dataesr/scientific-paragraphs-categorization huggingface dataset:
The block centres are offset between passes in order to ensure uniform coverage and enable self-calibration. Each block forms an astronomical observation request (AOR), in IRAC jargon. All other data included in our processing are archival data. They were obtained with a variety of observing strategies that we did not investigate in detail and that we do not attempt to summarise here. In Appendix C, we list the programme IDs of all the observations processed; the ones of our dedicated observations are shown in bold. The combination of the archival data and our own dedicated data produces a spatially variable depth in most fields; this is discussed further in Sect. 4. All observations are summarised in Table  1 , which gives, for each field and channel, the number of frames (data collec-tion events -DCEs -in IRAC terminology) used to produce the mosaics (we note that this can be lower than the number of frames downloaded as some were discarded; see Sect.
Pre-processing and calibration Processing begins with the Level 1 data products generated by the Spitzer Science Center via their 'basic calibrated data' pipeline  (Lowrance et al. 2016) , which were downloaded from the NASA/IPAC Infrared Science Archive (IRSA 2  ). They have had all well-understood instrumental signatures removed, have been flux-calibrated in units of MJy sr -1 , and are delivered with an uncertainty image and a mask image; they are described in detail in the IRAC Instrument Handbook 3. More precisely, we begin from the 'corrected basic calibration data' products, which have file extensions. cbcd for the image,. cbunc for the uncertainty, and. bimsk for the mask. The files are grouped by AORs, namely sets of a few to several hundred DCEs obtained sequentially. All frames are 256 × 256 pixels, the pixels are 1.
The left part of the table is for channels 1 and 2, split between cryogenic and warm mission, the right part is for channels 3 and 4, which were used during the cryogenic mission only. We note that the EDF-S field was observed only during the warm mission. proper motions. Next, they are identified on each IRAC frame, their observed fluxes and positions are determined in each frame using the APEX software (the point-source extractor in MOPEX 5 ) in forced-photometry mode, and the positions are used to update the astrometric solution of each frame. There are typically 30-40 Gaia DR2 sources available for each frame. In channels 1 and 2, most of them are detected and used for the astrometric correction. In the longer-wavelength channels, 3 and 4, only a few sources in total are detected and usable, but that is still sufficient to determine an astrometric solution with negligible distortion as shown in Sect.
These comparisons show that the linear interpolation procedure leads to an underestimation of aperture magnitude errors by 30-40%, while the magnitudes themselves are unaffected. Next, we used MOPEX to produce an average-combined image while rejecting outliers and excluding masked regions. Outlier rejection was done using the multi-frame temporal, dual outlier, and box outlier modules from MOPEX. We used the same parameters used by the Spitzer super mosaics, which were tuned to produce balanced outlier rejection for a wide range of data types and uses. The outlier rejection methods were then combined using the MosaicRMASK module. The specific parameters used can be found in Table  2. Frames with different exposure times are combined using the exposure time weighting feature in MOPEX. This appropriately scales the uncertainties and coverage maps to match the median exposure time before carrying out the combination.
Summary We have presented the Spitzer Space Telescope/IRAC midinfrared component of the Cosmic Dawn Survey: an effort to complement the Euclid mission's observations of deep and calibration fields with deep longer-wavelength data to enable high redshift legacy science. The survey consists of two major new programmes covering the three Euclid deep fields (EDF-N, EDF-F and EDF-S) and a homogeneous reprocessing of all existing data in Euclid's four calibration fields (COSMOS, XMM, EGS and HDFN). We have processed new data together with all relevant archival data to produce mosaics of these fields covering a total of ∼71 deg 2 in IRAC channels 1 and 2. Furthermore, the new mosaics are tied to the Gaia astrometric reference system. The MIR data will be essential for a wide range of legacy science with Euclid, including improved star/galaxy separation, more accurate photometric redshifts, determination of stellar masses of galaxies, and the construction of complete galaxy samples at z > 2 with well understood selection effects.
