dataesr/scientific-paragraphs-categorization huggingface dataset:
In diagrams of K -W2 and M K versus J -K, it falls within the sequence formed by young L-type dwarfs and planets. 2. NIRSpec has provided the first spectroscopy of TWA 27B at 2. 5-5 μm. In these data, the fundamental band of CH 4 is absent, and the fundamental band of CO is weak, whereas both features are stronger in VHS 1256b. The nondetection of CH 4 reinforces a previously observed trend of weaker CH 4 with younger ages among L dwarfs  (Miles et al. 2018; Beiler et al. 2023; Miles et al. 2023) , which has been attributed to enhanced nonequilibrium chemistry among young objects. Explaining the weak CO in TWA 27B requires that an additional atmospheric property varies with age, such as the temperature gradient or cloud thickness. 3. We have compared the NIRSpec data for TWA 27B to spectra predicted by the ATMO models of cloudless brown dwarfs  (Tremblin et al.
2015 (Tremblin et al. , 2017)) , which include nonequilibrium chemistry and a temperature gradient reduction caused by fingering convection. The broad shape of the spectrum is matched well with a model that has T eff = 1300 K and a low surface gravity. One of the primary differences between the model and observed spectra lies in the fundamental bands of CH 4 and CO, which are somewhat stronger in the model spectrum. Meanwhile, the model temperature of 1300 K is higher than expected from evolutionary models given the luminosity and age of TWA 27B (T eff = 1200 K). Based on previous modeling of young L-type objects like TWA 27B, the inclusion of thick clouds could potentially resolve both issues (e. g. ,  Barman et al. 2011b). It remains to be seen whether cloudy models can provide a good fit to the 1-5 μm data from NIRSpec.
If it does have a disk, excess emission is likely present at longer IR wavelengths, which should be detectable with 5-28 μm data from JWST/MIRI that were collected during the same visit as the NIRSpec observations. 5. We have calculated the bolometric luminosity of TWA 27B by integrating its NIRSpec spectrum and estimating the missing flux at shorter and longer wavelengths with model spectra, arriving at a value of log L/L e = -4. 466 ± 0. 014. Based on that luminosity and the age its association (10 ± 2 Myr), TWA 27B should have a mass of 5-6 M Jup according to evolutionary models  (Burrows et al. 1997; Chabrier et al. 2023 ). These luminosity and mass estimates are similar to the values from previous studies.
P. T. acknowledges support from the European Research Council  under grant agreement  ATMO 757858. The JWST data were obtained from MAST at the  Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Inc. , under  NASA  contract  NAS 5-03127. The JWST observations are associated with programs 1270 and 1386. We acknowledge the team for program 1386 (PI:  S. Hinkley ) for developing their observing program with a zeroexclusive-access period. This work used data from the  ESA  mission Gaia (https://www. cosmos. esa. int/gaia), processed by the Gaia Data Processing and Analysis Consortium (DPAC; https://www. cosmos. esa. int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement.
2MASS is a joint project of the  University of Massachusetts  and  IPAC at Caltech , funded by  NASA  and the  NSF. WISE  is a joint project of the  University of California, Los Angeles , and the  JPL/Caltech , funded by  NASA. NEOWISE  is a joint project of  JPL/Caltech  and the  University of Arizona , funded by  NASA. This work used data from the  NASA/IPAC Infrared Science Archive , operated by  JPL  under contract with  NASA , and the VizieR catalog access tool and the SIMBAD database, both operated at  CDS, Strasbourg, France. The Center for Exoplanets and Habitable Worlds is supported by the  Pennsylvania State University , the  Eberly College of Science , and the  Pennsylvania Space Grant Consortium.
Methodology The data used in this work is obtained from the SPARC 7 data set. It contains independent observations of the total velocity (V tot ) and luminous mass distributions, which allows to infer the bulge (V b ), disk (V d ), and gas (V g ) velocity contributions. We extract the tangential velocities of the DM component from the data (for each galaxy of the sample) by subtracting the inferred baryonic components (as provided in the SPARC data set) from the total velocity V tot , and statistically compare with the corresponding velocities predicted by each DM model as detailed next.
Data Selection The SPARC data set includes 3. 6 μm near-infrared and 21 cm observations. The former traces the stellar mass distribution (bulge and disk) while the latter traces the atomic gas distribution and provides velocity fields from which the RCs are derived. See  Lelli et al. (2016)  for a complete description of the SPARC data. The data is distributed in separated files such as Table1. mrt 8 (i. e. , Hubble type, inclination etc. ) and Table2. mrt 9 (i. e. , RC data) and can be found at http://astroweb. cwru. edu/SPARC/. We extract the observed circular velocity V tot and the baryonic contribution V bar , composed of a bulge (V b ), disk (V d ), and gas component (V g ). The bulge and disk components are inferred from surface brightness observations for a given massto-light ratio.
