dataesr/scientific-paragraphs-categorization huggingface dataset:
Results In this section, we present statistical results based on our annotations of tidal features, including their geometrical properties and surface brightness. We opted for median values rather than mean ones in order to get the most representative values. The database contains 8441 annotations. The number of annotations per feature type is detailed in Table  2. The annotations have been made by four users: two of them have annotated all the galaxies, while 30 and 58. 5% of the galaxies were delineated by the two other users.
Tidal tails and streams A total of 223 tidal tails and 84 streams in our database were kept after the selection process  16. Here we present several geometrical and surface brightness analyses of these features. Based on these analyses, we provide a discussion on the criterion used to differentiate tidal tails from streams in Sect. 6. 5. Notes. In parenthesis is indicated the number of annotations kept after our selection process on the main galaxy, halo, tidal tails and streams.
Stream progenitors If streams emanate from a disrupted satellite, remnants of the progenitor may still be visible. As matter of fact, the presence of a condensation within a tidal structure was one of our criteria to label it as a possible stream, especially if a sign of an S-shape was present  17. Hence, it is interesting to check the percentage of streams that have a progenitor from our annotation database. The progenitor of the stream is defined as follows. If there are no companion or dwarf galaxy in the annotations, the stream is considered orphan. If the stream has an overlap (partial or entire) with a companion or dwarf galaxy, then the progenitor is the companion galaxy. The absence of a progenitor in a genuine stream may indicate that the satellite has been totally destroyed, if the stream was formed long ago, or that it is hidden in the primary galaxy.
Surface brightness and radius We measured the surface brightness along the external contours of the annotated stellar halos. Figure  15    for the 340 stellar halos kept after our selection process. It peaks at 26. 1 mag arcsec -2 (median value). Such a value is clearly much lower than the nominal SB limit of the survey, and that obtained when deriving integrated surface brightness profiles of galaxies. It just reflects the ability of the eye in delineating an external contour on our asinh images. In addition, from the coordinates of the annotations stored in our database, we can compute the radius of the annotated stellar halos. The histogram of the values measured for all galaxies is shown on Fig. 16. One can see that the median radius is around 16. 5 kpc for ETGs and 15. 9 kpc for LTGs, with the majority of the halos having a radius smaller than 30 kpc.
The study of integrated SB profiles of ETGs and LTGs might reveal differences, but this is behind the scope of this paper. From our annotation database, we cannot directly infer the stellar mass of the halo, an important parameter to constrain the mass assembly of galaxies  (Merritt et al. 2020). This estimate requires a lot of processing, in particular to take into account PSF effects  (Karabal et al. 2017 ). It will be the focus of another paper in this series.
Covering factor As mentioned earlier, our annotation database does not provide an estimate of the stellar mass of the LSB structures surrounding their host galaxies. However one proxy of their relative importance is their "covering factor", defined as the percentage of the pixels belonging to one type of structure with respect to a given field of view. For the latter, we considered boxes of side length 20 R e centered on the primary galaxy. For this measurement, we selected the annotations of a given galaxy and a given user. The median values of the covering factor for different types of structures are given in Table  8. A covering factor of 0 (respectively 1) means that the feature is not present on (respectively entirely covers) the given field of view. Results are shown in Fig. 17. Within the selected boxes, the stellar halo has a median covering factor of 0.
Discussion The originality of our approach relies on the use of an annotation database of Low-Surface-Brightness features compiled by a group of users who have precisely delineated a large number of individual structures (representing a total of 8441 annotations) directly on displayed images and classified them. The method raises a number of issues partly posted in the previous sections: (1) the difficulty of matching individual annotations made by different users (2) the presence of overlapping structures, in particular the contaminants which have a large covering factor, which prevent us from determining with precision the boundaries of some structures (3) the ambiguity in the classification of the various types of tidal features (4) the fact that we have used images coming from various CFHT surveys with varying depth and surface brightness limits and (5) the reliability of the annotations when considering users with different levels of expertise.
