We present morphological classifications of over 41,000 galaxies out to photometric redshifts of ~2.5 across six square degrees of the Euclid Deep Field North (EDFN) from the Hawaii Twenty Square Degree (H20) survey, a part of the wider Cosmic Dawn survey.This repository contains the data released alongside the research publication "Galaxy Zoo: Cosmic Dawn -- morphological classifications for over 41,000 galaxies in the Euclid Deep Field North from the Hawaii Two-0 Cosmic Dawn survey" (Pearson et al., 2025). Please cite this paper (DOI to follow on publication) when using the data in this repository.The data consists of classifications made by volunteers through the Galaxy Zoo project on the Zooniverse citizen science platform, as well as predicted classifications made by a deep learning model, Zoobot. Also included are the subject images themselves (in PNG/JPG format), a file of metadata for each subject, and a .csv file of all the tags assigned to the subjects by volunteers. Most files are given in .parquet format, which is a fast csv-like format that can be read in with the pandas Python package as DataFrames using pd.read_parquet(loc, columns=[]).A Jupyter notebook gzcd_info_and_example_usage.ipynb is included which provides more details on the data set and examples of how to use it.

We present morphological classifications of over 41,000 galaxies out to photometric redshifts of ~2.5 across six square degrees of the Euclid Deep Field North (EDFN) from the Hawaii Twenty Square Degree (H20) survey, a part of the wider Cosmic Dawn survey.This repository contains the data released alongside the research publication "Galaxy Zoo: Cosmic Dawn -- morphological classifications for over 41,000 galaxies in the Euclid Deep Field North from the Hawaii Two-0 Cosmic Dawn survey" (Pearson et al., 2025). Please cite this paper (DOI to follow on publication) when using the data in this repository.The data consists of classifications made by volunteers through the Galaxy Zoo project on the Zooniverse citizen science platform, as well as predicted classifications made by a deep learning model, Zoobot. Also included are the subject images themselves (in PNG/JPG format), a file of metadata for each subject, and a .csv file of all the tags assigned to the subjects by volunteers. Most files are given in .parquet format, which is a fast csv-like format that can be read in with the pandas Python package as DataFrames using pd.read_parquet(loc, columns=[]).A Jupyter notebook gzcd_info_and_example_usage.ipynb is included which provides more details on the data set and examples of how to use it.

Posterior samplesPosterior samples from the photodynamical modeling.Filename: K2-19_Almenara2025_posterior.csvColumns:    G:  N-body gravitational constant (au^3 day^-2 M_Sun^-1)    Tref: Reference time for the orbital elements (BJD_TDB)    timestep: N-body time step (days)    coordinate: Coordinate system    m_s: Star mass (M_Sun)    r_s: Star radius (au)    m_p: Planet mass (M_Sun)    r_p: Planet radius (au)    a_p: Planet's semimajor axis (au)    e_p: Planet's eccentricity     i_p: Planet's orbital inclination (degrees)    w_p: Planet's argument of pericentre (degrees)    Node_p: Planet's longitude of the ascending node (degrees)    M_p: Planet's mean anomaly (degrees)'p' is the planet number, ranging from 1 (the innermost planet) to n (the outermost planet):    1: K2-19 d    2: K2-19 b    3: K2-19 c    4: candidate K2-19 eTransit forecastTransit timing predictions derived from the photodynamical modeling.Filename: K2-19_Almenara2025_forecast2035.csvColumns:     1. Planet number (see the description of the posterior file)    2. Epoch number of the transit    3. Transit time (BJD_TDB)     4. Lower 68.3% credible interval (days)    5. Upper 68.3% credible interval (days)Light curves Transit observations of K2-19. Filename: K2-19_[Mid-transit UT date or start date for K2 and TESS][Telescope][Band].txtColumns:     1. Mid-Exposure Time (BJD_TDB)    2. Normalized Flux    3. Normalized Flux Error    4. Exposure Time (seconds) Additional columns for:    CHEOPS : roll angle (degrees), background flux    EulerCam :  point spread function centroid shifts (pixels), full width at half maximum (pixels), peak flux value (ADU), and airmass

