dataesr/scientific-paragraphs-categorization huggingface dataset:
As a first application, in this paper we test FORECAST by forward modeling the ILLUS-TRISTNG100 simulation  (Weinberger et al. 2018; Pillepich et al. 2018b; Nelson et al. 2019 ) and creating a dataset that mimics the observational properties of the GOODS-South field, as observed by the CANDELS survey  (Grogin et al. 2011; Koekemoer et al. 2011) , and using CANDELS  (Guo et al. 2013 ) and ASTRODEEP-GS43 catalog  (Merlin et al. 2021 ) for our comparisons. This simulated dataset is publicly available 2  , together with a JWST CEERS-like dataset and the FORECAST code. The paper is organized as follows. In Sect. 2, we describe the methods implemented in FORECAST to forward model the simulated data. The synthetic dataset produced to test the code is described in Sect. 3. The results of the photometric analysis of our synthetic images are discussed in Sect.
3. 2. In Sect. 4, we present the public release of the FORECAST code along with an additional dataset of synthetic images emulating the JWST CEERS survey. Finally, in Sect. 5 we summarize the main points of our work and discuss possible future work. All the magnitudes are defined in the AB magnitude system  (Oke 1974) , with fluxes in units of µJy, namely m AB = -2. 5 log 10 ( f ν ) + 23. 9. We adopt the flat ΛCDM Cosmology constrained by Planck Collaboration XIII (2016), with Ω Λ,0 = 0. 6911, Ω m,0 = 0. 3089, Ω b,0 = 0. 0486, σ 8 = 0. 8159, n s = 0. 9667; and we use the Hubble constant in terms of h ≡ H 0 /100 km s -1 Mpc -1 = 0. 6774.
Description of the software In this section, we provide a description of the algorithms included in FORECAST to build the synthetic images (Sect. 2. 1) and to add realistic observational features (Sect. 2. 2). A102, page 2 of 19  Fortuni, F. , et al. : A&A, 677, A102 (2023)   FORECAST uses a particle-based approach to reconstruct the observable photometric properties of galaxies within the field of view. The reconstruction is based on the properties of individual resolution elements in the simulation, represented by stellar particles. Each stellar particle corresponds to a single stellar population (SSP) and collectively forms the simulated objects, representing galaxies in the field of view. In short, FORECAST reads the physical properties of the stellar particles from the output snapshots of a chosen simulation and translates them into observable quantities as follows.
Light-cone construction Simulation output data are organized in snapshots, which are photographs of the simulated cosmological volume at a specific time of its evolution. FORECAST creates a light cone placing an observer at z = 0 and rearranging the data from the output snapshots of the chosen simulation, projecting the positions of the simulated objects on a two-dimensional field of view. The light-cone construction procedure is inherited from the software MapSim by  Giocoli et al. (2015). The snapshots used to build the light cone must include the following properties for the stellar resolution elements: -comoving coordinates within the simulated volume, (x * ,y * ,z * ) in ckpc -stellar mass, M * in M ⊙ -initial stellar mass, M i, * in M ⊙ -stellar metallicity Z * as M Z /M TOT -age, t SSP in yr -redshift, z * -subhalo membership ID The initial stellar mass is the amount of mass owned by a stellar element when it is born, while the stellar mass accounts for mass returned through winds and supernovae to the ISM by evolved stars.
FORECAST constructs deep light cones stacking simulation boxes, using different snapshots to cover the entire chosen redshift interval (partitions of the light cone). When the length of the simulation box along the z-axis, which is the order of a few hundred Megaparsecs in typical cosmological hydrodynamical simulations, is smaller than the distance between two subsequent snapshots, FORECAST adds a replica of the previous or following snapshot, tailoring it to fill the gap. The software replicates the snapshot with the closest redshift to the redshift of the midpoint of the gap. This operation ensures a seamless construction of the complete light cone without any missing sections. Each snapshot is adjusted with rotated, inverted and shifted coordinates to pick structures at an evolutionary stage as close as possible to the one they would be if the entire redshift range was continuously sampled by simulation snapshots.