Disentangling tidal tails from streams As mentioned in Sect. 3. 2, we defined tidal tail as structures with stellar material apparently coming from the primary galaxy, while streams originate from a less-massive smaller companion which may still be visible, hidden, or have been destroyed. Most probably the users have adopted this definition in different ways according to their expertise and used a variety of observables (location, shape, amount of overlap with the closest galaxy, etc. ) to assess the classification. Getting quantitative measurements from our annotation database, we are able to determine whether these 2 classes of tidal objects show different properties, may thus be really distinguished from our images and retrospectively check whether the basic initial criterion for disentangling them was relevant.
Limits of the study We remind readers here that all our quantitative measurements relied on the analysis of the annotation database. In particular, we did not perform aperture photometry in our images. This means that we cannot determine the flux of the tidal features or of the halos, and hence their stellar mass. Furthermore, no masking techniques were used to remove the polluting light from foreground or background objects. The SB values that are obtained are useful to compare trends between features, but a more detailed analysis would be needed to precisely assess them. Similar to any other visual classification process, our annotations would be more reliable if tens or hundreds of people would have participated. In this paper, we rely on the annotations of four users only. However, we realized this process is complicated and the users need to be trained both to recognize LSB structures and to draw their shape with precision, making any citizen science project like Galaxy Zoo difficult to implement.
This task is made more complicated by the presence of many pollutants that overlap with the interesting features. The time necessary to annotate is quite important as well (around 10 minutes per galaxy). Therefore, automated techniques will be needed to analyze larger samples of galaxies with few annotations. The annotations we made could be used to train machine learning algorithms. In fact, it has already been the case with  Richards et al. (2020) , who used our cirrus annotations to train a new machine learning algorithm to detect cirrus on deep images. Yet, the small number of annotations is a problem as large datasets are needed to train such algorithms. Appropriate data augmentation or annotations of cosmological simulations may be necessary to have large enough training datasets.
Conclusions The detection and classification of tidal features around galaxies is essential as their characterization gives valuable information about the past assembly history of their host galaxy. In this paper, we present an online annotation tool that enables users to draw the shapes of LSB structures with precision in deep images for a large number of galaxies. We also show how we may use our annotation database to estimate some physical quantities, such as the shape, size, surface brightness and colors of tidal tails, streams, shells and stellar halos. Such values were so far not very well constrained on large samples of galaxies. They may be compared to simulations to better understand the type of mergers that took place, and more generally to constrain models of galactic evolution.
Using a customized online tool, we have manually identified, delineated and classified LSB features including contaminants around 352 nearby massive galaxies from the CFHT MATLAS and CFIS surveys. four different users. A database containing 8441 annotations was compiled (Table  2 ). We have developed a methodology to associate common annotations between users and link the associations with their host galaxies. With such a project, we aim at differentiating the types of tidal debris and a posteriori determine whether a classification solely based on eye inspection of deep images is reliable. Not so surprisingly, the annotated structures show an apparent large variety of shapes and sizes when put and compared together (as visible in Figs. A. 1 and A. 2). There is a large overlap between tails (tidal structures emanating from the primary galaxy and made during major mergers) and streams (tidal debris from disrupted low-mass companions).
They may significantly alter our SB and color measurements. Having the possibility to remove them would also be a strong asset for future automatic classifications. Finally, our annotations were all done manually, which took a non negligible amount of time of a group of participants. Though feasible in samples of hundreds of galaxies, thanks to our dedicated online tool, it cannot be applied to samples of thousands. Machine learning methods will be needed to automatically detect and classify tidal features in large surveys to come, and our annotation database can be used to train such algorithms. As future prospects, the properties of tidal features with respect to the host galaxy properties will be studied in a future paper. In addition, the amount of stellar mass in the tidal features could be computed.