Posterior samplesPosterior samples from the photodynamical modeling.Filename: K2-19_Almenara2025_posterior.csvColumns:    G:  N-body gravitational constant (au^3 day^-2 M_Sun^-1)    Tref: Reference time for the orbital elements (BJD_TDB)    timestep: N-body time step (days)    coordinate: Coordinate system    m_s: Star mass (M_Sun)    r_s: Star radius (au)    m_p: Planet mass (M_Sun)    r_p: Planet radius (au)    a_p: Planet's semimajor axis (au)    e_p: Planet's eccentricity     i_p: Planet's orbital inclination (degrees)    w_p: Planet's argument of pericentre (degrees)    Node_p: Planet's longitude of the ascending node (degrees)    M_p: Planet's mean anomaly (degrees)'p' is the planet number, ranging from 1 (the innermost planet) to n (the outermost planet):    1: K2-19 d    2: K2-19 b    3: K2-19 c    4: candidate K2-19 eTransit forecastTransit timing predictions derived from the photodynamical modeling.Filename: K2-19_Almenara2025_forecast2035.csvColumns:     1. Planet number (see the description of the posterior file)    2. Epoch number of the transit    3. Transit time (BJD_TDB)     4. Lower 68.3% credible interval (days)    5. Upper 68.3% credible interval (days)Light curves Transit observations of K2-19. Filename: K2-19_[Mid-transit UT date or start date for K2 and TESS][Telescope][Band].txtColumns:     1. Mid-Exposure Time (BJD_TDB)    2. Normalized Flux    3. Normalized Flux Error    4. Exposure Time (seconds) Additional columns for:    CHEOPS : roll angle (degrees), background flux    EulerCam :  point spread function centroid shifts (pixels), full width at half maximum (pixels), peak flux value (ADU), and airmass

Related paperMacêdo, R.A., Bellinati, C.C, Fontana, W.B., Andrade, E.C., Pereira, R.G. Partons from stabilizer codes, Physical Review B, 2025Numerical dataThe numerical data used to make the plots of the paper is present on this data set. Each file correspond to the following data:gap.csv: Value of the energy gap along the different adiabatic paths defined by alpha (figure 4b)entanglement_entropy_nx.csv: Scaling of the entanglement entropy with the cylinder length (figure 5a)entanglement_entropy_ny_spin.csv: Scaling of the entanglement entropy with the cylinder circumference for the projected states (figure 5b)entanglement_entropy_ny_parton.csv: Scaling of the entanglement entropy with the cylinder circumference for the unprojected states (inset of figure 5b)topological_entanglement_entropy.csv: Value of the topological entanglement entropy of the projected states along different adibatic paths (figure 6a)phase_diagrams.csv: Critical values for s1 and s2  at different adiabatic paths (used for constructing the phase diagram of figure 6b)magnetization_nx.csv: Magnetization on the z direction along the adiabatic path for different system sizes (inset of figure 7a)susceptibility_nx.csv: Spin susceptibility on the z direction along the adiabatic path for different system sizes (figure 7a)plaquette.csv: Expected value of the plaquette operator along different adiabatic pathscorrelation_function.csv: Sz-Sz correlations as a functions (figure 8a)bond_dimension_convergence.csv: Maximum value that correlation length assumes along the adiabatic path as a function of the Matrix Product State bond dimension (figure 8b)correlation_length_nx.csv: Correlation length along the adiabatic path for different cylinder lengths (figure 8c)correlation_length_ny.csv: Correlation length along the adiabatic path for different cylinder circumferences (figure 8d)Code availability All codes used in our work are available upon reasonable request. Funding FAPESP, Grants Nos 2021/06629-4, 2022/15453-0, and 2023/06101

