dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions We have performed the first comparison of two consecutive solar minima at Mars with the same dataset. We have found: • The ionosphere of Mars behaved, in general, very similar at both solar minima. Despite the extended low solar irradiance of the solar minimum 23/24, the largely reduced ionization found at Mars during that period might be the typical behavior of the ionosphere during any solar minimum. • The TEC increases linearly with respect to EUV for all SZA at both minima. In each panel, the relation 1:1 is indicated with a black dashed line and the current linear best fit in solid pink for solar minimum 23/24 and solid blue for solar minimum 24/25. The best-fit equations and R 2 are also included. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.
Acknowledgments B. S. -C and M. L. acknowledge support through  UK-STFC  Grant  ST/ S000429/1. M. C. acknowledges support from the  Italian Space Agency (ASI)  through contract  ASI-INAF 2019-21-HH. 0. MARSIS-MEX data are freely available at https://www. cosmos. esa. int/web/psa/mars-express, and TIMED-SEE data at the  University of Colorado 's website (https://l asp. colorado. edu/lisird/data/timed_see_ssi_l3/).
The ALICE apparatus is described in detail in  [37, 38]. The data were collected using triggers (as detailed in  [39] ) based on the signal amplitude in the V0 detectors  [40] , which are also used for the centrality determination  [41]. Events were further selected offline using selection criteria defined in  [39]. The number of events in the centrality classes 0-10% and 30-50% considered for this analysis is about 100 × 10 6 and 85 × 10 6 , respectively, corresponding to a luminosity of (130. 5 ± 0. 5) µb -1 and (55. 5 ± 0. 2) µb -1  [42]. The Monte Carlo (MC) simulations utilized in this analysis are produced using the HIJING 1. 36  [43]  and PYTHIA 8. 243  [44]  (Monash tune  [22] ) event generators and the GEANT3 transport package  [45] , as described in the supplemental material  [46].
6 to 0. 8 in 1 < p T < 5 GeV/c, and y fid = 0. 8 for p T > 5 GeV/c. The Λ + c -candidate selection was performed using multivariate techniques based on the Boosted Decision Tree (BDT) algorithm provided by the XGBoost package  [49]. The selection criteria considered in the optimization included both topological  [10]  and particle identification (PID) variables. The PID was performed using the specific ionization energy loss dE/dx in the TPC gas and the time of flight from the interaction point to the Time-Of-Flight (TOF) detector  [50, 51]. The BDT training was performed considering as signal candidates prompt (not coming from beauty-hadron decays) Λ + c decays from MC simulations  [46]. Background candidates were taken from the sidebands of the invariant mass distribution in data. The yields of Λ + c baryons were extracted in each p T interval via binned maximum-likelihood fits to the candidate invariant mass distributions.
36 event generator  [43]  to simulate Pb-Pb collisions at √ s NN = 5. 02 TeV. In each simulated event, Λ + c signals were added by injecting cc or bb pairs generated with the PYTHIA 8. 243 event generator  [44]  with the Monash tune  [22]. The Λ + c baryons were forced to decay into the hadronic decay channel of interest, Λ + c → pK 0 S → pπ + π -. All generated particles were transported through the ALICE detector using the GEANT3 package  [45]. The conditions of all the ALICE detectors in terms of active channels, gain, noise level, and alignment, and their evolution with time during the data taking, were taken into account in the simulations. Λ + c production.
The Catania model describes the data in both central and mid-central collisions for p T > 2 GeV/c, however for p T < 2 GeV/c the model predicts an R AA higher than unity which is disfavored by data. The SHMc model instead significantly underestimates the Λ + c R AA over the whole p T range. As already noted by the authors of  [9] , this suggests that the corona description could be further optimized.