Figs. C. 1-C. 3 from Bílek et al. (2020)  present tidal features identified in MATLAS images. Once the user has finished annotating a galaxy, the shape parameters, positions (in pixel coordinates and in right ascension and declination) and labels of each annotation are stored in a database hosted by a server. These can be used to redraw the annotations on the images or plot them as thumbnails, as it will be seen later. Examples of annotated galaxies are visible in Fig. 4. The annotations are made by several users. In this work, all the users are considered to have the same level of expertise in the identification of tidal features. It must be noted however that our tool allows us to take into account possible different levels of expertise. This must be taken into account, as it reflects the degree of confidence one can have in the annotations.
Acknowledgements. The authors thank the anonymous referee for a useful report and  Garreth Martin  for his feedback on the paper. F. R. was supported by  Science and Technology Facilities Council (STFC)  grant  ST/P006779/1. This research has made use of the SIMBAD database, operated at  CDS, Strasbourg, France , and of "Aladin sky atlas" developed at  CDS, Strasbourg Observatory, France. This work is based on data obtained as part of the  Canada-France Imaging Survey , a  CFHT large program  of the  National Research Council of Canada  and the  French Centre National de la Recherche Scientifique. Based on observations obtained with  MegaPrime/MegaCam , a joint project of CFHT and CEA Saclay, at the  Canada-France-Hawaii Telescope (CFHT)  which is operated by the  National Research Council (NRC)  of  Canada , the  Institut National des Science de l'Univers (INSU)  of the  Centre National de la Recherche Scientifique (CNRS) of France , and the  University of Hawaii.
This research used the facilities of the  Canadian Astronomy Data Centre  operated by the  National Research Council of Canada  with the support of the  Canadian Space Agency. This research is based in part on data collected at Subaru Telescope, which is operated by the  National Astronomical Observatory of Japan. We are honored and grateful for the opportunity of observing the Universe from  Maunakea , which has the cultural, historical and natural significance in  Hawaii. Pan-STARRS  is a project of the  Institute for Astronomy of the University of Hawaii , and is supported by the  NASA   SSO Near Earth Observation Program  under grants  80NSSC18K0971 ,  NNX14AM74G ,  NNX12AR65G ,  NNX13AQ47G ,  NNX08AR22G ,  YORPD20_2-0014  and by the  State of Hawaii.
Figs Figs. C. 1-C. 3 from Bílek et al. (2020)  present tidal features identified in MATLAS images. Once the user has finished annotating a galaxy, the shape parameters, positions (in pixel coordinates and in right ascension and declination) and labels of each annotation are stored in a database hosted by a server. These can be used to redraw the annotations on the images or plot them as thumbnails, as it will be seen later. Examples of annotated galaxies are visible in Fig. 4. The annotations are made by several users. In this work, all the users are considered to have the same level of expertise in the identification of tidal features. It must be noted however that our tool allows us to take into account possible different levels of expertise. This must be taken into account, as it reflects the degree of confidence one can have in the annotations.
Aims. Contamination from bright diffuse Galactic thermal and non-thermal radio emission poses crucial challenges in experiments aiming to measure the 21-cm signal of neutral hydrogen from the cosmic dawn (CD) and Epoch of Reionisation (EoR). If not included in calibration, this diffuse emission can severely impact the analysis and signal extraction in 21-cm experiments. We examine largescale diffuse Galactic emission at 122 MHz around the North Celestial Pole, using the Amsterdam-ASTRON Radio Transient Facility and Analysis Centre (AARTFAAC-) High Band Antenna (HBA) system. Methods. In this pilot project, we present the first-ever wide-field image produced with a single sub-band of the data recorded with the AARTFAAC-HBA system. We demonstrate two methods, multi-scale CLEAN and shapelet decomposition, to model the diffuse emission revealed in the image.
Although higher time and frequency resolution is preferred for RFI flagging, we restricted the data resolution to lower values of 2 s and 195 kHz (to observe the NCP for a longer duration) due to the limited capacity of the AARTFAAC storage system. The raw correlation data were later transported to the LOFAR-EoR KSP processing system (dawn HPC cluster,  Pandey et al. 2020) , where all of the data processing and analysis were performed. The raw data were converted to a standard measurement set (MS) format using Aartfaac2ms 1  (Offringa et al. 2015)  and the raw visibilities were phased to the NCP before preprocessing. The observational details of this data set are summarised in Table  1.