We validated our final products by comparing catalogues extracted from channels 1 and 2 to external catalogues. In all fields, comparing with Gaia DR2, the residual astrometric uncertainty for sources with total magnitudes 16 < [3. 6] < 19 is around 0. 15 (1σ). Our photometric measurements are in excellent agreement with WISE photometry and our number counts are consistent with previous determinations. The Cosmic Dawn Survey Spitzer Space Telescope survey presented here represents the first essential step in assembling the required multi-wavelength coverage in the Euclid deep fields which are set to become some of the most important fields in extragalactic astronomy for the coming decade. Since the Spitzer Space Telescope mission has finished, and all available data in these fields have been processed with the latest reduction pipeline, the resulting mosaics will remain the deepest and widest MIR imaging survey for the foreseeable future.
Acknowledgements. We thank the MOPEX support team for fixing issues that appeared when combining large numbers of files. This publication is based on observations made with the  Spitzer Space Telescope , which is operated by the  Jet Propulsion Laboratory, California Institute of Technology  under a contract with  NASA , and has made use of the  NASA/IPAC Infrared Science Archive , which is funded by the  National Aeronautics and Space Administration  and operated by the  California Institute of Technology. This publication has also made use of data from the  European Space Agency (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 Gaia Data Processing and  Analysis Consortium  (DPAC) has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement.
This publication makes use of data products from the  Wide-field Infrared Survey Explorer (WISE ), which is a joint project of the  University of California, Los Angeles , and the  Jet Propulsion Laboratory/California Institute of Technology , funded by the  National Aeronautics and Space Administration. H. J. McC. acknowledges support from the  PNCG. This work used the CANDIDE computer system at the IAP supported by grants from the  PNCG  and the  DIM-ACAV  and maintained by  S. Rouberol. S. T. and J. W. acknowledge support from the  European Research Council (ERC)   Consolidator Grant funding scheme  (project  ConTExt , grant No. 648179 ). I. D. has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No.
896225. The Cosmic Dawn Center is funded by the  Danish National Research Foundation  under grant No. 140. H. Hildebrandt is supported by a Heisenberg grant of the  Deutsche Forschungsgemeinschaft  ( Hi 1495/5-1 ) as well as an  ERC Consolidator Grant  (No. 770935 ). The Euclid Consortium acknowledges the  European Space Agency  and a number of agencies and institutes that have supported the development of Euclid, in particular the  Academy of Finland , the  Agenzia Spaziale Italiana , the  Belgian Science Policy , the  Canadian Euclid Consortium , the  French Centre National d'Etudes Spatiales , the  Deutsches Zentrum fúr Luft-und Raumfahrt , the  Danish Space Research Institute , the  Fundação para a Ciência e a Tecnologia , the  Ministerio de Ciencia e Innovación , the  National Aeronautics and Space Administration , the  National Astronomical Observatory of Japan , the  Netherlandse Onderzoekschool Voor Astronomie , the  Norwegian Space Agency , the  Romanian Space Agency , the  State Secretariat for Education, Research and Innovation (SERI)  at the  Swiss Space Office (SSO) , and the  United Kingdom Space Agency.
These values are directly extracted from the simulations and inferred from the experimental data. The burn duration is directly measured using a gamma reaction history (GRH) diagnostic  [56] , and the radius and volume of the hot spot are determined from threedimensional imaging of the primary 14 MeV neutrons  [57]. The ion temperature is measured from Doppler broadening of the D-T and D-D neutron spectrum using neutron-timeof-flight (NTOF) detectors  [58]. The density of the hot spot is inferred from the fusion energy produced, the known D-T and D-D temperature-dependent reaction rates, the measured burn duration, and the measured hot spot volume. The inferred measured pressure is then a function of the inferred density and measured temperature of the hot spot. This method has been validated against a database of ensemble simulations spanning the performance and input parameter space of N210808  [59].
There are some additional guides to find a shell identification that were detailed in  Bílek et al. (2013). They are applications of Occam's razor: we should not postulate the existence of more shell generations than necessary, and we should not postulate the existence of too many shells escaping observations. This visual approach of finding the shell identification was sufficient for NGC 474, where we analyzed only eight shells and were interested mainly in the ages of the shell generations. Bílek et al. (2014)  presented a code that can find all possible shell identifications. The code was necessary for that work because we studied a galaxy with many more shells, and it was essential not to miss any possible shell identification. The input parameters of the code are the maximum allowed number of shell generations and of the missing shells and the maximum allowed deviation of the observed and modeled shell radii.