Adding realism: Noise and PSF Here we describe the two final steps necessary to simulate a real mosaic, which are the inclusion of observational noise and the convolution with a Point Spread Function of the sources of interest. These two steps are independently performed with a python script and can be executed multiple times on the ideal images produced by FORECAST to simulate them with different depths. When FORECAST assembles its output in a chosen band, the simulated image is noiseless and its resolution is only limited by the pixel scale. First of all, as mentioned in Sect. 3. 1, we convolve it with the PSF of the instrument and filter that is being simulated; this spreads the flux coming from a single pixel over an extended region. Then, we add a noise background that limits the depth of the image.
Testing FORECAST: Emulation of the CANDELS GOODS-South Field Before exploiting the tool to make forecasts for the nextgeneration surveys, we tested its capabilities emulating a wellknown dataset, to compare the new mock data with existing photometric catalogs, investigating which are the most relevant tensions with observed data, and how the procedure can be improved for future work. These data products, together with the A102, page 7 of 19  Fortuni, F. , et al. : A&A, 677, A102 (2023)  JWST CEERS dataset (see Sect. 4), are publicly available for scientific analysis. In Sect. 3. 1, we illustrate the procedure adopted to build the images and catalog analyzed in this work. We describe the cosmological hydrodynamical simulation employed to build the images in Sect. 3. 1. 1. We then describe the mock light-cone setup adopted for the present analysis and give an overview of the simulated images in Sect.
Constructing the images In order to examine the performance of the software tool, we tested the synthetic images against a thoroughly investigated observational counterpart. To this aim, the size and filter set of the simulated FoV and the extension in redshift of the mock light cone have been chosen to emulate the Great Observatories Origins Deep Survey Field South (GOODS-South, GS), making use of the state-of-the-art hydrodynamical simulation ILLUSTRISTNG. The GS field, located at RA = 3 h 32 m 30. 39 s and Dec = -27 • 48m 11. 28s with a covered region of 10 ′ × 16 ′ , has been targeted for deep, multiwavelength observations from ground and from space as part of several survey programs (e. g. ,  Grogin et al. 2011; Brammer et al. 2012; Curtis-Lake et al. 2023; Robertson et al. 2023). We elect to produce thirteen images corresponding to 13 band-passes covering from the rest-frame optical to the nearinfrared (NIR) wavelength range: HST ACS B435, V606, I814, Z850, and WFC3 Y105, J125, JH140, and H160; plus a groundbased VLT HAWK-I K s band, and four IRAC channels, namely CH1, CH2, CH3, and CH4.
2018b ). The ILLUSTRISTNG dataset has been publicly released with a complete user guide in  Nelson et al. (2019). In each output snapshot of the simulation, overdensities of dark matter are identified using the Friends-of-Friends algorithm  (Davis et al. 1985) ; self-bound subhalos, which are primarily constructed with dark matter particles, and have their baryons (i. e. , gas and stars) associated with the geometrically nearest DM particle, are later identified using the SUBFIND algorithm for substructures identification  (Springel et al. 2001; Dolag et al. 2009). They also generate two distinct merger trees at the subhalo level, with SUBLINK (Rodriguez-Gomez et al. 2015) and LHALOTREE  (Springel et al. 2005)  algorithms. This work makes use of the TNG100-1 simulation boxes (L = 75 h -1 Mpc = 110.
Photometric analysis In order to validate the accuracy of the simulated field, we proceeded using a processing pipeline that is very similar to the one typically used to extract the photometric information, and then the scientific properties, from real imaging data. We have only bypassed the reduction processing steps of a typical raw imaging dataset (e. g. , flat-fielding, bias and background subtraction, mosaicing, etc. ), assuming they have been performed in an ideal way. We also did not extract new PSF models from the images, exploiting the ones used to build the simulated images. Therefore, we start our analysis from a simulated image that is comparable to the final mosaic on which the CAN-DELS team performed the photometric measurements for the final catalogs. Specifically, for our analysis and comparisons, we used our multiwavelength photometric catalog ASTRODEEP-GS43, which is an upgrade of the CANDELS catalog by  Guo et al.