Data preprocessing The preprocessing steps include RFI flagging and averaging the raw visibilities. We used Aoflagger  (Offringa et al. 2010 (Offringa et al. , 2012) )  to flag RFI-corrupted data. We also flagged all the visi-1 https://github. com/aroffringa/aartfaac2ms The remaining data were averaged to a lower time resolution of 12 s. After averaging, the data volume of an 11-h observation of 3. 1 MHz bandwidth was ∼1. 7 TB. We noted that five out of six stations outside the superterp had significantly lower visibility amplitudes in all observations recorded during this first pilot programme. Furthermore, the visibilities corresponding to these stations showed extremely erratic behaviour even after calibration. Therefore, we also flagged the tiles corresponding to these five stations before the calibration step.
For each iteration, we used an auto-masking threshold  (Offringa & Smirnov 2017)  of 8σ, 5σ, and 3σ, respectively, where σ is the image rms. The final CLEAN model contains 4924 components (delta functions and Gaussians) to represent the compact sources in the field. A97, page 4 of 11 5. We finally imaged the DI-calibrated visibilities obtained after step 4 with Wsclean using a 'Briggs +0. 5' weighting scheme in all subsequent analyses. Figure  3  shows single sub-band Stokes I dirty images of the NCP field produced using A12-HBA and LOFAR-HBA data at 122. 06  MHz (195. 3 kHz bandwidth). The A12-HBA system has an ∼25 times larger field of view compared to the LOFAR-HBA system. Galactic large-scale diffuse emission is clearly visible around NCP in the A12-HBA image (see also  Bernardi et al.
However, it is incapable of modelling the emission on scales smaller than several degrees. -The diffuse emission has a brightness temperature variance of ∆ 2 =180 = (145. 64 ± 13. 61) K 2 at 122 MHz on angular scale of 1 degree, and it is consistent with C ∝ -2. 0 in the 20 200 range. The analysis in this pilot work is admittedly based only on data from a single sub-band at 122 MHz. Despite this restriction, for the first time using AARTFAAC-HBA as a wide-field imaging instrument, we have convincingly shown that largescale diffuse emission dominates the emission in Stokes I on degree scales and larger. This was previously much harder to assess due to the limited short-baseline coverage of LOFAR in beam-formed mode. In the current analysis, we used traditional CLEAN deconvolution to model and subtract the compact sources before modelling the diffuse structure.
Acknowledgements. B. K. G. and L. V. E. K. acknowledge the financial support from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (Grant agreement No. 884760 , "CoDEX"). F. G. M. and M. K. acknowledge support from a  SKA-NL roadmap grant  from the  Dutch Ministry of OCW. This work was supported in part by  ERC  grant  247295  "AARTFAAC" to RAMJW. LOFAR, the  Low Frequency Array designed  and constructed by  ASTRON , has facilities in several countries, owned by various parties (each with their own funding sources), and collectively operated by the  International LOFAR Telescope (ILT)  foundation under a joint scientific policy. The authors gratefully acknowledge the feedback from the anonymous referee that helped improve and clarify this work.
This research made use of publicly available software developed for LOFAR and AARTFAAC telescopes. Listed below are software packages used in the analysis: Aartfaac 2ms (https://github. com/aroffringa/aartfaac2ms),Aoflagger (https://gitlab. com/aroffringa/aoflagger),Sagecal-co (http:// sagecal. sourceforge. net/),and Wsclean (https://gitlab. com/aroffringa/wsclean). The analysis also relies on the Python programming language (https://www. python. org)and several publicly available python software modules: Astropy (https://www. astropy. org/;Astropy Collaboration 2013), Matplotlib (https://matplotlib. org/), Scipy (https://www. scipy. org/)and Numpy (https://numpy. org/).
