dataesr/scientific-paragraphs-categorization huggingface dataset:
The tension between Planck and BAO data (that forces the Planck data to be compatible with a flat universe when combined with it) is also not relieved when EDE is considered. As it is well known, the indication for a closed universe from the Planck data is at odds with practically all remaining cosmological observables. This could suggest that either there is an undetected systematic effect in the Planck data, or that additional modifications to the model should be considered. Following this second line of reasoning, it has also been shown that including a variation in the dark energy equation of state, w, can make Planck and luminosity distance data in agreement with a closed universe, providing w < -1, i. e. also assuming a dark energy phantom model. While a phantom closed model could appear as an extreme modification to the flat CDM model, it has been shown that a Planck+Pantheon analysis prefers this model over CDM at the level of three standard deviations.
(Riess et al. , 2021). The dashed markers represent the values of K = 0 and A L = 1. As we can see there is no correlation between EDE and, A L and K parameters. The inclusion of the BAO dataset significantly prefers A L > 1 solution to the Planck anomalies, leaving the constraints on f EDE as practically unchanged. It is therefore interesting to investigate the bounds on the EDE component also in this extreme scenario. In Table  II  and in Fig. 2  we report the constraints obtained in the case of an EDE+w and EDE+ K +w scenarios. As we can see, there is no correlation between EDE and the dark energy equation of state. For example, the constraints on w from Planck+BAO are essentially the same in the CDM and EDE cases. When moving to the EDE+ K +w extension we note that also in this case the constraints on f EDE are practically the same as those reported in Table I with w = -1.
We could also see that a closed phantom model is preferred from the Planck+SN dataset at about three standard deviations while Planck+BAO data prefer a flat cosmological constant model. As we can better see in Fig. 2 , there is indeed a significant tension between the Planck+BAO and Planck+SN results with the corresponding confidence level contours that are completely disjointed in the K -w plane and prefer two significantly different scenarios. Since this tension is present also in the CDM model, we can therefore conclude that the introduction of EDE does not help in alleviating the tension between these two datasets in case of a K + w scenario. Finally, we also study the constraints in the EDE+ K + A L scenario, therefore considering at the same time both changes in the curvature and in the A L lensing parameter.
We report the constraints obtained in this parameter extension in Table  III  and in Fig. 3. As already pointed out in the literature, there is a strong degeneracy between curvature and the A L parameter when only the Planck data is considered. The degeneracy is clearly noticeable by looking at the constraints in the A L vs K plane in Fig. 3. The indication for a closed universe from Planck angular spectrum data is clearly driven by the A L anomaly and this is confirmed also when an EDE component is considered. However, despite varying both the curvature and the A L parameter and having therefore a significant variation in the constraints on cosmological parameters with respect to the CDM case, the upper limits on the f EDE component are essentially left unchanged. As we can see from Fig. 3 , the posteriors on f EDE do not show any indication for a value different from zero.
Conclusions In this brief paper we have considered constraints on an early dark energy component from the full Planck dataset in an extended parameter space. Since the Planck angular power spectrum data is showing some anomalous value of the lensing amplitude or curvature parameter, we checked if these anomalies could also hide a possible indication for an early dark energy component by anti correlating with the f EDE parameter. The answer to this question is negative: the Planck constraints on the f EDE parameter are essentially unaffected by variation in lensing, curvature, and dark energy equation of state. Our results therefore suggest that the main indication for a EDE component mostly relies on the ACT-DR4 dataset.
Acknowledgments E. F. acknowledges the support from  'la Caixa' Foundation  ( ID 100010434 , code  LCF/BQ/DI21/11860061 ) and the " Center of Excellence Maria de Maeztu   2020-2023 " award to the  ICCUB  ( CEX2019-000918-M  funded by  MCIN/AEI/10. 13039/501100011033 ). A. M. thanks  TASP INFN initiative  for support. L. P. acknowledges the financial support from the  INFN InDark initiative  and from the  COS-MOS  network (www. cosmosnet. it) through the  ASI (Italian Space Agency)  Grants  2016-24-H. 0  and  2016-24-H. 1-2018. We acknowledge the use of getdist (Lewis, 2019) package, and the use of computing facilities at  CINECA.