The systematic uncertainties on the observables are estimated by varying the track selection and PID criteria. The systematic uncertainties due to track selection include the variation of the selection criteria on DCA xy , DCA z , the number of reconstructed space points in the TPC, and the quality of the track fit from their nominal values. The systematic uncertainties due to PID are calculated by varying the default n(σ TPC i ) and m 2 criteria. Systematic uncertainties due to each of these sources are considered as uncorrelated and the total systematic uncertainty on the observables is obtained by adding all the contributions in quadrature. The resulting ratio of the second to first order cumulant for d is shown in Fig. 1  for different centrality classes. The data is found to be consistent with unity within uncertainties as expected from a Poisson distribution and does not exhibit a significant centrality dependence.
Context. The Oort Cloud is located in the farthest outskirts of the Solar System, extending to a heliocentric distance of several tens of thousands of au, and remains the last region of the Solar System where no object has been detected. Thus, all our knowledge of the Oort Cloud has been deduced from the observed long-period comets that are thought to originate from it. Aims. We aimed to retrieve valuable information that might be hidden in the orbital distributions of the observed long-period comets. Such information will allow us to impose constraints not only on the present shape of the Oort Cloud but also on its initial shape 4. 5 Gyr ago. This has direct implications for the scenario proposed for its formation. Methods. We used two different databases of long-period comets. First, we calculated the distribution of orbital elements that might carry valuable information about the shape of the Oort Cloud.
Then, we compared the distribution with that obtained from two synthetic samples of observable comets. These samples correspond to two considerably different initial configurations: one is a disk model, where we consider a swarm of comets with orbits aligned to the ecliptic plane and with a cometary perihelion close to the giant planets. The other is an isotropic model, where we consider a fully isotropic and thermalized initial distribution of comets. Results. The comparison revealed that the databases contained several features that were in better agreement with the disk model than with the isotropic model. The Oort Cloud contained an initial disk of objects with perihelia close to the planetary region of the Solar System and aphelia extending out to roughly 20 000 au. Some parts of this disk likely remain in the present Solar System.
In Sect. 2, we recap the main results of  Fouchard et al. (2020)  and the relevant theoretical analysis in  Higuchi (2020). In Sect. 3, the databases employed in this work are presented, together with the methodology used to produce the useful distributions. The results of our exploration of these databases are presented in Sect. 4. Section 5 presents our conclusions. This preliminary investigation was based on the numerical orbit propagation of two large samples of synthetic comets over 4. 5 Gyr, taking the effects of the Galactic tides, passing stars, and planetary perturbations into consideration. The Galactic parameters -a density of the local Galactic disk ρ 0 = 0. 1 M ⊙ • pc -3 and an angular velocity of the Sun around the Galactic center Ω 0 = -26 km s - 1  kpc -1 -were taken from  Levison et al.
For the later Ω G p could also be smaller than 90 •. However, the number of comets in the databases did not allow us to distinguish between these two behaviors. For comets in the B3 group, the orbital plane was close to the EE plane, which implied an excess of Ω G p close to 0 • and an excess of cos i E p close to -0. 5. These features should be detected in comets with q p < 7 au. These features are related to previous orbital elements. This choice was necessary, as explained by  Fouchard et al. (2020)  because drastic changes in the orbital parameters occurred during the last orbital period, canceling out the features of interest. Unfortunately, this is problematic when known comets are considered instead of synthetic comets coming from numerical simulations. This will be discussed in  Sect.
Databases of cometary orbital elements In this study we consider only the comets that satisfy the following conditions: (1) comets whose perihelion distance q in the present apparition is smaller than 5 au, and (2) comets whose original semimajor axis a orig is greater than 10 4  au. Candidate comets were selected from two databases. We note that the cometary orbital elements stored in these databases do not differ from one another at a significant level  (Ito & Higuchi 2020; Ito 2021 ).