However, the resulting counts were substantially similar, both in the IU and in the detections. Then, to check whether the problem could originate from the input catalog or from the detection procedure, we created a new realization of our H image, including only sources with true magnitude in the relevant range, to avoid contamination and blending with brighter sources. We compared this tailored simulated image (which we tag as "reference IU") with a similar image simulated with GALSIM  (Rowe et al. 2015) , which we fed with a mock galaxy catalog produced with the EGG software  (Schreiber et al. 2017) , again only including galaxies of the same input magnitude range. EGG catalogs are based on empirical relations calibrated on the observed CANDELS data, extrapolated to faint magnitudes; we created a catalog with a faint limiting magnitude (H = 31) to ensure input completeness in our considered magnitude range.
Estimated physical properties We finally checked the accuracy in the estimates of the redshift and the stellar mass of the detected galaxies. To this aim, we performed a SED-fitting procedure with the code ZPHOT  (Fontana et al. 2000) , adopted in many studies (e. g. ,  Castellano et al. 2016; Santini et al. 2015; Merlin et al. 2021 ). We Fig. 8. Results of the SED-fitting photometric redshifts estimate using ZPHOT. The plot shows the distribution of the redshifts of the sources with a IU match, both estimated with ZPHOT (black shade) and from the input catalog (blue line); also shown is the distribution of a sample of sources from GOODS-South DEEP, taken from the ASTRODEEP catalog  (Merlin et al. 2021, yellow shade). The histograms are normalized to allow for easier comparison. use a library of template galaxy SEDs identical to the one used in  Merlin et al.
Data release of a JWST-like survey Together with the CANDELS mock observations, we have also produced and made public a second dataset, this time emulating JWST observations of the same FoV we presented in the previous section. The set of data consists of a galaxy catalog and ten astronomical images. To construct this dataset, we extended the light cone realized for the CANDELS emulation up to z s = 20, again exploiting the ILLUSTRISTNG simulation. The pixel scale is 0. 031 arcsec (the typical value of JWST short wavelength detectors), yielding images on a grid of ∼750 million pixels. We emulated ten JWST bands: eight from NIRCam (F090W, F115W, F150W, F200W, F277W, F356W, F410M, F444W), and two from MIRI (F560W, F770W). For the post-processing we used the PSF models provided by STScI in the WebbPSF webpage 4.
For this emulation, we also decided to change the synthetic stellar population model by adopting  Gutkin et al. (2016) , which includes rest-frame ultraviolet and optical nebular emission from HII regions in star-forming galaxies in a wide range of chemical compositions. The image in the JWST/NIRCam F090W filter is simulated with resolution and limit magnitude adopted by  Merlin et al. (2022)  for the JWST GLASS survey  (Treu et al. 2022). Additionally, the remaining seven broad-band images in NIR-Cam filters emulate the JWST CEERS survey  (Finkelstein et al. 2023) ; we created two images in the CEERS JWST/MIRI bands with resolution and depth adopted by  Papovich et al. (2023). We did not perform any analysis on this dataset, leaving it to future work. All the released data products are available on our website 5.
Future work will include (i) implementing additional effects in the light cone: adding Milky Way stars and local objects, Active Galactic Nuclei, the absorption due to Milky Way gas and dust, the effect of lensing; (ii) implementing options to allow for more flexibility, e. g. , giving the user the possibility to choose a preferential position to extract the light cone. We make the simulated CANDELS dataset publicly available. We also release a set of images simulated in ten JWST bands, and the corresponding Input Universe catalog containing simulated physical properties and simulated true fluxes of the galaxies. As new upcoming observational instruments will allow us to probe the Universe to an unexplored extent, numerical tools like FORECAST will help us to capture the significance of their exploration, improving the synergism between observations and theory.
The next few years will revolutionize our understanding of the Cosmos and will make us more aware about the Universe we inhabit. A102, page 17 of 19  Fortuni, F. , et al. : A&A, 677, A102 (2023)  Table A. 1: Fourth degree polynomial fit coefficients at z = 0. M HI M g = a 1 T 4 g + a 2 T 3 g + a 3 T 2 g + a 4 T g + a 5 z a 1 a 2 a 3 a 4 a 5 0 2. 31105e-02 -8. 91893e-01 1. 14885e+01 -6. 27073e+01 1. 18956e+02 In the input configuration file of the code, the user chooses the image simulation parameters (e. g. , the dimension of the FoV, the filters, the hydrodynamical simulation; see Sect. 2. 1. 1). The code requires the input files of the chosen hydrodynamical simulation to build the light cone and produce the final images. The data products of the numerous currently available simulations are organized differently, changing from one to another simulation, and are stored with different formats; as example ILLUSTRISTNG stores a single snapshot in multiple.