The degeneracy can be broken by deriving the star formation histories of the centers of shell galaxies, which can now be performed routinely. one described in  Nagesh et al. (2021) , which makes use of an adjusted version of the DICE code  (Perret 2016). We found that at fixed orbital parameters of the encounter, the morphology of the shells is sensitive to the initial structure of the secondary. In the final simulation, the galaxies started 50 kpc apart. The difference of the y-components of the velocities of the galaxies was 5. 0 km s -1. The components of the velocities of the galaxies in the other directions were zero. The spin vector of the secondary pointed in the direction of the z-axis. The orbital initial conditions for the simulation are summarized in Table A. 2.
A. 2. Newtonian simulations The initial conditions of the Newtonian simulations are summarized in Table  A. 3. The stellar masses of the galaxies were the same as in the MOND simulation. The dark matter halos were modeled by NFW halos truncated at 100 kpc. The initial models were generated by the MAGI code  (Miki & Umemura 2018)  and were evolved in isolation for 2 Gyr before they were entered in the simulation of the collision. The scale radii and characteristic densities of the halos were derived as following the mean dark matter scaling relations  (Behroozi et al. 2013; Diemer & Kravtsov 2015). We explored various initial distances of the primary and secondary. We obtained shells that are comparable in size with those observed for as large initial separation as possible. We avoided initial distances that are larger than the truncation radius of the halos because such simulations would not be able to treat dynamical friction correctly.
Acknowledgements. We thank the anonymous referee for a very constructive and helpful report. I. E. and M. B. acknowledge the support from the  Polish National Science Centre  under the grant  2017/26/D/ST9/00449. M. B. is thankful for the financial support by  Cercle Gutenberg. B. F. acknowledges financial support from the  European Research Council (ERC)  under the European Unions  Horizon 2020 research and innovation programme  (grant agreement No. 834148 ).
AC K N OW L E D G E M E N T S WHB and WJC thank the UK Science and Technology Facilities Council (STFC) for support under grant ST/R0023297/1. AM acknowledges support from the ERC Consolidator Grant funding scheme (project ASTER OCHR ONOMETR Y, https://www. asteroch ronometry. eu , G. A. n. 772293). RAG acknowledges the support from PLATO and GOLF CNES grants. LGC thanks the support from grant FPI-SO from the Spanish Ministry of Economy and Competitiveness (MINECO) (research project SEV-2015-0548-17-2 and predoctoral contract BES-2017-082610). SM acknowledges support from the Spanish Ministry of Science and Innovation (MICINN) with the Ram ón y Cajal fellowship no. RYC-2015-17697 and grant no. PID2019-107187GB-I00, and through AEI under the Severo Ochoa Centres of Excellence Programme 2020-2023 (CEX2019-000920-S).
DLB acknowledges support from the TESS GI Program under NASA awards 80NSSC18K1585 and 80NSSC19K0385. JC is supported by a grant from the Max Planck Society to prepare for the scientific exploitation of the PLATO mission. CK is supported by Erciyes University Scientific Research Projects Coordination Unit under grant number DOSAP  MAP-2020-9749. This paper includes data collected by the TESS mission, which are publicly available from the Mikulski Archive for Space Telescopes (MAST). Funding for the TESS mission is provided by the NASA Explorer Program. This work has used data from the European Space Agency (ESA) mission Gaia ( ht tps://www. cosmos. esa. int /gaia ), processed by the Gaia Data Processing and Analysis Consortium (DPAC, https: // www. cosmos. esa. int/web/gaia/ dpac/consortium ).
, 2014). Specific spectral features used in this study that are indicative of Fe 2+/3+ -bearing phyllosilicates include an absorption band centered at ∼670 nm and an absorption band centered at ≥900 nm (Figure  3b ;  Anderson & Stucki, 1979; Bishop et al. , 2008). However, not all Fe/Mg-phyllosilicates exhibit both diagnostic absorption bands mentioned previously (e. g. , saponite; Figure  3a ). Figure  3c  shows examples of other relevant minerals that exhibit subtle differences in the wavelength range of Mastcam. Anhydrite exhibits an absorption band centered at ∼650 nm but does not exhibit absorption bands beyond 800 nm. Jarosite, akaganeite, lepidochrosite, and goethite can all have absorption bands centered at similar wavelengths to Fe 2+/3+ -bearing phyllosilicates, so differentiating these minerals is reliant on using VNIR spectra in tandem with quantitative datasets (e.