The baryonic component is then given by ( ) = ¡ + ¡ + V V V V. 1 bar 2 b b 2 d d 2 g 2 For convenience, the given velocities V b and V d in the SPARC data are normalized for a mass-to-light ratio of 1 M e /L e. In this work, we follow the same data selection criteria as done in  McGaugh et al. (2016). We choose averaged mass-tolight ratios ϒ b = 0. 7 for all bulges, and ϒ d = 0. 5 for all disks as convenient average representatives. We exclude all galaxies with a bad quality flag (Q = 3) and face-on galaxies with an inclination i < 30°. The latter is to minimize the ( ) i sin corrections to the observed velocities. For all measurements, we require a minimum precision of 10% in velocity. Additionally we reject all points where the baryonic velocity is greater than 95% of the observed velocity.
This condition is required to avoid negative velocities for the inferred DM components. It affects mainly data points in the inner region, which is dominated by baryonic matter and strongly depends on the chosen mass-to-light factors. Therefore, those inner points are less reliable. Finally, we exclude all remaining galaxies with less than 6 data points to be statistically significant. We obtain 120 galaxies (out of 174) with 2396 points (of 3355) in total. The galaxies not fulfilling the quality criteria have such poor data that they do not allow to gain any insights. In the worst case (e. g. , too few points, points on a nearly straight line, etc. ), it is not possible to fit the RCs.
Acceleration Relations The RC of each component (e. g. , bulge, disk, gas) traces its centripetal acceleration a = v 2 /r, giving access to independent acceleration measurements. The radial acceleration relation compares the radial acceleration due to the total mass (a tot ) with that due to the baryonic mass (a bar ). is equally inferred from the SPARC data, while [ ()] = + a V v r r tot bar 2 DM 2 is inferred from the total mass distribution, composed of the observed baryonic component (taken from SPARC data) and the best-fitted circular velocities of the DM component v DM (r) for each DM model. See Sections 2 and 3. 3 for details on how best fits are obtained. For the SPARC data as well as for each DM model, we applied then a least-square fitting to obtain 0 a. The result obtained here from the SPARC data only  competing DM models are able to reproduce the radial acceleration relation.
Goodness of Model The SPARC galaxies show different characteristics in their RC such as a nearly flat curve through the entire galaxy data; a rising trend in the inner halo followed by a single maximum; or multiple extrema in the form of oscillations. See Figure  7  for three typical examples within the SPARC data set. Some galaxies show just a rising trend implying that the RCs are incomplete, likely due to the faintness and/or lack of data for outermost halo stars. Of interest is therefore a quantitative description about the goodness of a DM halo model fitting the entire galaxy sample (120 galaxies). The goodness of a fit for a single galaxy is well described by the χ 2 value; see Equation (4). When competing models with different numbers of parameters are compared, it is appropriate to consider the reduced χ 2 defined as c c = d r 2 2 , with the degree of freedom d = Np, N being the number of observables (for a single galaxy), and p is the number of parameters (of the considered model).
We recall that the DM models are suited for DM halo RCs with only one maximum. Therefore, we restrict further the SPARC sample to galaxies (44) showing one clear maximum in their RC (see also Section 5. 3 for details about the diversity of SPARC galaxies). This condition is equivalently reflected in the fermionic scenario where data supports a significant escape of DM particles, which is for energy-cutoff values at plateau of W p < 10 (see Appendix B for further details). For that subsample, the picture changes considerably-see Figure  4 -and can be summarized as Figure  3. Goodness of model analysis for the entire sample (120 galaxies). We count the population of fitted galaxies having a reduced χ 2 smaller than a given one. The normalized population (step-like) follows nearly a log-normal distribution (solid) characterized by the mean value ĉr 2.
We would like also to point out that many galaxies in the SPARC data set are missing significant information in the outer halo (e. g. , due to faint stars) or show a complex behavior (oscillatory-pattern) in their RCs (see, e. g. , right box in Figure  7 ). In any case, it does not allow to univocally determine the cutoff parameter (i. e. , W p ) for the fermionic DM model because any sufficiently large W p would not change the χ 2 value (see, e. g. , bottom left panel of Figure  8 ). Nevertheless, there are some other individual galaxies where the escaping particles effects are clearly preferred. Interestingly, all of those galaxies are of Magellanic type: NGC 0055 (Sm), UGC 05986 (Sm), UGC 05750 (Sdm), UGC 05005 (Im), F565-V2, (Im), UGC 06399 (Sm), UGC 10310 (Sm), UGC 07559 (Im), UGC 07690 (Im), UGC 05918 (Im), and UGC 05414 (Im).
