dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments: We thank  Riccardo Zucca  and  Sytse Wierenga  for help with graphics. This work has been supported by the  European Research Council 's  CDAC  project: " The Role of Consciousness in Adaptive Behavior: A Combined Empirical, Computational and Robot based Approach " ( ERC-2013-ADG 341196 ). RS acknowledges the support of the  Secretaria d'Universitats i Recerca del Departament d'Economia i Coneixement de la Generalitat de Catalunya , the  Botin Foundation , by  Banco Santander  through its  Santander Universities Global Division  and by the  Santa Fe Institute.
We present panco2, an open-source Python library designed to extract galaxy cluster pressure profiles from maps of the thermal Sunyaev-Zeldovich effect. The extraction is based on forward modeling of the total observed signal, allowing to take into account usual features of millimeter observations, such as beam smearing, data processing filtering, and point source contamination. panco2 offers a large flexibility in the inputs that can be handled and in the analysis options, enabling refined analyses and studies of systematic effects. We detail the functionalities of the code, the algorithm used to infer pressure profile measurements, and the typical data products. We present examples of running sequences, and the validation on simulated inputs. The code is available on github, and comes with an extensive technical documentation to complement this paper.
The algorithm is based on forward modeling of the tSZ signal and MCMC sampling, and allows users to account for different features of millimeter-wave observations that may manifest as systematic biases or uncertainties in recovered pressure profiles. An earlier version of panco2 was described in  Kéruzoré et al. (2022) , which offered less flexibility in the analysis, as the only data that could be analyzed was maps from the NIKA2 camera 150 GHz channel  (Adam et al. 2018; Perotto et al. 2020). This software represents an evolution of the software developed within the NIKA and NIKA2 collaborations, and has already been used for different studies based on NIKA2 data (e. g. Artis et al. 2022; Muñoz-Echeverría et al. 2022a,b). Here, we present a generalization of the code, that makes it able to perform pressure profile extractions from arbitrary tSZ datasets.
This paper is structured as follows. We present the algorithm implemented in panco2 to estimate pressure profiles from tSZ maps in §2. In §3, we present a set of realistic simulated maps, mimicking observations by different millimeterwave instruments of mock clusters at different masses and redshifts, that we use to validate the accuracy of the pressure profiles recovered by panco2. In §4, we extend the validation dataset to incorporate more complex data features, such as point source contamination and anisotropic filtering. We discuss the caveats of panco2 and some possibilities for improvements in §5, and conclude in §6. Throughout this paper, even though panco2 can use different cosmological models, we assume a flat ΛCDM Universe, with Ω m = 0. 3, Ω Λ = 0. 7, h = 0. 7. This cosmology is mainly used to infer angular diameter distances to the cluster being studied from its redshift, in order to map sky distances to physical ones, as well as to generate mock pressure profiles using the cosmology-dependent universal profile of A10.
In panco2, we perform this integration analytically, by following the derivation presented in Appendix A of  Romero et al. (2018) , using the spherically symmetric case. This allows us, for any given pressure profile, to create a Compton parameter map in the same coordinate system as the data, i. e. an estimate of the value of y for each pixel in the map. Conversion and zero level -Depending on the data product available, and on the convention used in the raw data processing software employed to create these data products, tSZ maps can be expressed in a variety of units (e. g. surface brightness, CMB temperature fluctuation). These units can usually be converted to Compton-y through a scalar conversion coefficient, C conv , which can depend on many different quantities that may be difficult to estimate, or even fluctuate during observations, such as instrumental bandpasses, weather conditions, instrumental calibration, or even temperature of the ICM through relativistic corrections to the tSZ effect  (Mroczkowski et al.
Posterior distribution and MCMC sampling panco2 performs the extraction of a pressure profile from tSZ data using Bayesian MCMC. In that framework, a prior probability distribution for the parameters, p(ϑ), must be multiplied to the likelihood function of eq. (  3 ) to obtain a posterior distribution for the parameters given the data: p(ϑ|D) ∝ p(D|ϑ) p(ϑ). In panco2, the prior distributions for the different model parameters are considered uncorrelated, meaning the prior distribution is the product of the individual priors on parameters: p(ϑ) = i p(ϑ i ), (6) where the product runs over all individual parameters i. The prior on each parameter is to be specified by the user, using the large variety of distributions available in the scipy. stats 3 module (Virtanen et al. 2020). The resulting posterior distribution, p(ϑ|D), is then sampled using MCMC.
