dataesr/scientific-paragraphs-categorization huggingface dataset:
0. We call attention to the fact that the limb darkening coefficients obtained in Sect. 5 could have been used as priors since both planets are transiting the same star. However, the two planets may transit the stellar disk in different regions, and since AU Mic is largely filled by starspots, the limb darkening may also be affected by the different temperatures of the transited regions in the photosphere. Table  3  presents the priors and fit parameters, while Fig. 6  shows the TESS data for the three transits and the best-fit model obtained from our analysis. As in Sect. 5, the MCMC samples and posterior distributions are illustrated in Figs. C. 2 and C. 4 in Appendix C.
005, T c0. 005q, for T c being the time of conjunction calculated from the fit ephemeris. We subtract T c from the measured times of conjunction to obtain the TTVs as presented in Table  5. We detect no significant TTV in the TESS data. TTVs with amplitudes of one minute or more would have been likely detected in this dataset. Using the nominal solution from Table  4 , we generated the synthetic TTVs as in  Hébrard et al. (2020) , for the minimum and the maximum masses of the outer planet. In Fig. 9 , we show the variations corresponding to each solution superimposed with the observational data (Table  5 ). For the outer planet, the amplitude of the TTV is around one minute for both mass choices. However, for the inner planet, the minimum mass produces TTV with an amplitude of only a few seconds, while for the maximum  mass the amplitude can reach up to three minutes.
AU Mic b and c with an orbital period of 8. 46 d and 18. 86 d are near the 9:4 mean-motion resonance. Such a configuration is stable and could cause significant TTV, which we do not detect with the current dataset. Still, that non-detection provides an upper limit to the mass of AU Mic c of M c ă 7 M C , implying an upper limit to the planet density of ρ c ă1. 3 g cm ´3. This suggests that both planets are likely inflated, as it should be expected for young planets (e. g. ,  Helled et al. 2020). Therefore, these planets are interesting targets for atmospheric characterization by transmission spectroscopy. The interactions between the planets coupled in a near 9:4 mean-motion resonance and the debris disk surrounding the star are to be the subject of subsequent studies. High-resolution images have revealed that the disk has a complex structure with small-sized substructures, such as "feature A" at a dozen astronomical units showing a "loop-like" morphology  (Wisniewski et al.
2019). As for the case of β Pictoris, it has been shown that the planets can sculpt the disk morphology through the gravitational interaction between planets and the debris disk parents bodies at far distances  (Lecavelier Des Etangs et al. 1996; Augereau et al. 2001) , particularly through resonance mechanisms in young systems that are still evolving (Lecavelier Des Etangs 1998). The impact of the presence of AU Mic b and AU Mic c on the dust disk deserves further analysis. MAST data archive at the Space Telescope Science Institute (STScI). In particular, the TESS data secured in 2020 are part of the TESS Guest Investigator Programs G03263 (PI: P. Plavchan), G03141 (PI: E. Newton), and G03273 (PI: L. Vega). Together with other data and analyses of that particularly interesting system, that will be the subjects of forthcoming papers by  Cale et al.
, Collins et al. , El Mufti et al. , Gilbert et al. , Wittrock et al. Funding for the TESS mission is provided by the NASA Explorer Program. STScI is operated by the Association of Universities for Research in Astronomy, Inc. , under NASA contract NAS 5-26555. We thank the TESS Team members for making available the extremely accurate photometric data they obtained. We acknowledge support by CFisUC projects (UIDB/04564/2020 and UIDP/04564/2020), GRAVITY (PTDC/FIS-AST/7002/2020), ENGAGE SKA (POCI-01-0145-FEDER-022217), and PHO-BOS (POCI-01-0145-FEDER-029932), funded by COMPETE 2020 and FCT, Portugal. This work benefited from HPC resources of MesoPSL financed by the Region Ile de France and the project Equip@Meso (reference ANR-10-EQPX-29-01) of the programme Investissements d'Avenir. Table  D.