(2015b) , and further rederived here for fermionic DM profiles but this time for realistic average halo-mass galaxies, as obtained from the SPARC data set, following the thermodynamic analysis of  Argüelles et al. (2021). That is, typical galaxies with halo masses of about ∼5 × 10 10 M e and with appreciable escape of particles (i. e. , W p = 1) are thermodynamically and dynamically stable, suggesting a deep link between thermodynamics of selfgravitating systems and galaxy formation. A case-by-case stability analysis in relation to observed galaxies (e. g. , SPARC data set) is out of the scope of the present paper and will be the subject of a future work. bounded [ ] Î W W W ,.
Diversity of SPARC Rotation Curves We show in this section a detailed χ 2 analysis of the RC fits for three selected galaxies, each representing some characteristics of given observational data. We divide the SPARC galaxies in three groups by the inferred DM component as explained next. This analysis is based on the fermionic model where such a grouping seems to be appropriate to select galaxies with valuable predictions about the inner halo. The first group, represented by UGC 05986, shows only a single maximum in its DM RC, i. e. , a rising trend in the inner halo followed by a clear turning point, as can be seen by the data points in the left plot of Figure  7. In the same plot for UGC 05986, we show the best fits of the competing DM models as thick curves, i. e. , the fermionic (blue), DC14 (yellow), and NFW (red).
Summary and Conclusion For the case of disk galaxies, as provided by the SPARC data set (see Section 2. 1), we have studied the galactic RCs and different galaxy scaling relations-such as the radial acceleration relation, MDAR, and DM SDR-from an alternative perspective in which the halos are formed through an MEP. Within this paradigm, we considered the DM halo as a selfgravitating system of neutral fermions at finite temperature while the baryonic mass components were provided from the SPARC data set. For comparison, we have taken into account empirical DM fitting models motivated from DM-only simulations like NFW (within CDM) and Burkert (within WDM); the DC14 (or generalized NFW) model, which contains different physics such as the influence of baryonic feedback in the morphology of CDM halos; and the Einasto model as recently studied in  Bozek et al.
Individual Rotation Curve Fittings A deeper understanding of the radial acceleration relation and MDAR is backed by a goodness of model analysis for 120 filtered and individual galaxies of the SPARC data set covering different Hubble types. The DM contribution to the RCs reflects some diversity in galaxies, which, in general, are better fitted by cored DM halo models instead of cuspy (e. g. , NFW; see Figure  3 ). This is not only in agreement with a similar analysis done by  Li et al. (2020)  but also totally in line with the results of  Kaplinghat et al. (2020). That is, it has been shown here that the DM halo models suitable to explain the acceleration relations do not necessary explain well the SPARC RCs. Comparing the fitting goodness of the superior DC14 with the inferior (and statistically disfavored) NFW implies that baryonic feedback mechanism is important in galaxy formation.
From the measured V (μ 0 H ⊥ , I ≥ I tot c ) oscillation with a period of μ 0 H osc = 0. 16-0. 24 mT (Fig. 3d-f  and Extended Data Fig. 3 ) and using the relationship μ 0 H osc = Φ 0 A eff SQUID , we obtain A eff SQUID = 9-13 µm 2. This value is 2-3 times larger than the geometrical area of the SQUID loop ((2λ L + L x ) (2λ L + L y ) = 4. 1 µm 2 ), which we attribute to a flux-focusing effect. Note that if the width of the SQUID loop is much larger than λ L , the flux-focusing effect comes into play and effectively widens the SQUID area to be L ctc x L ctc y ≈ 11 µm 2 (Fig. 3b ), indicating the reliable performance of our SQUID. Here L ctc x,y is the centre-to-centre spacing between the tracks defining the two opposite sides of the SQUID. Interestingly, from a comparison of the sweep-up and sweep-down V SQUID (μ 0 H ⊥ ) data (Fig.