Warsaw catalog (CODE) Królikowska published a series of cometary catalogs called the Warsaw catalog of cometary orbits, also known as the CODE catalog. They are available in the VizieR database 3  and on their own websites 4. Details regarding the theoretical background and the implementation of their method are available in their series of publications:  Królikowska (2014 Królikowska ( , 2020)) ,  Królikowska et al. (2014) ,  Królikowska & Dybczyński (2017 , 2018 , 2020). As of 2023 January, the original orbital elements of 278 comets (along with their previous, current, next, and future orbits) are available with 1σ uncertainties. Królikowska's online comet catalog stores a maximum of 12 orbital solutions for a single comet. In this study we consider only their "preferred" orbit. We note that one of the main specificities of this catalog is the strong attention given to the modeling of non-gravitational forces (e.
Data processing The following procedure was developed to account for uncertainties in the original orbital energy, which are crucial for the origin of long-period comets. In both databases, the original, or assimilated, orbital elements were provided for each comet. These elements are called the nominal orbital elements. Each comet was then cloned 199 times; all clones had the same orbital elements as the nominal one except for the original orbital energy, which was computed randomly using a Gaussian distribution with the mean corresponding to the nominal orbital energy and the standard deviation equal to the uncertainty. Using the comet clones in the database, 200 different comet samples were built. Each sample contained the same comets as the database, but with different original orbital energies.
Hereafter, a sample from a database is called a clone of the database. A comet in a clone of a database is a "new" long-period comet when its original perihelion distance is smaller than 5 au, and its original orbital semimajor axis is larger than 10 4 au or the original orbit is hyperbolic. This implies that the number of new long-period comets in the different clones from the same database may not be always the same. As it was discussed in Sect. 2. 3, some features of interest can be detected considering the distributions of Ω G p and cos i E p. These elements were computed at the perihelion passage preceding the perihelion where the comet was observable. These data were systematically stored in the numerical experiments conducted by  Fouchard et al. (2020). This is clearly not the case for real comets, mainly because it is not possible to reconstruct precisely the effect of passing stars during the last orbital period.
In addition,  Fouchard et al. (2020)  observed that our features of interest on the Ω G p and cos i E p distributions were still present even if passing stars were non A104, page 4 of 12  Fouchard, M. , et al. : A&A proofs,  including for the computation of the previous orbital elements. Consequently, our strategy to investigate the presence or not of some features of interest in the Ω G p and cos i E p distributions is reliable. The direction of perihelion used to compute δ and δ * was that of the original orbit, that is, it was directly obtained from the databases. Each clone of a database yields distributions for cos i E p , Ω G p , | sin δ| and | sin δ * |. Hence, these 200 clones allowed the computation of the means and standard deviations for each distribution.
Splitting the samples As discussed in Sect. 2, in order to highlight the different features of interest, the sample of comets has to be split according to either q p for the Ω G p and cos i E p distributions or the original orbital energy for the | sin δ| and | sin δ * | distributions. Because the number of comets in the databases was much lower than the number of synthetic comets obtained from the disk or isotropic models, only three zones were defined for q p : (i) q zone #1: q p < q c ; (ii) q zone #2: q c < q p < 31 au; and (iii) q zone #3: q p > 31 au. Here, we used the value q c = 7 au. Therefore, according to  Fouchard et al. (2020) , comets with 7 < q p < 13 au were merged with comets with 13 < q p < 31 au. This choice allows for the best isolation of the EE features. That is, the EE features should be observed in distributions computed from comets in zone #1, and the KQ features in the distributions computed from the comets in zone #2.
We considered the orbital energy z = -1/a and computed the mean values, zi , of the nominal orbital energy of comets in q zone #i (hyperbolic comets are excluded for q zone #3). Then the medium value between z1 and z2 , z m , and the medium value between z2 and z3 , z M , were computed. Thus, the z zones are defined as follows: (i) z zone #1 contains comets with original orbital energy between -10 -4 au -1 and z m , (ii) z zone #2 contains comets with original orbital energy between z m and z M , and (iii) z zone #3 contains comet with original orbital energy greater than z M. As for the q zones, the EE-features should be observed in the distributions obtained from z zone #1 and KQ-features from comets in z zone #2. A final comment on data processing: the only difference between the clones of the same database was the original orbital energy of the comets.