Acknowledgements Funding information This work has been financed by the  CEA , the  INFN , the  ÖAW  and partially supported by the  TU Munich  and the  MPI für Physik. NUCLEUS members acknowledge additional funding by the  DFG  through the  SFB1258  and the  Excellence Cluster ORIGINS , by the  European Commission  through the  ERC-StG2018-804228  " NU-CLEUS ", by the  P2IO LabEx  ( ANR-10-LABX-0038 ) in the framework " Investissements d'Avenir"  ( ANR-11-IDEX-0003-01 ) managed by the  Agence Nationale de la Recherche (ANR) , France and by the  Austrian Science Fund (FWF)  through the  "P 34778-N, ELOISE ".
ACKNOWLEDGMENTS We thank  Benjamin Basso ,  Maxim Chernodub ,  Alexander Gorsky ,  David Gross ,  Shota Komatsu ,  Marina Krstic-Marinkovic ,  Martin Kruczenski ,  Robert Pisarski ,  Jiaxin Qiao  and  Junchen Rong  for useful discussions. V. K. thanks  Kavli Institute for Theoretical Physics (KITP) of California University at Santa Barbara  for kind hospitality during the work on this project.
We present an alternative calibration of the MagLim lens sample redshift distributions from the Dark Energy Survey (DES) first three years of data (Y3). The new calibration is based on a combination of a Self-Organising Maps based scheme and clustering redshifts to estimate redshift distributions and inherent uncertainties, which is expected to be more accurate than the original DES Y3 redshift calibration of the lens sample. We describe in detail the methodology, we validate it on simulations and discuss the main effects dominating our error budget. The new calibration is in fair agreement with the fiducial DES Y3 n(z) calibration, with only mild differences (< 3𝜎) in the means and widths of the distributions. We study the impact of this new calibration on cosmological constraints, analysing DES Y3 galaxy clustering and galaxygalaxy lensing measurements, assuming a ΛCDM cosmology.
The SOMPZ method works by leveraging the DES deep fields, which have deeper observations with additional photometric bands and overlap with many-band redshift surveys available. It is possible to reproduce realistic selection functions in the deep fields from the injection of galaxies into actual DES images using the sophisticated image simulation tool Balrog  (Everett et al. 2022 ). The SOMPZ method provides an ensemble of redshift n(z) for a given galaxy sample, which captures the uncertainties in the redshift distributions at all orders (i. e. , not only in the mean or width of the distributions). The clustering constraints are then incorporated through a rigorous joint likelihood framework where the clustering data is forward modelled as a function of the input n(z), and the specific WZ systematics are marginalized over.
Balrog sample The Balrog sample is another key element of the SOMPZ methodology. It is used to relate galaxies with given deep photometry to observed galaxies with wide field photometry, which are noisier. To this aim we rely on Balrog  (Suchyta et al. 2016 ), a software which injects "fake" galaxies into real images. For this analysis, Balrog was used to inject deep field galaxies into the broader wide field footprint  (Everett et al. 2022). After injecting galaxies into images, the output Balrog images are passed into the DES Y3 photometric pipeline and injected galaxies are detected equivalently to real galaxies, yielding multiple realisations of each injected galaxy. The Balrog sample spans ∼20% of the DES Y3 footprint. We further select injected galaxies using the MagLim selection. We then construct a matched catalog matching Balrog injected wide field MagLim galaxies with their deep field counterparts, for a total of 351,165 galaxies with both deep and wide photometric information.
The maximum-aposteriori values of the shift and stretch and related uncertainties obtained through this matching procedure are used as a prior for the DNF n(z) shift and stretch used in the cosmological inference. Despite the DNF+WZ and SOMPZ+WZ methods using the same photometric and clustering measurements, the methodologies differ in a number of aspects: (i) SOMPZ vs DNF uncertainties: SOMPZ and DNF are both machine learning methods, but they are substantially different in spirit and implementation. DNF is a traditional supervised machine learning code where the likelihood (directional neighborhood) between wide field magnitudes/colors and redshift is learned from training with a subsample of galaxies with both reliable redshift information and measured wide field photometry. On the other hand, in SOMPZ machine learning is only used in an unsupervised fashion (without knowledge of redshift), to group self-similar parts of wide field magnitude/color space together.