The LHCb collaboration has also measured the b-quark production cross-section at √ s = 7 and 13 TeV in the region with pseudorapidity 2 < η < 5, using semileptonic decays of b-flavoured hadrons  [6]. The ATLAS collaboration has measured the inclusive b b-dijet production cross-section  [7] , while the CMS collaboration has performed a measurement of the inclusive b-jet production cross-section  [8]. The latter two measurements were performed at √ s = 7 TeV in the central pseudorapidity region, with |η| < 2. 5. In this paper, a measurement of the inclusive b band cc-dijet cross-sections at √ s = 13 TeV is presented. The data sample used corresponds to a total integrated luminosity of proton-proton (pp) collisions of 1. 6 fb -1 , collected during the year 2016. Cross-section measurements are also performed differentially as a function of the dijet kinematics.
A global event cut (GEC) on the number of hits in the scintillating-pad detector is also applied. The software trigger requires at least one charged particle to be reconstructed with p T > 1. 6 GeV/c that is inconsistent with originating from any PV, as well as the presence of two jets. Both jets are reconstructed as described below, and required to have p T > 17 GeV/c and a secondary vertex (SV) in the jet cone. Simulation is required to model and correct for the effects of the detector acceptance and the imposed selection requirements. In the simulation, pp collisions are generated using Pythia  [16]  with a specific LHCb configuration  [17]. Decays of unstable particles are described by EvtGen  [18] , in which final-state radiation is generated using Photos  [19]. The interaction of the generated particles with the detector, and its response, are implemented using the Geant4 toolkit  [20]  as described in Ref.
The SV-tagging efficiency, determined in simulation, is about 60% for b-jets and 20% for c-jets. These values are lower with respect to those obtained in the √ s = 7 and 8 TeV datasets studied in Ref. [25]. The relative efficiency loss is below the 10% level, and is explained by the higher particle multiplicity of √ s = 13 TeV events with respect to √ s = 7 and 8 TeV events, that introduces more noise in the SV finding. To further distinguish light-flavour jets from heavy-flavour jets and b-jets from c-jets multivariate analysis algorithms as described in Ref. [25]  are used. Two boosted decision tree (BDT) classifiers  [26] [27] [28] , that use as inputs variables related to the SV, are employed: one for heavy-/light-jet separation (BDT bc|q ) and the other for b-/c-jet separation (BDT b|c ).
The outputs of the BDT bc|q and BDT b|c classifiers show a correlation with the jet p T , while they are almost uncorrelated with the jet pseudorapidity and the other kinematic variables. For this reason different templates are built for different intervals of [p T (j 0 ), p T (j 1 )]. The p T binning scheme is the following:  [20, 30]  GeV/c,  [30, 40]  GeV/c, [40,50] GeV/c, [50,60] GeV/c and p T > 60 GeV/c. As by definition p T (j 0 ) is higher than p T (j 1 ), only 15 non-empty [p T (j 0 ), p T (j 1 )] intervals are present. In order to measure the differential b band cc-dijet yields the dataset is divided in subsamples for each of the kinematic observables. For a given observable the data sample is divided in three dimensions into bins of that observable and in bins of [p T (j 0 ), p T (j 1 )].
The evaluation of these systematic uncertainties is described in Sec. 6. Pseudoexperiments are performed to assess the fit stability and to determine the fit bias and coverage. Relative biases of the order of 0. 01% (0. 02%) on the fitted b b (cc) yields  The observables t 0 and t 1 obtained by summing the fitted yields over the η(j 0 ) and [p T (j 0 ), p T (j 1 )] bins, where t 0 and t 1 are the linear combinations of the tagging observables of both jets. The fit is compared against the data (black points). The statistical uncertainty on data is small and not visible in the plot. The stacked histograms show the contribution from: (red) b b, (blue) cc, (lavender) bq and qb, (light blue) cq and qc. The qq component is not displayed since its yield is negligible. The dashed grey areas represent the total uncertainty (statistical and systematic) on the fit result.
6 lower in data than in simulation. The GEC efficiency is determined in data using independent samples of events with no GECs applied. Finally trig is corrected for data/simulation differences in the efficiency of the other trigger requirements. To do this per-jet weights are measured with a tag-and-probe technique, comparing data and simulation samples of Z + jet events. The total selection efficiency for the b b process is found to be about 15% and about 1. 5% for the cc process. This difference is due to the SV-tagging efficiency, as explained in Sec. 3. Unfolding matrices are obtained for each of the four considered observables using simulation. Uncertainties due to the finite simulated sample size in the unfolding matrix construction are propagated to the result. Since the detector response is known to be similar for b-and c-jets and the b b simulation sample is larger, the b b sample is used for both the b b and cc unfolding.