The angular resolutions of the Planck and SPT y-maps, as well as that of the NIKA2 camera at 150 GHz, are represented as dotted horizontal lines. maps built from Planck and SPT data have angular resolutions (expressed as the full width at half maximum, or FWHM) of 10 and 1. 25 , respectively  (Planck Collaboration et al. 2016b; Bleem et al. 2022) , and the beam of the NIKA2 camera 150 GHz band -used for tSZ mapping -has an FWHM of 18  (Perotto et al. 2020). We choose to create SZ maps for three clusters, labeled (C1, C2, C3), covering different regions of the mass-redshift plane: C1 : z = 0. 05, M 500 = 9 × 10 14 M ; C2 : z = 0. 5, M 500 = 6 × 10 14 M ; C3 : z = 1, M 500 = 3 × 10 14 M , (7) These mock clusters are shown as black stars in Figure  3. The top panel shows their positions in the mass-redshift plane, indicating that C1, C2 and C3 are realistic detections for the Planck, SPT, and ACT tSZ surveys, respectively.
Data generation As discussed previously, and illustrated in the bottom panel of Figure  3 , the angular size of the chosen clusters varies from a few arcminutes to several tens of arcminutes, thus their resolved mapping is not achievable by the all the instruments. We therefore create different sets of maps for the three clusters. For C1, the most extended cluster, we create mock Planck and SPT maps. For C2, our intermediate case, we create SPT and NIKA2 mock maps. Finally, for C3, the smallest cluster in our sample, we only create mock NIKA2 data. The characteristics of the maps are designed to mimic known cluster observations by the three instruments, and summarized in table 1. The generated maps are shown on the left panels of Figure  4. The original data products for this map are in healpix format, which panco2 cannot process, as it relies on the flat-sky approximation.
The noise in these NIKA2 maps is considered white and isotropic, but not homogeneous, as the scanning strategy used for observations of the NIKA2 SZ Large Program creates a variation in the noise level of the maps depending on the distance from the pointing center. To accurately take into account this effect, we use the noise RMS map of K20, which we multiply by a scalar value to account for different exposure times. Data produced by the NIKA2 collaboration is in NIKA2 150 GHz surface brightness units, usually presented in mJy/beam. The conversion coefficient of y-to-mJy/beam can be evaluated by integrating the tSZ spectral distortion in the NIKA2 150 GHz effective bandpass (accounting for atmospheric opacity at the time of the observations), and is usually of the order of -12 mJy/beam/y, therefore we choose this value for our map generation.
Results The results of the regression is presented in Figures  4  and 5. Figure  4  shows, for each of the five datasets, the simulated data (left), best-fitting 2D model (center), and the residuals (right). The residual maps contain no significant structure, providing a visual indication of goodness of fit. The left panels of Figure  5  also shows the data, model, and residuals, but as 1D azimuthal profiles, and include uncertainty computed from the spread of the sampled posterior. The compatibility of the green curves (i. e. residuals) with zero within uncertainties proves that panco2 is able to retrieve a pressure profile that fits the data. To assess goodness of fit, we also compute reduced chisquared values, defined as: χ2 = - 2 k log L(ϑ b. f. ), (9) where the log-likelihood log L is defined in eq.
The power spectrum of the residuals is nearly indistinguishable from that of the noise, showing that the anisotropic filtering in the input data does not prevent the recovery of a tSZ model map, as long as this filtering is known and accounted for. In addition, the best-fitting reduced chi-squared reported in table 2 also indicates goodness of fit. We generate a mock NIKA2 map of the C2 cluster in which point sources are added to the surface brightness map. We choose to add two point sources S1 and S2, each with a flux of F = 1 mJy, and respectively located at 30 and 75 from the cluster center. They are added to the sky model with their true positions, and fluxes are treated as a model parameter as described in §2. 3.