A : The ProtoDUNE-SP detector is a single-phase liquid argon time projection chamber (LArTPC) that was constructed and operated in the CERN North Area at the end of the H4 beamline. This detector is a prototype for the first far detector module of the Deep Underground Neutrino Experiment (DUNE), which will be constructed at the Sandford Underground Research Facility (SURF) in Lead, South Dakota, U. S. A. The ProtoDUNE-SP detector incorporates full-size components as designed for DUNE and has an active volume of 7 × 6 × 7. 2 m 3. The H4 beam delivers incident particles with well-measured momenta and high-purity particle identification. ProtoDUNE-SP's successful operation between 2018 and 2020 demonstrates the effectiveness of the single-phase far detector design. This paper describes the design, construction, assembly and operation of the detector components.
Construction of ProtoDUNE-SP was proposed to the CERN Super Proton Synchrotron Committee (SPSC) in June 2015  [1]  and, following positive recommendations by SPSC and the CERN -1 -Research Board in December 2015, was approved at CERN as experiment NP-04. ProtoDUNE-SP has since been constructed and successfully operated at the CERN Neutrino Platform (NP). The design of the ProtoDUNE-SP detector has been documented in a Technical Design Report  [2]. ProtoDUNE-SP prototypes most of the components of a DUNE single-phase far detector module at 1:1 scale, with an extrapolation of about 1:20 in total LAr mass. With a total LAr mass of 0. 77 kt, it represents the largest monolithic single-phase LArTPC detector built to date, and is a significant experiment in its own right. The ProtoDUNE-SP detector elements, the time projection chamber (TPC), the cold electronics (CE), and the photon detection system (PDS), are immersed in a cryostat filled with the LAr target material.
The detector continued to operate through July 19, 2020, collecting data to test and validate the technologies for the future DUNE far detector modules, demonstrate operational stability, and explore operational parameters. The construction, installation, and operation of the ProtoDUNE-SP detector, as described in this paper, has validated the design elements of the single-phase technology, the membrane cryostat technology and associated cryogenics systems, as well as instrumentation, data acquisition, and detector control. First performance results are presented in a separate paper  [3].
Detector components The far detector DUNE-SP TPC components are designed in a modular way so that they can be transported underground. The ProtoDUNE-SP components are therefore also modular. Six Anode Plane Assemblies (APAs) are arranged into the two APA planes in ProtoDUNE-SP, each consisting of three side-by-side APAs. Between them, a central cathode plane, composed of 18 Cathode Plane Assembly (CPA) modules, splits the TPC volume into two electron-drift regions, one on each side of the cathode plane. High voltage (HV) is delivered to the cathode plane by the HV feedthrough. A field cage (FC) completely surrounds the four open sides of the two drift regions to ensure that the -2 -electric field within is uniform and unaffected by the presence of the cryostat walls and other nearby conductive structures.
The detector dimensions are 7 m along the beam direction ( coordinate), 7. 2 m wide in the drift direction ( coordinate), and 6. 1 m high ( coordinate). The detector elements are suspended from the cryostat roof by the Detector Support Structure (DSS). The Photon Detection System (PDS) modules are embedded in the APAs. They will collect scintillation light from ionized LAr. Ten bar-shaped photon detectors with dimensions of 8. 6 cm (height) 2. 2 m (length) and 0. 6 cm (thickness) are installed at equally spaced heights within each APA. Three different designs of photon-detector technologies are implemented in ProtoDUNE-SP, all based on light readout by silicon photomultipliers (SiPM). Figure  1  illustrates how these components fit together. Cryogenic instrumentation, including a purity monitor, temperature sensors, and cameras, are located in the space between the cryostat walls and the detector elements.
Data acquisition and detector control The Data Acquisition (DAQ) system, a central element of ProtoDUNE-SP, interfaces with the detector's readout electronics, with external devices used for triggering (e. g. , beam instrumentation), with the Detector Control System (DCS), and with the offline computing. The DCS (known historically as "slow control") is in charge of the monitoring and control of the detector and includes hardware and software elements that give information and access to the detector subcomponents. The DAQ and DCS are described in section 6 of this paper.