[31, 35]. The systematic contributions from each source are summarized in Table  2. In the table, the mean relative uncertainties calculated averaging over η(j 0 ) intervals are reported separately for b b and cc events. Since the different sources are considered to be uncorrelated, the total uncertainties are obtained by summing the individual uncertainties in quadrature. The unfolding-related systematic uncertainties are in principle correlated with some of the efficiency uncertainties, but since they are sub-dominant this correlation is neglected. The dominant systematic uncertainties are those related to the GEC efficiency, the SV-tagging and the fit procedure. Finally, a closure test is performed to assess the validity of the analysis procedure. For this, the simulation samples are used to prepare a test dataset, and the full analysis chain is applied, from the fit to the unfolding.
These quantities are obtained by modelling the IR photometry from the Jin et al. (  2018 ) "super-deblended" catalogue, and by adding the dust continuum emission observed with ALMA in the  obs ∈ [0. 8 -3. 2] mm wavelength range. The FIR modelling is performed by using the customized  2 minimisation tool S -  (Kokorev et al. 2021)  1 and it is based on the Magdis et al. 1 https://github. com/VasilyKokorev/stardust (2012) approach which uses  Draine & Li (2007)  dust models and a mid-infrared AGN torus component from  Mullaney et al. (2011). The total infrared luminosity obtained from the fitting thus consists of a component associated with star formation ( IR,SF ), and one component arising from the dusty torus ( IR,AGN ). This allows us to estimate the fraction of the total IR luminosity associated with the dusty torus as  AGN =  IR,AGN / IR.
(2020)  and obtained using the same sample considered in this work (green dotted line in the left panel of Figure  3 ). To quantify the contribution of these galaxies to the scatter observed in the  Valentino et al. (2020)  relation, we fit  52 as a function of ΔMS excluding compact and ambiguous galaxies with ΔMS ≤ ±3. 5. We also exclude the strongest outlier above the main sequence, for consistency with our previous analysis. We apply a Bayesian regression analysis in the log-log space using the Python version of the _. package (Kelly 2007). The results of this fit are listed in Table  2. The best-fit line is shown as a dark red solid line in the left panel of Figure  3. The slope is  = 0. 45 ± 0. 14 and this is ∼ 2× steeper at ∼ 1. 3 than the trend reported in  Valentino et al. (2020)  when considering all galaxies regardless of their compactness.
Average CO spectral line energy distributions as a function of the compactness In the previous section we used the  52 ratio to study the CO excitation as a function of the main sequence offset and the compactness in individual galaxies in our sample. However, CO(4-3) and CO(7-6) observations available for a subset of our sources (see Tables 3  (a) Number of sources with CO line ratio measurements and upper/lower limits used to infer the relations presented in this table. We note that the _. package does not allow us to account for the presence of upper and lower limits at the same time. Therefore, the best-fit parameters for the ΔMS- 52 and  gas - 52 relation reported here are derived considering  52 detections only. We have however tested that including  52 detections and upper limits or  52 detections and lower limits we obtain fully consistent results.
ACKNOWLEDGEMENTS We would like to thank the referee for their constructive report which significantly improved the content and clarity of the paper. AP acknowledges funding from  Region Île-de-France  and  Incoming CEA  fellowship from the  CEA-Enhanced Eurotalents program , cofunded by  FP7 Marie-Skłodowska-Curie COFUND program  (Grant Agreement  600382 ). AP also gratefully acknowledges financial support from  STFC  through grants  ST/T000244/1  and  ST/P000541/1. MTS acknowledges support from a  Scientific Exchanges visitor fellowship  ( IZSEZO 202357 ) from the  Swiss National Science Foundation. AP thanks  Ivan Delvecchio  and  Ian Smail  for helpful discussions.