This has a direct consequence on q p , Ω G p , and cos i E p , but it does not affect the value of | sin δ| or | sin δ * |. However, the distributions of the latter two elements will still be affected because the comets in the different z zones will be different. With regard to our synthetic samples coming from the disk and the isotropic models, the previous orbital elements are computed just as they are for the comets in the database, that is, neglecting passing stars. In addition, we decided not to apply any statistical process to generate the distributions from the models. The number of comets used in our simulations was huge (tens of millions) and the final sample size was rather large. In addition, the original orbital elements were not subject to any uncertainty, except for a small numerical one.
Preliminary investigations Our first exploration of the data involved counting the new observable comets. The number of comets in the different q and z zones was also computed. In addition, when origin of longperiod comets is considered, the original orbital energy is the key parameter; thus, the mean value of the orbital energy was computed for the total group and for the comets in the different zones. The original hyperbolic orbits were not considered when computing the mean orbital energy. All such comets were added to zones #3. The results obtained for the two synthetic models and the two database are listed in Table  1. We recall that the number of observable comets, or data, in the synthetic models must be converted to a flux per million years in order to take into account the orbital period of the comets.
That is the contribution of a comet to the flux scales as P -1 where P is the orbital period of the comet. A comparison of the fluxes was not be performed here. Such a study was already performed in  Fouchard et al. (2017a). It should be noted that the semimajor axes corresponding to the mean orbital energies for the q zones #1 and #2 were very similar for all sample of comets (databases or synthetics). This is a direct consequence of the fact that q p depends mainly on the orbital energy. It also depends on the Galactic tides model used; however, the same model was used for our synthetic comets as for the comets in the databases for the computation of q p. For the q zone #3, the mean orbital energy was higher for the models than for the databases. This will be discussed later in this paper.
It is important to note that for the databases the computed values of q p may differ from the real perihelion distances of the comets at their previous perihelion passage. This is not only because passing stars were neglected, but also because the Galactic tides model we used to compute the previous orbital elements might not correspond to the real ones, particularly the density of the Galactic disk in the solar neighborhood. Regarding the distribution in the different zones, it should be noted that the original orbital energy of the comets in the MPC database was globally larger than of the CODE catalogs. One reason for this could be that in the CODE catalog non gravitational forces are modeled with careful attention, and introduced more frequently than in the MPC database. Indeed, the best solutions for the osculating orbit in CODE were obtained by introducing non-gravitational forces (NGFs) for 50% of the comets.
In contrast, NGFs were introduced for only 6% of the best solutions in MPC database. Because non-gravitational forces generally increase the orbital energy, the original semimajor axis of a non-gravitational solution is generally smaller than that of a purely gravitational solution  (Królikowska 2006). A singular consequence of this is that the number of hyperbolic comets was much larger for the MPC database than for the CODE catalog. Notes. For each database, the first row gives the total number of comets on elliptic orbits or the flux per Myr for the models + the number of comets on hyperbolic orbits, followed by the semimajor axis corresponding to the mean orbital energy of all non-hyperbolic comets ā. In the whole table, numbers in brackets for the models correspond to the number of data; see the main text for details.
For the three q zones, the first line gives the border of each zone, the second line gives the number and the proportion of comets in each zone. The semimajor axis corresponding to the mean orbital energy of the comets in each q zone are on the third line. For the three z zones, the first line gives the border of each zone and the second line the number and proportion of comets in each zone. Another important difference between the databases and the models is that the outer zones are less densely populated for the synthetic models than for the databases. This may be attributed to several factors. For instance, the real outer bound of the Oort Cloud may be more stable than that of the synthetic models. Hence, stellar perturbations were overestimated in our synthetic models (on gigayear timescales).