Hyperrank was also implemented for the WL sources, although it had a negligible impact on the results. The quantity chosen for the ranking in that case was the mean. We decided for this case it would be more appropriate to perform the optimised ranking of the realisation by the 68% sigma rather than the mean, and we tested it indeed improved the performance of the sampling. To test the different sampling strategies, we built a synthetic noiseless data vector based on theory predictions at fixed cosmology and we used as n(z) the realisations average of the SOMPZ+WZ estimates in data. We then marginalised over redshift uncertainties using the three approaches aforementioned. We performed this test both using 4 or 6 lens bins, although here we are just going to show the posteriors obtained with 4 bins as they are not qualitatively different from the ones with 6 bins.
When marginalising over the uncertainties using the hyperrank framework, on the other hand, such correlations are implicitly accounted for. Indeed, one can notice that the hyperrank posteriors are slightly tighter than the shift or shift-stretch posteriors. Unfortunately, we did not manage to successfully apply hyperrank to the data. When performing the cosmological analysis on data using hyperrank, we found significantly less smooth posteriors compared to our tests on simulations. A similar behaviour has also been found when applying hyperrank to the DES Y3 source sample  Amon et al. (2022) , and it has been interpreted as a consequence of a possible larger degree of complexity of the redshift distributions of our data compared to simulations. We attempted both to artificially smooth our n(z) and to increase the number of samples from the SOMPZ+WZ method, without reaching a satisfactory level.
ACKNOWLEDGEMENTS The project leading to these results have received funding from  "la Caixa" Foundation  ( ID 100010434 ), under the fellowship  LCF/BQ/DI17/11620053  and has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No. 713673. Funding for the DES Projects has been provided by the  U. S. Department of Energy , the  U. S. National Science Foundation , the  Ministry of Science and Education of Spain , the Science  and Technol-The Collaborating Institutions  are  Argonne National Laboratory , the  University of California at Santa Cruz , the  University of Cambridge ,  Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas-Madrid , the  University of Chicago ,  University College London , the  DES-Brazil Consortium , the  University of Edinburgh , the  Eidgenössische Technische Hochschule (ETH) Zürich ,  Fermi National Accelerator Laboratory , the  University of Illinois at Urbana-Champaign , the  Institut de Ciències de l'Espai (IEEC/CSIC) , the  Institut de Física d'Altes Energies ,  Lawrence Berkeley National Laboratory , the  Ludwig-Maximilians Universität München  and the associated  Excellence Cluster Universe , the  University of Michigan , the  National Optical Astronomy Observatory , the  University of Nottingham , The  Ohio State University , the  University of Pennsylvania , the  University of Portsmouth ,  SLAC National Accelerator Laboratory ,  Stanford University , the  University of Sussex, Texas A&M University , and the  OzDES Membership Consortium.
Based in part on observations at  Cerro Tololo Inter-American Observatory at NSF' s NOIRLab (NOIRLab Prop. ID  2012B-0001 ; PI:  J. Frieman ), which is managed by the  Association of Universities for Research in Astronomy (AURA)  under a cooperative agreement with the  National Science Foundation. The DES data management system is supported by the  National Science Foundation  under Grant Numbers  AST-1138766  and  AST-1536171. The DES participants from Spanish institutions are partially supported by  MINECO  under grants  AYA2015-71825 ,  ESP2015-66861 ,  FPA2015-68048 ,  SEV-2016-0588 ,  SEV-2016-0597 , and  MDM-2015-0509 , some of which include  ERDF  funds from the  European Union. IFAE is partially funded by the  CERCA program  of the  Generalitat de Catalunya. Research leading to these results has received funding from the  European Research Council  under the  European Union 's  Seventh Framework Program  ( FP7/2007-2013 ) including  ERC  grant agreements  240672 ,  291329 , and  306478.
Measurement of muon pairs produced via 𝜸𝜸 scattering in non-ultraperipheral Pb+Pb collisions at √  NN = 5. 02 TeV with the ATLAS detector The ATLAS Collaboration Results of a measurement of dimuon photoproduction in non-ultraperipheral Pb+Pb collisions at √  NN = 5. 02 TeV are presented. The measurement uses ATLAS data from the 2015 and 2018 Pb+Pb data-taking periods at the LHC with an integrated luminosity of 1. 94 nb -1. The  →  +  -pairs are identified via selections on pair momentum asymmetry and acoplanarity. Differential cross-sections for dimuon production are measured in different centrality, average muon momentum and pair rapidity intervals as functions of acoplanarity and  ⊥ , the transverse momentum kick of one muon relative to the other. Measurements are also made as a function of the rapidity separation of the muons and the angle of the muon pair relative to the second-order event plane to test whether magnetic fields generated in the quark-gluon plasma affect the measured muons.
