Verdu & Rosenberg, 2011). Here, we propose, and implement in MetHis, an efficient way to use estimated individual admixture fractions as summary statistics for ABC inference, based on allele sharing dissimilarity (ASD)  (Bowcock et al. , 1994)  and multidimensional scaling (MDS). For each simulated data set, we first calculated a pairwise interindividual ASD matrix using our implementation of the asd software (https://github.
| Simulating by randomly drawing parameter values from prior distributions We performed MetHis simulations under each of the nine competing scenarios (Figure  1 ), drawing the corresponding scenario-parameters in prior distributions detailed in We used Random-Forest ABC for scenario-choice implemented in the "abcrf" function of the abcrf package to obtain the cross-validation table and associated prior error rate using an outof-bag approach. We considered a uniform prior probability for the nine competing models. We considered 1,000 decision trees in the forest after visually checking that error-rates converged appropriately, using the "err. abcrf function". RF-ABC cross-validation procedures using groups of scenarios were conducted using the group definition option in the "abcrf" function  (Estoup et al.
| Case-study population genetics data sets We investigated, as two separate study-cases, the admixture histories of the African American (ASW) and Barbadian (ACB) population samples from the 1000 Genomes Project Phase 3 (1000 Genomes Project Consortium, 2015). Previous studies identified, within the same database, the West European Great-Britain (GBR) and the West African Yoruba (YRI) populations as reasonable proxies for the sources of both ACB and ASW, consistent with the macro-history of the Transatlantic Slave-Trade  (Baharian et al. , 2016; Martin et al. , 2017; Verdu et al. , 2017). Individuals in the 1000 Genomes Project were a priori sampled to be family unrelated. To avoid confounding factors due to cryptic relatedness in this sample set compared to MetHis simulations, we excluded individuals more closely related than first-degree cousins in the four populations separately using RELPAIR  (Epstein, Duren, & Boehnke, 2000) , as previously done  (Verdu et al.
, 2017). We also excluded the three ASW individuals showing traces of Native American or East-Asian admixture, as reported in previous studies  (Martin et al. , 2017). Among the remaining individuals we randomly drew 50 individuals in the target admixed ACB and ASW, respectively, and included the remaining 90 YRI individuals and 89 GBR individuals. We extracted biallelic polymorphic sites (SNPs as defined by the 1000 Genomes Project Phase 3) from the merged ACB+ASW+GBR+YRI data set, excluding singletons. Since MetHis could only simulate independent markers, we LD-pruned the ACB and ASW SNP-sets using the PLINK  (Purcell et al. , 2007)  "--indep-pairwise" option with a sliding window of 100 SNPs, moving in increments of 10 SNPs, with an r 2 threshold of 0. 1. Finally, we randomly drew 100,000 SNPs from the remaining SNP-set.
| Prior-checking of simulations' fit to the casestudy data sets We plotted prior distributions of each summary statistic and visually verified that the observed summary statistics for the ACB and ASW respectively fell within the simulated distributions. Then, we explored the first four axes of a principal component analysis (PCA) computed with the "princomp" function in R, using the 24 summary statistics and all 90,000 simulations, and visually checked that observed summary statistics were within the cloud of simulated statistics. Finally, we performed a goodness-of-fit approach using the "gfit" function from the abc package in R, with 1,000 replicates and tolerance level 0. 01.
For each pair of tolerance level and number of neurons, we conducted cross-validation with 1000 randomly chosen simulated data sets that we used, in turn, as pseudo-observed data with the "cv4abc" function in the package abc. We compared the median pointestimate of each posterior parameter ( ̂ i ) to the true parameter value used for simulation ( i ). The cross-validation parameter prediction error was then calculated across the 1000 separate posterior estimations for pseudo-observed data sets for each pair of tolerance level and number of neurons, and for each parameter i , as ∑ 1000 1 ( ̂ i -i ) 2 ∕ ( 1000 × Variance ( i ) ) , using the "summary. cv4abc" function in the package abc  (Csilléry et al. , 2012). Results showed that, a priori, all numbers of neurons considered perform very similarly for a given tolerance level.
, 2010; Robert et al. , 2010; Sisson et al. , 2018). To further increase the range of applicability of our MetHis-ABC framework, our software readily implements microsatellite markers together with a general stepwise mutation model  (Estoup et al. , 2002) , fully parameterizable by the user (Note S1). This will allow investigating numerous complex admixture histories from non-model species for which large amounts of SNP data are less frequently available, but for which microsatellite markers are readily available. Even if prior knowledge of the date for the founding admixture event is lacking, MetHis users can simply set the founding of the admixed population in a remote past and implement a second founding event with variable date to be estimated with ABC, together with later additional admixture events and other parameters of interest.
In this context, and because pedigrees are rarely known a priori, we developed MetHis, a C open-source software package available at https://github. com/romai n-laure nt/ MetHis. MetHis simulates independent SNPs or microsatellite mark- ers in an admixed population H under any version of the two source- populations general model from Verdu and Rosenberg (2011), and calculates summary statistics of interest to the study of complex admixture processes (Note S1).
We study the forward-backward asymmetry A F B in pp → + -at the Z peak within the Standard Model Effective Field Theory (SMEFT). We find that this observable provides per mille level constraints on the vertex corrections of the Z boson to quarks, which close a flat direction in the electroweak precision SMEFT fit. Moreover, we show that current A F B data is precise enough so that its inclusion in the fit improves significantly LEP bounds even in simple New Physics setups. This demonstrates that the LHC can compete with and complement LEP when it comes to precision measurements of the Z boson properties.
Thus one might naively expect that LHC data will not improve significantly our knowledge of the Zf f couplings. The purpose of this work is to study qualitatively and quantitatively this question in the SMEFT setup. In order to do that, we focus on the forward-backward (FB) asymmetry, A F B , which is particularly clean both experimentally and theoretically  [18, 19]. Contrary to the naive expectation, we find that current A F B measurements provide unique information about the Z couplings to quarks that improve significantly LEP-only bounds. This is true not just in complicated scenarios requiring intricate cancellations between many Wilson Coefficients, but also in simple theory setups. To illustrate this last point, we anticipate one of our results in Fig. 1 , which shows the experimental bounds on possible modifications of the Z couplings to left-and right-handed up quarks, assuming all other interactions are SM-like.