Or, on the contrary, the stellar perturbations in the models may have been underestimated, which means that they are less efficient into injecting comets in the regions from which the Galactic tides can make them observable. Another reason could be that the original semimajor axes stored in the database were still overestimated despite the introduction of non-gravitational forces. This last point could also explain why the number of hyperbolic comets was higher in the databases than in the synthetic models and also why the median orbital energy for q zone #3 is higher for the databases than for the models. So far, we have not been able to determine which of these reasons are more likely. The answer resides in part in a better understanding of the neighborhood of our Solar System on a long timescale, in particular by astronomical observations.
The proportion of comets in q zone #1 showed the largest fluctuation among the sample considered. The differences between the CODE and the MPC databases may arise again from the orbital energy, which is generally larger for the MPC database than for the CODE database. Thus, the variations in the perihelion distance in one orbital period are larger for MPC comets, which implies that fewer comets are found in q zone #1. For the models, considering again that the tides are less powerful for comets in q zone #1, comets in this zone have the perihelion that may stay near the planetary region of the Solar System for many periods. A large ecliptic inclination is preferable for such comets to survive planetary scattering. Clearly, the isotropic model contains a larger number of such comets than the disk model; hence, more comets are found in q zone #1 in the isotropic model than in the disk model.
cos i Ep and Ω Gp distributions We first analyzed the pair of variables cos i E p and Ω G p. Figure  2  shows the position of the observable comets in the (Ω G p , cos i E p ) plane for the three q zones. The main features observed in the two models were already discussed by  Fouchard et al. (2020). As shown in Fig. 2 , the comets were concentrated along the i G = 90 • curve. Indeed, it has already been shown that this is a consequence of Galactic tides injection, and is almost independent of the route taken by the comets toward the observable region (see A104, page 6 of 12  Fouchard, M. , et al. : A&A proofs,. Position of the comets obtained through the two databases and the two models (CODE, MPC, disk, and isotropic, from left to right) in the (Ω Gp , cos i Ep ) plane for the three q zones from top to bottom (see the main text for details).
The double-hashed area is a forbidden region, and the solid black line corresponds to the Galactic inclination i G = 90 •. Higuchi et al. 2007 , for instance). The nonuniform distribution of comets along this line is related to this route, and it seems that some nonuniformity is also present in the data stored in the databases. We then considered the distribution of Ω G p and cos i E p. Using the strategy outlined in Sect. 3. 3, we considered 200 clones for each database. The means and standard deviations for each distribution were computed. As mentioned in Sect. 3. 3, no clone were computed for the synthetic models; thus, the distributions were directly obtained from the data. The results are shown in Fig. 3. First, we clearly define the dynamical features of interest. To achieve this, the distribution of the models was considered.
For a feature to be meaningful, it must be clearly visible in the distribution obtained from the disk model and not present in the distributions obtained from the isotropic model. With this constraint, the following features were identified: (i) E * 1 , an EE feature that corresponds to a maximum of cos i E p around -0. 5 in q zone 1, and E 1 and E 2 , two KQ features that correspond respectively to a maximum of Ω G p in the interval [90 • , 180 • ] and a maximum of cos i E p at 1 in q zone 2. These features are denoted by blue and orange patches, respectively, in Fig. 3. This raises the most important question in the present study, of whether the above three features are present in the orbital distributions of the comets stored in the databases. As shown in Fig. 3 , it is not possible to provide a clear answer.
However, the following observations were made. For E * 1 , the typical EE feature is rather clear on the MPC database but not on the CODE database. However,  Fouchard et al. (2017b)  show that a comet has a greater chance of surviving planetary scattering when its ecliptic inclination at the perihelion passage preceding the observed perihelion is close to 180 •  (Fouchard et al. 2017b). Hence, for an isotropic source of comets, a maximum of the cos i E p distribution should located in -1. This is the case for the isotropic model. However, none of the database cos i E p distribution shows such a maximum; thus, the comets in the databases in q zone #1 are not coming from an isotropic reservoir. For E 1 , in the Ω G p distributions, the KQ feature is clearly present in the CODE catalog. In addition, this feature is present in the MPC catalog but is less striking.