, 2017). Raw Mastcam data are converted from digital number (DN) values to irradiance (W/m 2 ), and then to radiance (W/m 2 / nm/sr) using preflight calibration measurements, as described by  Bell et al. (2017). A calibration target on the deck of the rover is imaged along with each multispectral image set, and spectra from the calibration target are used to model the effects of illumination and viewing geometry as well as atmospheric scattering in Mastcam images and thus convert radiance images into radiance factor (I/F;  Bell et al. , 2017; Kinch et al. , 2015; Wellington et al. , 2017). In this study, we examined 62 Mastcam full-filter multispectral image sets acquired between sols 2302 and 2975 that were calibrated to radiance and processed to relative reflectance (I/F). This study specifically focuses on image sets corresponding to all drill sites and targets in which the DRT was used to clear bedrock targets.
Acknowledgments We thank the  MSL mission team  for their hard (virtual) work whilst facing all the challenges that have come with the COVID-19 global pandemic and collecting the data that enabled this work. We also thank the  U. S. Geological Survey Astrogeology Science Center  and anonymous reviewers for helpful comments and discussion, and  Michael St. Clair  and  Million Concepts , LLC for Mastcam supplemental data information. This material is based upon work supported by the  National Science Foundation Graduate Research Fellowship Program  under Grant No. DGE-1842166.
Data Availability Statement All Mastcam and Mars Hand Lens Imager images used in this manuscript are on the Planetary Data System (PDS) Geosciences Node  (Edgett, 2013; Malin, 2013) , as well as the HiRISE data used in this manuscript  (McEwen, 2007). All ChemCam spectra used in this manuscript are available through the PDS Geosciences node  (Johnson & Ward, 2022). Derived Mastcam and ChemCam data used in this paper are available  (Rudolph & Johnson, 2022).
Observation In Fig. 1 , we show the archival Swift data for NGC 3227 from 2008-10-28 to 2020-12-27. During X-ray obscuration events, the significantly lowered soft X-ray flux leads to an elevated X-ray hardness ratio. The X-ray hardness ratio of NGC 3227 increased by ∼0. 5 in ∼2 weeks around early November 2019 (Fig. 2 , bottom panel). It remained at a high-level until 2019-12-05. After that, the target was out of the Swift visibility window until April 2020, where the hardness ratio was negative. Accordingly, the duration of the X-ray obscuration event in 2019 was less than five months. We note that the timescales of both the elevation and duration can be even shorter for NGC 3227. Around November 2008, the Swift X-ray hardness ratio increased by ∼0. 3 within a day and ∼0. 5 in ∼2 weeks (Fig.
2 , upper panel). On 2016-12-09, the duration of the X-ray obscuration was ∼20 ks (Paper II). The relatively large Swift X-ray hardness ratio in late 2019 triggered two coordinated multi-wavelength observations with HST/COS, XMM-Newton, and NuSTAR of NGC 3227. Table  1  lists the XMM-Newton and NuSTAR data used in the present work. The data reduction was the same as described in Sect. 2 of Paper I. In Table  1 , we also list four HST observations, one in 2000, one in 2010, and two in late 2019. With these UV grating spectra, we aim to briefly investigate the X-ray and UV connection of the obscuring event (Sect. 5). For each observation, the two first-order spectra of Reflection Grating Spectrometer  (RGS, den Herder et al. 2001)  were fitted simultaneously over the 6-37 Å wavelength range.
A lowly ionized X-ray obscurer is also statistically unacceptable, especially for the 2019-11-15 data set. Due to the relatively short A72, page 5 of 11   2  and 3 ) to the RGS spectra of NGC 3227 on 2019-11-15 (top) and 2019-12-05 (bottom). The warm emitter component is shown in red and is identical for both observations, while the continuum is shown in blue. The black curves are the total emission. exposure (Table  1 ) and low flux (e. g. , Fig. 3 ), the 2019-12-05 data set is less sensitive (∆C 20) to a wide range of ionization parameters.