Acknowledgments We thank  Oren Bergman ,  Markus Dierigl ,  I. García-Etxebarria ,  Yuta Hamada ,  Jonathan Heckman ,  Ben Heidenreich ,  Simeon Hellerman ,  Jake McNamara ,  Paul Oehlmann ,  Cumrun Vafa ,  Irene Valenzuela , and  Alberto Zaffaroni  for many useful discussions and comments on the draft. MM is grateful to the  Simons Center Summer Workshop 2022  for hospitality and a stimulating environment while this work was completed, as well as to the "Engineering in the Landscape" workshop at  Uppsala University. HPF is grateful for the hospitality of the  CMSA at Harvard University , under the  CMSA Swampland Program. The work of MM is supported by a grant from the  Simons Foundation  ( 602883 , CV). This work was also partially supported by the  ERC  Consolidator Grant  772408-Stringlandscape.
Selection of signal candidates Signal candidates are reconstructed through the decays J/ψ → µ + µ -and ψ(2S) → µ + µ -, and are required to have a rapidity within the range 2. 0 < y * < 4. 5, where the starred notation indicates that the observable is defined in the nucleus-nucleus centre-of-mass frame. All remaining selection criteria given here are defined in the laboratory frame. One of the candidate muons must pass the hardware-level trigger, which requires a muon p T greater than 500 MeV/c. The dimuon candidates are selected with a minimum-bias software trigger, requiring at least one track reconstructed by the vertex detector; this software trigger is 100% efficient with respect to the following offline selection, since it has a looser multiplicity requirement. The offline selection requires two muon candidates, both with tracks that have p T > 700 MeV/c within the pseudorapidity range 2.
Source Relative uncertainty [%] σ coh J/ψ σ coh ψ(2S) Tracking efficiency 0. 5-2. 0 0. 5-2. 0 PID efficiency 0. 9-1. 6 0. 9-1. The momentum resolution is expected to shift events from one p * T interval to another. The uncertainties due to the momentum resolution are evaluated by comparing the p T spectra between generated and reconstructed events. The evaluated relative uncertainties vary from 0. 9 to 34% for different p * T intervals. The largest uncertainty corresponds to the p * T interval between 140 to 160 MeV as shown in table 8 (appendix A), where very small signal yields are observed. The slight discrepancies between the data and the fit results are visible at ln(p * 2 T ) ∼ -4 [ln ( GeV/c 2 )] for both the J/ψ and ψ(2S) fits, as seen in figure  3. This is expected to originate from a mis-modelling of the predicted signal shape from simulation.
Acknowledgments We express our gratitude to our colleagues in the CERN accelerator departments for the excellent performance of the LHC. We thank the technical and administrative staff at the LHCb institutes. We acknowledge support from  CERN  and from the national agencies:  CAPES ,  CNPq ,  FAPERJ  and  FINEP (Brazil) ;  MOST  and  NSFC (China) ;  CNRS/IN2P3  (France);  BMBF ,  DFG  and  MPG (Germany) ;  INFN (Italy) ;  NWO (Netherlands) ;  MNiSW  and  NCN (Poland) ;  MEN/IFA (Romania) ;  MICINN (Spain) ;  SNSF  and  SER (Switzerland) ;  NASU (Ukraine) ;  STFC  ( United Kingdom );  DOE NP  and  NSF (USA). We acknowledge the computing resources that are provided by  CERN ,  IN2P3 (France) ,  KIT  and  DESY (Germany) ,  INFN (Italy) ,  SURF (Netherlands) ,  PIC (Spain) ,  GridPP  ( United Kingdom ),  CSCS (Switzerland) ,  IFIN-HH (Romania), CBPF (Brazil) ,  Polish WLCG (Poland)  and  NERSC (USA).
The best characterization of these diffuse foregrounds has been done by Planck (Planck Collaboration 2020d) using a data set covering frequencies from 30 to 353 GHz. This frequency range limited strongly the estimation of the synchrotron spectral parameters. In Planck Collaboration (2020d) it is shown that, with Planck data only, one cannot test the spatial variability of the synchrotron spectral index due to limited sensitivity and frequency coverage. The data only allows a measurement of a global spectral index of βs = -3. 1 ± 0. 1. The synchrotron spectral index has also been estimated using other datasets, e. g. ,  Fuskeland et al. (2014) ;  Krachmalnicoff et al. (2018) ;  Fuskeland et al. (2021). The Q-U-I JOint Tenerife Experiment (QUIJOTE)  (Rubiño-Martín et al. 2010 ) is a polarimetric ground-based CMB experiment whose main scientific goal is the characterization of the polarization of the cosmic microwave background (CMB) and other Galactic and extragalactic physical processes in the frequency range 10-40 GHz and at large angular scales ( 1 • ).