The remainder of this paper is structured as follows: Section 2 describes the ATLAS detector; Section 3 describes the data and Monte Carlo samples used in the measurement and the applied event and dimuon selections; Section 4 describes the corrections for trigger and reconstruction inefficiency and the estimation and subtraction of backgrounds; Section 5 describes the systematic uncertainties in the measurement; Section 6 presents the results; and Section 7 presents a summary of the results and conclusions.
Data Sets The Pb+Pb data used in this measurement were recorded in 2015 and 2018 with integrated luminosities of 0. 49 nb -1 and 1. 44 nb -1 , respectively. Two dimuon triggers were used for this analysis. The first trigger (L1Single) required a single muon with  T > 4 GeV at L1, and two muons with  T > 4 GeV at the HLT. The second trigger (L1Pair) required two muons with  T > 4 GeV at L1 and also at the HLT. By requiring only one muon at L1, the first trigger has greater L1 trigger efficiency, but it was prescaled for a small portion of the data-taking and thus did not sample the full luminosity. A total of 4. 7 million and 12. 2 million events were obtained from these triggers in 2015 and 2018, respectively. Separate samples of minimum-bias Pb+Pb events and Pb+Pb events selected by a combination of single-muon triggers are used to evaluate the efficiency of the muon triggers used in this analysis.
383  [26]  event generator. The detector response in the MC samples was simulated using G 4  [27] , and the resulting events are reconstructed using the same algorithms that are applied to the data  [28]. A total of 4 million such events are analyzed using the same methods as applied in the data analysis. The STAR MC sample is also used for comparison with the measured observables. Potential backgrounds from DY processes are estimated using the P B v2  [29] [30] [31]  generator interfaced to P 8 configured using parameter values set to the AZNLO tune  [32]  and CTEQ6L1  [33]  parton distribution functions (PDFs). Separate samples of  , , and  events were generated and combined with appropriate isospin weights. The P B generator was configured to provide per-event weights for five different nuclear PDF sets: nCTEQ15  [34] , EPPS16 NLO  [35] , nNNPDF1.
Trigger and reconstruction efficiency In the analysis described below, muon pairs are corrected for trigger and reconstruction efficiency by application of a per-pair weight to data events: 𝑊 = 1 𝜀 trig 𝜀 rec 1 𝜀 rec 2 𝜀 vtx , where  trig is the pair trigger efficiency,  rec 1 and  rec 2 are the single-muon reconstruction efficiencies, and  vtx is the vertex-pointing efficiency. Since all dimuons used in this analysis are back-to-back in azimuth and are thus well-separated in the detector, the efficiency of the dimuon triggers is evaluated using independent, single-muon trigger efficiencies. The single-muon trigger efficiencies are evaluated by testing whether offlinereconstructed muons passing the preselections described in Section 3 are matched to a muon found by the trigger. These efficiencies are evaluated as a function of muon  T and of the product of the muon charge and pseudorapidity,.
Differential distributions Figures  7  and 8  show, for several centrality intervals, differential dimuon yields versus  and  ⊥ , respectively, for the production of dimuons satisfying the corresponding fiducial selection. The distributions are plotted over ranges that extend beyond the fiducial limits in order to emphasize the behavior of the HF-decay background. Also shown on the plots are the background levels represented by the constants,   HF and   ⊥ HF , together with statistical uncertainties of the constants resulting from the template fit. The backgrounds provide a good description of the behavior of the  and  ⊥ distributions at large  and  ⊥ , although the data show a slight, centrality-dependent [MeV] k 20 40 60 ] -1 [MeV k d N d ATLAS -1 Pb+Pb 5. 02 TeV, 1. 94 nb 0-5% excess over those HF background estimates.
Conclusion ATLAS has measured dimuon production via  scattering processes in non-ultraperipheral Pb+Pb collisions at √  NN = 5. 02 TeV. The measurements use data from the 2015 and 2018 Pb+Pb runs at the LHC corresponding to an integrated luminosity of 1. 93 nb -1. Backgrounds, dominated by heavy-flavor decays, are evaluated using template fits to the distribution of muon-pair  0 values. A much smaller background from DY processes, estimated using P +P 8 calculations implemented with nNNPDF2. 0 nuclear PDFs, is subtracted from the data. Cross-sections and normalized yields of  →  +  -pairs are measured as a function of pair rapidity, pT , and centrality. The cross-sections vary weakly with centrality, decreasing from central to peripheral collisions. The STAR model, which was recently augmented to allow evaluation of cross-sections for (e.