The software provides lymph node auto-contouring for pelvic, breast and head and neck tumor locations. However, it was not retained by the local medical staff for head and neck tumors. The trained models were directly made available to users, without specific training on local data. No further evolution of the model is currently planned on the basis of the prospective data acquired in clinical routine. The deployment timeline started with head and neck tumors, for which a first multicentric evaluation was carried out using data from the Centre Léon Bérard and Gustave Roussy (Villejuif, France). In this study, 100 patients were retrospectively selected in both centers (50 from each) and for each patient ART-Plan solution was used to generate full annotations of 14 OAR. After VSDC evaluation, the automatic contours were blended with those corresponding to the clinical experts (manual contours) in a blinded fashion.
Each contour was then scored by 5 experts from both centers, as A/clinically acceptable, B/clinically acceptable after minor corrections, C/not acceptable. Results showed that 96% of all manual contours were classified as clinically relevant (A + B). Values were equal to 98% for automatic contouring. The software had difficulty in delineating optical nerves and sub-mandibular glands. Following these positive results, deployment was pursued for pelvic and thoracic tumor locations, in five patients per location as a local test set. Five OAR were evaluated for the pelvic region and 6 OAR for the thoracic one. The comparison of the contours created by ART-Plan Annotate with the ones drawn by a clinical expert showed a mean Jaccard's index of 86% for the pelvic region and of 84% for the thoracic region.
Deployment of DLCExpert in Hôpital Européen Georges Pompidou (Paris) DLCExpert (Mirada Medical, Oxford, UK) exploits state-of-theart DL technology to delineate automatically OAR and lymph node areas for head and neck, thorax, breast and prostate tumor locations. To provide high quality contours, training data integrates hundreds of patient datasets (at least 150 patient data per model), curated by clinical experts according to consensus guidelines. Based on diverse European collaborations, several models have been developed per anatomy, which allows the tool to be configured to fit local practices. Thus, institution can make the choice to keep model A for organ X and model B for organ Y. A post-processing tool is finally proposed by the provider to interact with the contours after they have been automatically inferred.
In Section 4 we present our analysis results including tests of the adequacy of our models; we find that the radius of PSR J0740+6620 is R e = 13. 7 +2. 6  -1. 5 km at 68% credibility. We also describe several differences between our analysis and the analysis in the parallel paper  Riley et al. (2021) , in Section 4. 6. In Section 5 we present the implications of our PSR J0740+6620 analysis, combined with our previous results on PSR J0030+0451 and other data, for the EOS of cold dense matter. Our conclusions are in Section 6. We present our full corner plots and tables of posteriors in the appendices. Samples from our full posterior probability distributions are available at https://zenodo. org/record/4670689.
Our Waveform Modeling Procedure For PSR J0030+0451 we had only NICER data to analyze. In contrast, for PSR J0740+6620 we also have XMM-Newton imaging data from pn, MOS1, and MOS2. We therefore need to model all four data sets jointly. Once we have full models including backgrounds for all four data sets, then the log likelihood of the data given the model is simply the sum of the log likelihoods of each of the individual data sets. Thus ln L tot = ln L NICER + ln L XMM-pn + ln L XMM-MOS1 + ln L XMM-MOS2. (1) In writing the total log likelihood this way, we are assuming that the data sets are uncorrelated with each other. This assumption is clearly justified for the independent NICER and XMM-Newton data. It is also reasonable for the three XMM-Newton data sets because the pn, MOS1, and MOS2 cameras are all separately mounted and read out on XMM-Newton; there is no plausible mechanism that would link counts in one detector with counts in another detector.
The log likelihood we use is therefore ln L = φ i E j (d ij ln m ij -m ij ) , ( 2 ) where there is an implied sum over all four instruments. The NICER data have 32 phase bins. In contrast, for each of the three XMM-Newton data sets, we have only one bin in rotational phase because the timing resolution is insufficient to divide the data further. We now discuss the two components of the calculation of m ij for each phase-channel bin in each data set: the computation of the model counts from the spots for each of the four instruments, which is straightforward, and the incorporation of phase-independent background counts, which is more involved and requires different treatments for the NICER and for the XMM-Newton data.