Related paperMacêdo, R.A., Bellinati, C.C, Fontana, W.B., Andrade, E.C., Pereira, R.G. Partons from stabilizer codes, Physical Review B, 2025Numerical dataThe numerical data used to make the plots of the paper is present on this data set. Each file correspond to the following data:gap.csv: Value of the energy gap along the different adiabatic paths defined by alpha (figure 4b)entanglement_entropy_nx.csv: Scaling of the entanglement entropy with the cylinder length (figure 5a)entanglement_entropy_ny_spin.csv: Scaling of the entanglement entropy with the cylinder circumference for the projected states (figure 5b)entanglement_entropy_ny_parton.csv: Scaling of the entanglement entropy with the cylinder circumference for the unprojected states (inset of figure 5b)topological_entanglement_entropy.csv: Value of the topological entanglement entropy of the projected states along different adibatic paths (figure 6a)phase_diagrams.csv: Critical values for s1 and s2  at different adiabatic paths (used for constructing the phase diagram of figure 6b)magnetization_nx.csv: Magnetization on the z direction along the adiabatic path for different system sizes (inset of figure 7a)susceptibility_nx.csv: Spin susceptibility on the z direction along the adiabatic path for different system sizes (figure 7a)plaquette.csv: Expected value of the plaquette operator along different adiabatic pathscorrelation_function.csv: Sz-Sz correlations as a functions (figure 8a)bond_dimension_convergence.csv: Maximum value that correlation length assumes along the adiabatic path as a function of the Matrix Product State bond dimension (figure 8b)correlation_length_nx.csv: Correlation length along the adiabatic path for different cylinder lengths (figure 8c)correlation_length_ny.csv: Correlation length along the adiabatic path for different cylinder circumferences (figure 8d)Code availability All codes used in our work are available upon reasonable request. Funding FAPESP, Grants Nos 2021/06629-4, 2022/15453-0, and 2023/06101

These are all the submitted samples to dataset 3 of the “Fast Calorimeter Simulation Challenge 2022”. They each consist of 100k calorimeter showers of electrons with energies sampled from a log-uniform distribution ranging from 1 GeV to 1 TeV.The training data (based on Geant4) can be found at https://doi.org/10.5281/zenodo.6366324, the paper describing the results is available on arXiv:2410.21611, and further details, in particular helper scripts to parse the data and calculate and visualize basic high-level physics features, are available at https://calochallenge.github.io/homepage/.The subscripts in the file names corresponds to the individual submissions: ID numberSubmission nameOriginal reference_1CaloDiffusionarXiv:2308.03876_2L2LFlows-MAFarXiv:2302.11594, arXiv:2405.20407_3conv. L2LFlowsarXiv:2405.20407_5MDMAarXiv:2305.15254, arXiv:2408.04997_6CaloCloudsarXiv:2305.04847,  arXiv:2309.05704_7Calo-VQarXiv:2405.06605_9CaloScore distilledarXiv:2206.11898, arXiv:2308.03847_10CaloScore single-shotarXiv:2206.11898, arXiv:2308.03847_13iCaloFlow teacherarXiv:2305.11934_14iCaloFlow studentarXiv:2305.11934_21Geant4-TransformerDOI_23CaloPointFlowarXiv:2403.15782_27CaloVAE+INNarXiv:2312.09290_31Calo-VQ(norm)arXiv:2405.06605_33CaloDREAMarXiv:2405.09629The samples here can be used to reproduce the results of arXiv:2410.21611 and as benchmarks for new models after the challenge concluded.