Similarly, no dependence of the standard deviation of the  ⊥ distributions on the orientation of the muon pair relative to the second-order event plane is observed. This result would be inconsistent with magnetic broadening in a field whose direction in the transverse plane is correlated with the impact parameter vector. While the overall broadening of the distributions does not depend on |Δ| or |2Δ|, the magnitude of the depletion at small  ⊥ appears to depend on both quantities. of America. In addition, individual groups and members have received support from BCKDF, CANARIE, Compute Canada and CRC, Canada; PRIMUS 21/SCI/017 and UNCE SCI/013, Czech Republic; COST, ERC, ERDF, Horizon 2020 and Marie Skłodowska-Curie Actions, European Union; Investissements d'Avenir Labex, Investissements d'Avenir Idex and ANR, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF, Greece; BSF-NSF and MINERVA, Israel; Norwegian Financial Mechanism 2014-2021, Norway; NCN and NAWA, Poland; La Caixa Banking Foundation, CERCA Programme Generalitat de Catalunya and PROMETEO and GenT Programmes Generalitat Valenciana, Spain; Göran Gustafssons Stiftelse, Sweden; The Royal Society and Leverhulme Trust, United Kingdom.
Acknowledgments We thank  CERN  for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of  ANPCyT , Argentina; YerPhI, Armenia;  ARC, Australia ;  BMWFW  and  FWF , Austria; ANAS, Azerbaĳan;  CNPq  and  FAPESP, Brazil ;  NSERC ,  NRC  and  CFI ,  Canada ; CERN;  ANID, Chile ;  CAS ,  MOST  and  NSFC, China ;  Minciencias , Colombia; MEYS CR, Czech Republic; DNRF and  DNSRC, Denmark ;  IN2P3-CNRS  and  CEA-DRF/IRFU , France; SRNSFG, Georgia; BMBF,  HGF  and  MPG , Germany; GSRI, Greece; RGC and  Hong Kong SAR, China ;  ISF and Benoziyo Center, Israel ;  INFN, Italy ;  MEXT  and  JSPS, Japan ; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MEiN, Poland; FCT, Portugal; MNE/IFA, Romania; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DSI/NRF, South Africa; MICINN, Spain;  SRC and Wallenberg Foundation, Sweden ;  SERI ,  SNSF  and  Cantons of Bern and Geneva, Switzerland ; MOST, Taiwan; TENMAK, Türkiye; STFC,  United Kingdom ;  DOE  and  NSF , United States A.
Context. Radio used as a star formation rate (SFR) tracer presents enormous advantages by being unaffected by dust and radio sources being pinpointed at the sub-arcsecond level. But the interpretation of the low frequency 1. 4 GHz luminosity is hampered by the difficulty in modeling the cosmic ray paths in the interstellar medium, and their interactions with the magnetic field. Aims. In this work, we compare the SFR derived from radio observations, and the ones derived from spectral energy distribution (SED) modelling. We aim at better understand the behavior of the SFR radio tracer, with a specific emphasis on the link with star-formation histories. Methods. The analysis is based on a sub-sample of 1584 star-forming galaxies extracted from the COSMOS VLA 3 GHz survey project. We used the SED modelling code Code Investigating GALaxy Emission, CIGALE with a non-parametric star formation history model (SFH) and fit the data over the wavelength range from the ultraviolet (UV) up to the mid-infrared (mid-IR).
The article is organized as follows. Sect. 2 describes the data set employed in this work which consists of a seminal galaxy sample from the work of  Jiménez-Andrade et al. (2019). This sample includes several physical parameters, such as the radio SFR indicator obtained through the infrared-radio correlation. In Sect. 3, we present the Code Investigating GALaxy Emission, CIGALE which has been employed to recover the star-forming properties from broad and narrow-band SED modelling, this in a wavelength range coming from the NUV, up to the FIR. Sect. 4 illustrates the results from the data analysis to eventually discuss and conclude in the last section. We adopt the standard ΛCDM cosmology with Ω m = 0. 3, Ω Λ = 0. 7 and H 0 = 70 km s -1 Mpc -1. We use the initial mass function (IMF) from  Chabrier (2003).