hdf5 files, while in the EAGLE simulation  (Schaye et al. 2015)  the snapshots are available for public download via an SQL web interface. Thus, the code requires these data products to be uniformed in a specific format in order to be easily read and processed. The code pipeline consists of four modules. The architecture of each module is not intrinsically parallel (e. g. , it does not exploit MPI protocols), but it has been designed to allow the user to independently run it on multiple snapshots to realize multiple light-cone partitions simultaneously. We release a beta version of the code, which can be read and improved by the scientific community with a request for access to its source through our website 17.
B. 0. 1. Pipeline The FORECAST code, currently available in beta version, is structured into four interconnected modules, where each module relies on the output of the previous one. The first and third modules require as input file the snapshots of a hydrodynamical simulation to extract relevant properties of the simulated stellar and gas resolution elements. Currently, the code reads input files in the ILLUSTRISTNG format. Users are required to convert their data into the TNG format (. hdf5 files with the same column names as TNG columns) to ensure the effective utilization of the code. Future updates to the code will include additional scripts to read input files from multiple hydrodynamical simulations in different formats, such as the EAGLE Project  (Schaye et al. 2015)  and the Simba Simulation  (Davé et al.
astrodeep. eu/FORECAST belonging to the sources included in the FoV. In output it is given the same file produced by the previous module, also including dust-corrected fluxes for stellar particles, and the gas massweighted mean of the gas metallicity and the neutral hydrogen column density. The final module adds the IGM correction to dust-corrected fluxes, producing the final output catalog, which includes the physical properties of the stellar particles and their corrected fluxes, and the mean properties of the gas. An independent C++ script handles the arrangement of the fluxes on a grid of pixels with N pix -per-side chosen by the user. Two additional independent scripts, written in python, are made available (i) to build the galaxy catalog, in ASCII format, from the particle catalog given in output by FORECAST (see Sect.
Acknowledgements. The IllustrisTNG simulations were undertaken with computational time awarded by the  Gauss Centre for Supercomputing (GCS)  under  GCS Large-Scale Projects GCS-ILLU  and  GCS-DWAR  on the GCS share of the supercomputer Hazel Hen at the  High-Performance Computing Center Stuttgart (HLRS) , as well as on the machines of the  Max Planck Computing and Data Facility (MPCDF) in Garching, Germany. We thank the  CINECA  award under the  ISCRA initiative , for the availability of high-performance computing resources and support. We thank the  INAF computing system PLEIADI , for the availability of high-performance computing resources and support. Carlo Giocoli  acknowledges support from the  PRIN-MIUR   2017   WSCC32 ZOOM-ING , the ASI no. 2018-23-HH. 0 , the  INAF  grant under the " Bando PrIN 2019 ", PI:  Viola   Allevato , the  INAF  theory grant 2022:  Illuminating Dark Matter using Weak Lensing by Cluster Satellites , PI:  Carlo Giocoli.
Hence, it is uncertain which tools users should apply to their system and what are the most urgent directions that developers should take in the future to improve existing capacities. This study conducted a systematic evaluation of COBRA-based tools for microbial communities using datasets from two-member communities as test cases. First, we performed a qualitative assessment in which we evaluated 24 published tools based on a list of FAIR (Findability, Accessibility, Interoperability, and Reusability) features essential for software quality. Next, we quantitatively tested the predictions in a subset of 14 of these tools against experimental data from three different case studies: a) syngas fermentation by C. autoethanogenum and C. kluyveri for the static tools, b) glucose/xylose fermentation with engineered E.
However, these types of raw data are rarely available along with their metadata. Therefore, only a few suitable candidate datasets were applicable to the surveyed modeling scenarios. We selected three datasets (Diender et al. [72] , Hanly and Henson  [73] , and Harcombe et al. [33] ), one dataset for each category of modeling tools to use as case studies to validate the predictive capabilities of the tools. Every case study represents a consortium of two species. To have an unbiased comparison, tools were not modified or augmented to revise their functionalities. Some tools were also available, but they were not quantitatively assessed since they were too specific to a particular application and not designed for general application. In these evaluations, two main points were addressed: the quality of the tool for modeling microbial communities, and the predictability of the community behavior by the tool when presented with data from simple test cases.