Joint analysis of several tSZ maps -We have shown in §3 that panco2 could recover pressure profiles from tSZ observations with different kinds of instruments -namely, we have tested Planck-, SPT-, and NIKA2-like mappings. But our analysis treated all of these datasets separately. For clusters with available data from several instruments, there may be interesting information to be extracted from the joint analysis of several maps (see e. g. Ruppin et al. 2018; Romero et al. 2018). Different profile parametrizations -As discussed in §2. 2, many studies of pressure profiles from tSZ (or X-rays) observations use the gNFW profile. Even though our radiallybinned parametrization offers more flexibility and circumvents the known problems of the usual gNFW parametrization, one may be interested in using other functional forms of pressure profile.
We have implemented functions in panco2 that allow users to easily simulate a mock tSZ map mimicking the data to be fitted. We encourage users to use this functionality to create such datasets and try out their choices of radial binning on these. Similarly to the validation presented in §3. 4, the comparison of the pressure profile reconstructed with the one used to generate the map will provide insights on how well adapted a choice of radial binning is to a dataset. How to perform such an analysis is presented in the accompanying technical documentation. Investigations towards the systematic impact of radial binning on recovered pressure profiles will be the subject of a future study. 6. CONCLUSION This paper presents the release of panco2, a software allowing its users to perform pressure profile extraction from a tSZ map.
We have highlighted the main features of the software and its algorithm, based on forward modeling MCMC of the tSZ signal with a radially-binned pressure profile. We have presented the variety of observational systematics that can be incorporated in the modeling in order to account for the known features of millimeter-wave observations. We have validated the software and its different functionalities on realistic simulated maps of different clusters. Our main conclusions are as follow. • Using realistic simulations, we have shown that panco2 could be used to recover accurate pressure profiles from different projections of tSZ mappings. In particular, §3 showed validation on three clusters, covering different parts of the mass-redshift plane, mapped to mimic the Planck y-map of Planck Collaboration et al.
(  2016a ), the SPT y-map of B22, and realistic NIKA2 150 GHz observations, effectively covering a range of angular resolutions from 18 to 10. • We have demonstrated the possibility to include correlated noise ( §4. 1) and anisotropic filtering ( §4. 2) in the analysis, and still recover accurate pressure profiles. • We have shown two different ways to account for point source contamination in panco2, by masking the sources or marginalizing over their contamination ( §4. 3). The combination of all these features and their validation makes panco2 a flexible and robust software, capable of retrieving accurate pressure profile estimations from a large variety of tSZ maps.
Products released The entirety of the panco2 software is made available as a github repository: https://github. com/fkeruzore/panco2. The software version described in this work is v1. 0. That same repository also includes the data generated for the validation described in §3 and §4, along with the products of said validation (i. e. the Markov chains and the running sequences used for each dataset). In addition, panco2 is also accompanied by an online technical documentation at: https://panco2. readthedocs. io. It includes detailed explanations of the technical aspects of the code, as well as a description of the inputs and outputs of panco2's different functions. The documentation also gives examples of analyses that can be performed with panco2 beyond the ones presented in the validation.
ACKNOWLEDGEMENTS We thank  Lindsey Bleem  for useful discussions and advice, in particular regarding SPT-like data. The development of panco2 comes after many years of work on pressure profile fitting codes within the  NIKA  and  NIKA2  collaborations, for which we thank the NIKA2 collaboration, and acknowledge major contributions by  Rémi Adam ,  Charles Romero , and  Matthew McWilliam. Argonne National Laboratory 's work was supported by the  U. S. Department of Energy, Office of Science, Office of High Energy Physics , under contract  DE-AC02-06CH11357. This work is supported by the  French National Research Agency  in the framework of the " Investissements d'avenir" program  ( ANR-15-IDEX-02 ).