Datasets In this analysis we have used the data from the following experiments: • QUIJOTE. We have used the low frequency QUI-JOTE MFI 11 and 13 GHz channels (MFI) (Rubiño-Martín et al. 2023) due to their better signal-to-noise ratio. Although QUIJOTE has observed 70% of the sky there are regions with poorer sensitivity due to the presence of artificial satellites and high atmospheric masses in some directions. Thus, in this analysis we have considered the mask shown in Fig. 1 , as the observable sky. This mask (satband+NCP) is described in  Rubiño-Martín et al. (2023). • WMAP. We have used the low frequency Nine-Year Wilkinson Microwave Anisotropy Probe (WMAP) K (22. 8 GHz) and Ka (33. 1 GHz) bands  (Bennett et al. 2013)   4. • Planck. We have used the full set of Planck polarization maps i.
We found that if only QUI-JOTE maps are filtered with FDEC the recovered βs map is biased in regions such as the North Polar Spur. When all data maps are filtered in the same way this bias disappears. Thus, for this analysis we have filtered all signal maps with their corresponding FDEC function. Another important instrumental effect arises from detectors having a finite bandwidth. This issue has to be taken into account when dealing with foreground components whose amplitude varies within that frequency band. This effect can be corrected by adding a multiplicative factor, called colour correction, to the signal that depends on the spectral behaviour. We have used the fastcc Python code  (Peel et al. 2022 , Génova-Santos et al. 2023 ) to obtain the colour corrections of each experiment considered here.
Synchrotron Spectral Index The major improvement obtained from including the lowfrequency QUIJOTE-MFI channels is having the sufficient sensitivity to study the synchrotron spectral index with great accuracy. Here we have conducted a deep study on several aspects with regard to this parameter. First, we have compared the recovered βs maps using different combinations of the available datasets (Section 5. 1. 1). Section 5. 1. 2 studies the spatial variability of βs. Finally, we compare our results to the available βs models that are often exploited in simulations used in CMB science forecasts in Section 5. 1. 3.
Datasets We have obtained different βs maps from component separation analyses using the four following datasets: WMAP K and Ka bands with PR4 (K/Ka+PR4); QUIJOTE-MFI 11 and 13 GHz channels with PR4 (MFI+PR4); QUIJOTE-MFI 11 and 13 GHz channels, WMAP K and Ka bands and PR4 (MFI+K/Ka+PR4) and QUIJOTE-MFI 11 and 13 GHz channels, WMAP K and Ka bands and PR3 (MFI+K/Ka+PR3). The results are shown in Fig. 2. It is clear from the comparison of the synchrotron spectral index uncertainty maps obtained in the K/Ka+PR4 case (first column) with respect to the MFI+K/Ka+PR4 case (third column), that the inclusion of QUIJOTE channels significantly improves the estimation of βs. Moreover, we observe that, outside the Galactic plane, the estimation of βs is very close to the mean value of the prior set on this parameter, in this case -3.
In this work we use the reduced χ 2 estimator, whose value at a given pixel p is calculated as: χ 2 red,p = 1 N dof i∈{Q,U } (dp,i -Sp,i)C -1 p,i (dp,i -Sp,i) , ( 10 ) where the sum is over all Q and U frequency channels, and N dof is the number of d. o. f. The bottom row of Fig. 2  shows the χ 2 red maps obtained for each dataset combination. These maps show that our default model, i. e. , a power law and a Figure  3. Reduced χ 2 , χ 2 red , obtained using the MFI+K/Ka+PR4 dataset vs. the χ 2 red obtained using K/Ka+PR4 (left), MFI+PR4 (center) and MFI+K/Ka+PR3 (right). The color scale is related to the density of points, redder (bluer) corresponds to denser (sparser) regions. The orange rectangle shows the χ 2 red within a 95% confidence region. The slope calculated with the points within this 95% confidence region is m = 0.
We have also used the χ 2 red estimator to select the dataset that is used as the default for further tests between the MFI+K/Ka+PR3 and the MFI+K/Ka+PR4 datasets, i. e. , the only combinations that include all the channels considered. In Fig. 3  the χ 2 red obtained using the MFI+K/Ka+PR4 dataset is plotted against the χ 2 red obtained with K/Ka+PR4, MFI+PR4 and MFI+K/Ka+PR3. The 95% confidence regions are delimited by orange lines. These lines indicate the χ 2 values, from the reduced χ 2distribution with N dof d. o. f. 10 , that satisfy that the normalized area covered to their left is equal to 0. 95. We have also fitted the points within this confidence regions to a straight line to determine which dataset has more pixels with smaller χ 2 red. If the slope is larger than unity, the dataset on the horizontal axis has more pixels with smaller χ 2.