Colored squares with '*' indicate features of OptCom evaluated using the OptCom function from MICOM. Colored squares with '**' indicate that the evaluation is an assumption based on the given information. Scom: SteadyCom; MMT: Microbiome Modelling Toolbox. The tools/approaches are ordered by the year of the latest publication (e. g. , MMT). https://doi. org/10. 1371/journal. pcbi. 1011363. g002 MICOM has excellent user support since it has two channels available for discussions and support. cFBA only needs the installation of CBMPy  [75]. SteadyCom and MMT are already available as part of the COBRA Toolbox, whose installation is fast and easy. MICOM is easy to install as it only needs a sentence of code and the installation of COBRApy  [76]. OptCom was run using the 'OptCom' function from MICOM, and thus, they were equally evaluated.
Except for CASINO and RedCom where the source code is unavailable, the source code is accessible to all users. OptCom, MICOM, and MMT produce the same results over time. Opt-Com, cFBA, and MICOM produce consistent solutions without infeasibility or non-convergence issues. SteadyCom and MMT can lead, in some cases, to non-convergence solutions or infeasibilities. NECom was applied to model a co-culture of two species. OptCom and cFBA have been used to model small microbial consortia (up to 3). CASINO, MMT, and MICOM can be used to model large communities (gut microbiota). SteadyCom and RedCom can be applied to model larger microbial communities than OptCom and cFBA (� 9). OptCom toolbox used 'txt' files as input models that do not meet any community-level standard. cFBA, CASINO, SteadyCom, RedCom, MICOM, and NECom meet SBML or COBRA community-level standards for model input and output files.
org/10. 5281/zenodo. 8074832). OptCom, SteadyCom, and MICOM predicted that the acetate produced by C. autoethanogenum was entirely consumed by C. kluyveri (see fluxes in the online repository: https://doi. org/10. 5281/zenodo. 8074832. However, cFBA predicted acetate production by the co-culture (Fig  3B ) and acetate consumption by C. kluyveri. Yet, acetate production was lower compared to the acetate measured in chemostat (� 0. 73 mmol h -1 ). MMT did not predict the production of acetate. Ethanol was difficult to detect in chemostat (�0. 1 mM), as most of it was consumed by C. kluyveri. cFBA predicted more ethanol than in chemostat, and in OptCom, SteadyCom, MMT, and MICOM, ethanol was fully consumed by C. kluyveri. Butyrate production was only predicted by cFBA and MMT. Caproate production was over-estimated by all the tools except cFBA, which predicted a value comparable to the experiments, and MMT, which did not predict caproate production.
The use of FVA as an alternative method to compute fluxes with the existing tools and approaches could also lead to additional information on cross-feeding metabolites. ). From the qualitative assessment of all the dynamic tools, it was apparent that none were flawless (all features being good or excellent). There were, in fact, compromising aspects in many of the tools where tools were superior for some features while flawed for other features (see  Fig 4 and S2 Fig). For instance, when examining their availability, it was determined that dyMMM, DAPHNE, MMODES, and surfinFBA were good or excellent. Still, the tools were considered merely satisfactory when evaluating their interoperability, flexibility, and reproducibility, except MMODES. In addition, tools such as dyMMM, DFBAlab, μbial-Sim, and MMODES were easy to install and contained accessible and tractable dependencies, but did not score above satisfactory levels for the software being traceable (Fig  4 ).
Also, tools that were built incorporating existing COBRA frameworks, such as COBRApy or COBRA Toolbox, typically scored well for being user-friendly and following current standards because they assimilate the current community standards and acquire greater functionality from the existing infrastructure. All the dynamic tools received at least a satisfactory score for their interoperability except MCM, which got a poor score. This is due to MCM having difficulties handling some GEMs, especially those of eukaryotes with multiple compartments. Also, MCM utilizes an in-house convention when processing GEMs; thus, issues could arise when needing to employ the GEM in another tool or database. It is best that GEMs adhere to SBML standards  [81]. Most software except MCM and d-OptCom earned at least a satisfactory score for dependencies.