Calculation of the model counts from the spots Suppose that we are considering a particular parameter combination, i. e. , we have some combination of M , R e , θ obs ,. to assess. Given the specified observation time, this gives us a spots-only waveform that is unique up to an arbitrary definition of the time corresponding to phase 0. As described in Section 3. 4 of  Miller et al. (2019) , once we have a candidate waveform that includes both the contribution from the spots and the contribution from the phase-independent background, we marginalize over the overall phase by fitting a Gaussian to the likelihood as a function of phase (this is equivalent to simply fitting a parabola to the log likelihood near its peak). We only need to carry out this process for the NICER data, because the XMM-Newton data have only one phase.
We therefore do not assume any particular spectral form for the non-stellar emission, but in practice this results in an estimated background that varies smoothly with energy channel. The assumption of independence between channels means that we can fit a Gaussian to the likelihood of each energy channel as a function of added background, and can then analytically integrate the likelihood to marginalize over the background. See  Miller et al. (2019)  for more details. We also note (see Section 4. 2 for a more thorough discussion) that future analyses may be able to incorporate estimates of the NICER background rather than letting the background be completely free. The XMM-Newton data must be treated differently, because there is only one phase bin, in contrast to the 32 rotational phase bins that we use for the NICER data.
To understand why, suppose that we treat the XMM-Newton background, for any instrument, in the same way that we treat the NICER background: each energy channel (with its single phase) can have added to it any number of background counts. Then the best model for the XMM-Newton data would have zero counts from the spots, and the background would equal the observed number of counts. This would automatically maximize the log likelihood. The NICER data are not driven to this pathological fit because there is clear modulation in the data; a phase-independent, background-only fit cannot reproduce this modulation. Therefore, we instead assume that the blank-sky background is a Poisson realization of the full XMM-Newton background. Each of the XMM-Newton instruments has had, throughout the course of the mission, significant exposure to portions of the sky with no known X-ray sources (see Section 2).
Then a given b avg implies an expected number of counts b obs = b avg (T obs /T back ) counts in that observation. Then because b avg = (T back /T obs )b obs , Equation (3) implies that the normalized probability distribution for b obs is P (b obs )db obs = (1/b back !)[b obs (T back /T obs )] b back exp[-b obs (T back /T obs )](T back /T obs )db obs. (4) If the energy channel had d observed counts in our PSR J0740+6620 observation, then the probability of obtaining that in a model with m expected counts is p(d|m) = m d /d! exp(-m). (5) If in our model we have s expected counts from the spots, then given a background b we expect a total number of counts m = s + b. Setting b = b obs as above, we find that the final likelihood of the data d given the spot counts s and the distribution P (b obs )db obs is p(d|s, back) = ∞ 0 [(s + b obs ) d /d!] exp[-(s + b obs )]P (b obs )db obs.
Choice of Energy Channels In their analysis of the PSR J0030+0451 data,  Miller et al. (2019)  used only NICER energy channels 40 and above, because they found that inclusion of lower-energy channels biased the inferred distance. For our analysis of the PSR J0740+6620 data we use the updated July 2020 NICER response (see https://heasarc. gsfc. nasa. gov/docs/heasarc/caldb/nicer/). With this response there does not appear to be any bias in distance or other quantities when we use data down to energy channel 30. Analysis of the NICER data showed that the detection significance is optimized when we use channels up to 118 (see Section 2. 1), but there is also an excess of counts relative to the background up to channel ∼ 150. We used a range between these (channels 30 through 123 inclusive), but exploratory runs including channels up to 150 did not produce palpably different results.
2 an additional fit in which we add one more parameter: A XMM , which is a single number that multiplies the ARFs of all three XMM-Newton instruments that we consider. We explore the consequences of allowing A XMM to be between 0. 9 and 1. 1, i. e. , the largest possible range. We therefore account for a possible inaccuracy in the ARF of NICER by the freedom in the fit to the distance to the source, and for an additional independent inaccuracy of the ARF of the XMM-Newton instruments using A XMM. We find that introduction of the parameter A XMM does not produce a significant change in the posteriors of any of our parameters (for example, the radius limits are changed by at most 0. 1 km) and the value of A XMM that we infer is consistent with 1. Note-Results of fits to synthetic J0740-like NICER and XMM-Newton data using parallel-tempered emcee and two different settings of MultiNest.