Since we are proposing a decentralized approach based on MASs, the communication aspect between agents should not be at all neglected. First, we start by defining briefly the different types of agents that we propose. (1) Consumer, (2) prosumer, (3) EPS, (4) battery, and (5) grid are our five types of agents. We represent in the right bottom of Fig. 2  the icons adopted for each type of agent and we add next to each one in the architecture its corresponding icon. We assume that each agent is equipped with a Raspberry Pi (RPI) on which its behaviour is implemented and executed. Moreover, in order to allow agents communicating with each other, we connect to each RPI a Low Range Wide Area Network (LoRaWAN) module through the universal serial bus (USB) port. In this way, all agents (which are represented by RPIs) will be able to communicate, exchange messages, and cooperate with each other to achieve their goals.
Simulation setup In order to evaluate our approach, 20 households of a residential area are used. All of them are located in the city of Loughborough in the United Kingdom. Detailed information about households (e. g. , number of occupants, consumption details, smart appliances, type of RES installed) can be found in  Murray et al. (2017). Table  2  provides the coordinates of each household in the community and his respective cluster affiliation, while Table  3  displays the coordinates of the EPSs installed and their associated clusters. The consumption patterns of these households are obtained from REFIT, a public dataset available to download from University of Strathclyde (2021). The dataset covers the period between September 2013 and June 2015. Fig. 3  depicts the consumption profiles of 12 households in the community.
Therefore, monetary exchanges between neighbors are not included in the bill. Including these exchanges would involve adding them as costs for consumers and subtracting them as revenues for prosumers, effectively resulting in no impact on the overall calculations. We conducted our simulations on a MacBook Air, that has 4 GB of RAM and a 1. 4 GHz Dual-Core Intel Core i5 CPU. We developed our code using Java Agent Development Framework (JADE), the most famous framework for the development of MASs  (Bellifemine et al. , 2008) , and we executed it on Eclipse, an open source environment.
• Compared EMICO with a literature work, demonstrating significant improvements in energy losses, electricity bills, message exchanges, and required cable length (up to 35. 66%, 21. 21%, 83. 81%, and 95. 03% reduction, respectively). Moving forward, our future research directions encompass: • Continuing simulations on different community datasets to explore the applicability of EMICO in diverse scenarios. • Investigating optimal placement strategies for the shared BESS within residential communities to further minimize energy losses. • Proposing strategies for agents to negotiate energy prices and prioritize specific agents based on specific circumstances.
Finally, if the number of simulations is large enough, one can make use of machine learning techniques to extract the maximum amount of information from the simulations while at the same time being able to develop high-dimensional interpolators to explore the parameter space without having to run additional simulations. The Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project  (Villaescusa-Navarro et al. 2021c ) was conceived to combine cosmology and astrophysics through numerical simulations and machine learning. At its core, CAMELS consists of a set of 4,233 cosmological simulations that have different values of the cosmological parameters and different astrophysical models. All these virtual universes can be used as a large dataset to train machine learning algorithms.
Organization The CAMELS simulations are divided into three different suites: • IllustrisTNG. All simulations run with the AREPO code and employing the IllustrisTNG model belong to this suite. There are 1,092 Il-lustrisTNG simulations in CAMELS. • SIMBA. All simulations run with the GIZMO code and employing the SIMBA subgrid model belong to this suite. There are 1,092 SIMBA simulations in CAMELS. • N-body. All N-body simulations belong to this suite. There are 2,049 N-body simulations in CAMELS. We provide further details on each suite below. Each simulation suite contains four different sets, depending on the way the values of the cosmological parameters, astrophysical parameters, and initial conditions random phases are organized: • LH stands for latin-hypercube. This set contains 1,000 simulations, each with different values of Ω m , σ 8 , A SN1 , A SN2 , A AGN1 , A AGN2 , and the initial conditions random phases.
The differences reflect the different subgrid models employed in these two simulations. The structure and contents of the IllustrisTNG snapshots are the same as in the publicly released IllustrisTNG simulation data  (Nelson et al. 2019). Likewise, the SIMBA snapshots are identical in format to that available on the publicly released SIMBA database 3. The snapshots are stored as hdf5 files, and we provide details in the online documentation on how to read and manipulate the data from them.