These are all the submitted samples to dataset 2 of the “Fast Calorimeter Simulation Challenge 2022”. They each consist of 100k calorimeter showers of electrons with energies sampled from a log-uniform distribution ranging from 1 GeV to 1 TeV.The training data (based on Geant4) can be found at https://doi.org/10.5281/zenodo.6366271 the paper describing the results is available on arXiv:2410.21611, and further details, in particular helper scripts to parse the data and calculate and visualize basic high-level physics features, are available at https://calochallenge.github.io/homepage/.The subscripts in the file names corresponds to the individual submissions: ID numberSubmission nameOriginal reference_1CaloDiffusionarXiv:2308.03876_3conv. L2LFlowsarXiv:2405.20407_4CaloINNarXiv:2312.09290_5MDMAarXiv:2305.15254 arXiv:2408.04997_7Calo-VQarXiv:2405.06605_8CaloScorearXiv:2206.11898, arXiv:2308.03847_9CaloScore distilledarXiv:2206.11898, arXiv:2308.03847_10CaloScore single-shotarXiv:2206.11898, arXiv:2308.03847_13iCaloFlow teacherarXiv:2305.11934_14iCaloFlow studentarXiv:2305.11934_15SuperCaloarXiv:2308.11700_22DeepTreearXiv:2311.12616, arXiv:2312.00042_23CaloPointFlowarXiv:2403.15782_27CaloVAE+INNarXiv:2312.09290_30CaloLatentML4PS@NeurIPS_32CaloDiTACAT_33CaloDREAMarXiv:2405.09629The samples here can be used to reproduce the results of arXiv:2410.21611 and as benchmarks for new models after the challenge concluded.

These are all the submitted samples to dataset 2 of the “Fast Calorimeter Simulation Challenge 2022”. They each consist of 100k calorimeter showers of electrons with energies sampled from a log-uniform distribution ranging from 1 GeV to 1 TeV.The training data (based on Geant4) can be found at https://doi.org/10.5281/zenodo.6366271 the paper describing the results is available on arXiv:2410.21611, and further details, in particular helper scripts to parse the data and calculate and visualize basic high-level physics features, are available at https://calochallenge.github.io/homepage/.The subscripts in the file names corresponds to the individual submissions: ID numberSubmission nameOriginal reference_1CaloDiffusionarXiv:2308.03876_3conv. L2LFlowsarXiv:2405.20407_4CaloINNarXiv:2312.09290_5MDMAarXiv:2305.15254 arXiv:2408.04997_7Calo-VQarXiv:2405.06605_8CaloScorearXiv:2206.11898, arXiv:2308.03847_9CaloScore distilledarXiv:2206.11898, arXiv:2308.03847_10CaloScore single-shotarXiv:2206.11898, arXiv:2308.03847_13iCaloFlow teacherarXiv:2305.11934_14iCaloFlow studentarXiv:2305.11934_15SuperCaloarXiv:2308.11700_22DeepTreearXiv:2311.12616, arXiv:2312.00042_23CaloPointFlowarXiv:2403.15782_27CaloVAE+INNarXiv:2312.09290_30CaloLatentML4PS@NeurIPS_32CaloDiTACAT_33CaloDREAMarXiv:2405.09629The samples here can be used to reproduce the results of arXiv:2410.21611 and as benchmarks for new models after the challenge concluded.

These are all the submitted samples to dataset 1 (photons) of the “Fast Calorimeter Simulation Challenge 2022”. They each consist of 121,000 calorimeter showers of photons with energies ranging from 256 MeV to 4.2 TeV.The training data (based on Geant4) can be found at  https://doi.org/10.5281/zenodo.8099322 the paper describing the results is available on arXiv:2410.21611, and further details, in particular helper scripts to parse the data and calculate and visualize basic high-level physics features, are available at https://calochallenge.github.io/homepage/.The subscripts in the file names corresponds to the individual submissions: ID numberSubmission nameOriginal reference_1CaloDiffusionarXiv:2308.03876_4CaloINNarXiv:2312.09290_7Calo-VQarXiv:2405.06605_8CaloScorearXiv:2206.11898, arXiv:2308.03847_9CaloScore distilledarXiv:2206.11898, arXiv:2308.03847_10CaloScore single-shotarXiv:2206.11898, arXiv:2308.03847_11CaloFlow teacherarXiv:2210.14245_12CaloFlow studentarXiv:2210.14245_16CaloManarXiv:2211.15380_17BoloGANATL-SOFT-PUB-2020-006_25CaloShower2GANarXiv:2309.06515_26CaloShower3GANarXiv:2309.06515_27CaloVAE+INNarXiv:2312.09290_28CaloForestarXiv:2408.16046_29CaloGrapharxiv:2402.11575The samples here can be used to reproduce the results of arXiv:2410.21611 and as benchmarks for new models after the challenge concluded.