2018). For NGC 3227, if the obscurer observed in the X-ray band intercepts our line of sight to (a significant portion of) the UVemitting region, we would expect to see absorption features in the UV band. As shown in Fig. 7 , the ionic column densities of for example, H i, C iv, N v, and Si iv for the X-ray obscurer are all well above 10 14 cm -2 for a wide range of the ionization A72, page 6 of 11 6. 8 6. 9 7. 0 7. 1 7. 2 Observed wavelength (Å)  parameter (-3 log ξ 3). These column densities are large enough to produce blueshifted absorption troughs in the HST/COS spectra for NGC 3227, similar to those in NGC 5548  (Kaastra et al. 2014 ) and NGC 3783  (Mehdipour et al. 2017; Kriss et al. 2019b ). Although a highly ionized X-ray obscurer can result in low ionic column densities, it is not consistent with the observed X-ray data (Sect.
UV covering factor of the X-ray obscurer As we cannot rule out the possibility that the obscurer is above the UV-emitting region, here we explore possible interpretations of the observed data in this case. The weakness or absence of the blueshifted broad absorption troughs in the HST/COS spectra can be explained if the obscurer observed in the X-ray band does not intercept our line of sight to (a significant portion of) the UV-emitting region. We note that the X-ray covering factor of the obscurer is 0. 7 for NGC 3227 in 2019. The UV covering factor is expected to be even smaller. Assuming a fiducial X-rayemitting central engine of the order of 10 R S , the UV-emitting region with R 2500 Å ∼ 80 R S would be a factor of 64 larger. The X-ray obscurer would then cover 1% of the UV-emitting region.
Acknowledgements. A supplementary package is available at Zenodo DOI: https://doi. org/10. 5281/zenodo. 6459682. This package includes data and scripts used to reproduce the fitting results and figures presented in this work. We thank the referee for careful reading and constructive suggestions to improve the quality of the manuscript. This work is based on observations obtained with  XMM-Newton , an  ESA  science mission with instruments and contributions directly funded by  ESA Member States  and the  USA (NASA). This research has made use of data obtained with the  NuSTAR mission , a project led by the  California Institute of Technology (Caltech) , managed by the  Jet Propulsion Laboratory (JPL)  and funded by  NASA. We thank the  Swift team  for monitoring our AGN sample, and the  XMM-Newton, NuSTAR , and HST teams for scheduling our ToO triggered observations.
This work work was supported by  NASA  through  A72 , page 10 of 11  J. Mao  et al. : Transient X-ray obscuration event of NGC 3227 in 2019 a grant for HST program number  15673  from the  Space Telescope by the Association of Universities for Research in Astronomy , incorporated, under  NASA  contract  NAS5-26555. SRON is supported financially by  NWO , the  Netherlands Organization for Scientific Research. J. M. acknowledges useful discussions with  Zhu Liu. S. G. W. acknowledges the support of a PhD studentship awarded by the  UK Science & Technology Facilities Council (STFC). S. B. acknowledges financial support from  ASI  under grants  ASI-INAF I/037/12/0  and n. 2017-14-H. O and from  PRIN MIUR  project " Black Hole  winds and the  Baryon Life Cycle of Galaxies : the stone-guest at the galaxy evolution supper", contract no.
2017PH3WAT. POP acknowledges financial support from the  CNRS Programme National des Hautes Energies (PNHE)  and from the french space agency CNES. B. D. M. acknowledges support via Ramón y Cajal Fellowship RYC2018-025950-I. D. J. W. also acknowledges support from  STFC  in the form of an  Ernest Rutherford Fellowship  ( ST/N004027/1 ). G. P. acknowledges funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No. 865637 ).
We use scaling relations linking mass and temperature [M ∆ -T], luminosity and temperature [L-T] and between r ∆ and the cluster core radius r c , assuming a β model with β=2/3, [r c -r ∆ ]. The relation between physical and apparent core radii reads: θ c [arcsec] = 648000 π r c [Mpc] / d a (z) [Mpc] (2) where d a is the angular diameter distance. We make use of the apec model: the emission spectrum from collisionally-ionised diffuse gas is calculated from the AtomDB atomic database,  (Smith et al. 2001 ) with a metallicity of 0. 3 Z. Folding the spectrum with the EPIC (European Photon Imaging Camera) XMM response matrices, provides us with count-rates in the three energy bands of interest  ([0. 5-2]  keV, [0. 5-1] keV and [1-2] keV). From this, we subsequently construct the 4D z-CR-HR-θ c diagram.