Void catalogues We release void catalogs built from the CAMELS simulations with the Void IDentification and Examination toolkit (VIDE)  Sutter et al. (2015). VIDE, based on ZOBOV  (Neyrinck 2008) , has been widely used to find voids both in data-e. g. voids from the SDSS BOSS  (Hamaus et al. 2016; Hamaus et al. 2020 ) and eBOSS  (Aubert et al. 2020)  datasets, or data from DES  (Pollina et al. 2019 )and simulations (e. g. Kreisch et al. 2019; Verza et al. 2019; Contarini et al. 2021; Kreisch et al. 2021 ). Furthermore, VIDE has also been applied to hydrodynamic simulations  (Habouzit et al. 2020; Panchal et al. 2020) , showing its suitability for the CAMELS dataset. VIDE was run on top of CAMELS galaxies, that were defined as subhalos containing more than 20 star particles. Given the size of the CAMELS simulations, and the extended size of cosmic voids (that usually span sizes from 5 -100 h -1 Mpc), the number of voids for each CAMELS simulation is relatively small.
Lyman-alpha spectra We release mock Lyman-α spectra generated using a public, well-tested code exhibited in  Bird et al. (2015)  and used previously for studies of the Lyman-α Forest in  Gurvich et al. (2017). The spectra is generated for 5,000 sightlines randomly placed through the simulation box. This spectral data was generated for the IllustrisTNG and SIMBA suites for all simulation sets at all redshifts. The locations of the random sightlines vary across snapshots. The total absorption along a sightline is the sum of the absorption from all the nearby gas cells. The simulated spectra has a spectral resolution of 1 km/s and any lines with an optical depth of τ < 10 -5 are neglected. For further details on the artificial spectra calculation, we direct the reader to  Bird et al. (2015). In the online documentation we provide details on how to read and manipulate the Lyman-α spectra.
Probability distribution function We estimate probability distribution functions (PDF) for 13 different physical fields using the 3D grids of the CAMELS Multifield Dataset (CMD) (see  Sec. 3. 8). The PDFs are calculated for all the fields: 1) gas temperature, 2) gas pressure, 3) neutral hydrogen density, 4) electron number density, 5) gas metallicity, 6) gas density, 7) dark matter density, 8) total mass density, 9) stellar mass density, 10) magnetic fields, 11) ratio between magnesium over iron, 12) gas velocity, and 13) dark matter velocity, for all the grid sizes, i. e. , 128, 256 and 512 at redshifts 0. 0, 0. 5, 1. 0, 1. 5, and 2. 0. The PDFs are calculated as follows. First, the 1,000 3D grids from all simulations in the LH set are read into memory. We then calculate the minimum value across grids and if it equals 0, a small offset is added to all voxels of all grids.
Profiles We provide three-dimensional spherically-averaged profiles of gas density, thermal pressure, gas massweighted temperature, and gas mass-weighted metallicity for the 1P, LH, and CV sets of both the Illus-trisTNG and SIMBA suites. We follow  Moser et al. (2021)  in extracting halo information and construction of the profiles. Specifically, we use illstack_CAMELS 5 (a CAMELS-specific version of the original, more general code illstack used in  Moser et al. 2021) , to generate the three-dimensional profiles, extending radially from 0. 01 -10 Mpc in 25 log 10 bins. The profiles are stored in hdf5 format which can be read with the python script provided in the illstack_CAMELS repository.
X-rays We provide mock X-ray photon lists in the form of SIMPUT fits files for all halos above 10 12 M across all hydrodynamic CAMELS runs at redshift z = 0. 05 obtained from the snapshot 032. The SIMPUT files are generated using the pyXSIM package 6  and contain positional coordinates in RA and DEC coordinates and energy in units of keV. These files serve as inputs into other software packages, including SOXS 7  and SIXTE  (Dauser et al. 2019 ) that generate mock observations for specific telescopes using custom instrument profiles. These SIMPUT files can also represent idealized observations by an X-ray telescope, and we also provide a single collated file with 1-dimensional projected surface brightness (SB) profiles for all halos for the soft X-ray band (0. 5-2. 0 keV) in units of erg s -1 kpc -2.