dataesr/scientific-paragraphs-categorization huggingface dataset:
The scanning methods require time consuming measurements and/or analysis of the recorded signals to ensure the fidelity of the data. Various methods to determine the interaction positions of γ rays in segmented germanium detectors have been developed that rely on PSA techniques. These methods typically analyse the profile of the induced real and transient charge signals. Real signals are measured at the electrode of the segment in which an interaction takes place with their measured charge terminating at a non-zero value. Transient signals are measured on the electrodes of segments neighbouring the interaction, due to the capacitive coupling between these segments and the moving charges a non-zero charge is induced which ultimately terminates at a zero value. The resulting large datasets of digitised preamplifier signals from the single crystal are compared with the calculated existing set of signal pulses known as a basis.
The current AGATA approach is to use a 2 mm Cartesian grid, which results in ∼48,000 basis sites throughout the full volume or ∼ 700-2000 points per segment. Three different approaches for the simulation codes for AGATA have been pursued: the MATLAB-based Multi Geometry Simulation code (MGS)  [13, 14] , the Java-based AGATA Signal Simulation toolkit (JASS)  [15]  and the Cbased AGATA Detector Library (ADL)  [16]. Combined with the segmentation of the capsule, the PSA position resolution is expected to be ∼ 5 mm FWHM for the AGATA detectors. The reader should note that there is an energy dependence to this value, which will be discussed in more detail later in this review.
MGS The first results for the pulse shapes of a 36-fold segmented AGATA detector were described in pioneering publications on the MGS code  [13, 14, 17]. These papers reviewed the mechanisms influencing the process of signal induction. The solution of the electric field and the charge carrier drift phenomena were considered for arbitrary crystal geometries crucial for the three irregularly tapered crystals that make up an AGATA triple cluster. The sequence of steps which were needed for the generation of a data set of position-dependent pulse shapes, included: -Calculation of the electric field starting from the solution of Poisson's equation. -An implementation of charge carrier transport in a semiconducting medium. -Trajectories of charge carriers for arbitrary interaction positions inside the detector volume.
-Application of the Shockley-Ramo theorem providing the resulting charge recovery at the contacts. -Weighting potential and weighting field resolution. -Scanning of selected areas in the crystal. -Simulation of the charge-collection efficiency. The numerical algorithms were implemented using MAT-LAB  [18]. This matrix-oriented programming language is adapted for grid-based solving algorithms enabling the modelling of general geometries with the support of a cubic grid. Emphasis was given to the proper numerical solution of Pois-son's equation and three distinct solving algorithms were implemented in the MGS software package. In this work the dependence of the electron drift velocity anisotropy was taken from  [11]. Electric conduction depends on several parameters, such as the strength of the applied electric field, the crystal orientation and the lattice temperature.
ADL PSA performance The performance of ADL was reviewed and refined as reported in  [26] ). The position sensitivity of several AGATA detectors was determined by analysing the data of a 22 Na source measurement. In the decay process of 22 Na, a positron is emitted that annihilates with an electron very close to the point of emission. The positron has a maximum energy of 540 keV, a mean energy of 180 keV and a mean range of about 0. 24 mm  [27]. The annihilation photons produced which are emitted at an angle of near 180 • , allows for the assessment of the quality of the PSA results. For this purpose, the shortest distance of a straight line connecting the two identified interaction points to the known source position is determined. It should be noted that variations in the momentum carried by the neighbouring nucleus and the positron range provide a physical limit to the position determination, however, this is below the functional limit of the germanium detectors themselves.
The AGATAGeFEM software package A number of software packages have been developed to calculate pulse shapes from the High-Purity Germanium detectors used in AGATA (e. g. see the references  [12] [13] [14] [15] [16] 28, 29]  and other sections of this article). The complex shapes of the AGATA crystals can be more accurately described using a finite element scheme and finite element methods (FEM) than with very dense rectangular grids. It is beyond the scope of the present work to describe FEM, and the interested reader is referred to  [30]  and references therein. AGATAGeFEM also uses a different model for the hole mobility with respect to ADL  [10, 16]  and JASS  [15, 28, 29]. It is described in detail in  [31]. The use of FEM allows AGATAGeFEM to have a varied precision of the mesh used to calculate the electric field, it varies from several millimetres in regions of weak fields to tens of micrometres where the combination of strong field gradients and complex geometry are combined.
Another advantage of FEM is that the solution is an approximation of a function describing the electric potential and not the electric potential at the grid points. This removes possible ambiguities when calculating the electric field in the detector volume. The AGATAGeFEM package, written in C++, uses highquality open-source FEM software to calculate the electric and weighting potentials of AGATA type germanium detectors. The GNU Scientific Library  [32]  is used for solving the ordinary differential equation of charge-carrier transport. The geometry is described to within 10 -6 mm for charge transport and mesh generation. In AGATAGeFEM the solutions of the Poisson and Laplace equations are not projected down to a regular grid when used in the charge carrier-transport process. This is also the case for calculations of the induced signals via the Shockley-Ramo theorem.
A weighting field as calculated by AGATAGeFEM together with pulse shapes from a net-charge segment and its nearest neighbour are shown in Fig. 4. Using threads and the message passing interface  [33]  AGATAGeFEM is fully parallelised, both to calculate the field and the pulse-shapes. A graphical interface using the ROOT  [34]  interpreter interface allows for the calculation and display of the E-fields and pulses from ROOT. Furthermore, a very simple server client mechanism allows other programs to ask the server to calculate pulse shapes. The modulating effect of the response of the electronics is clearly seen (red shape). The effect of linear (green shape) and derivative (blue shape) cross-talk is also shown. For the net-charge segment the effect of the derivative cross-talk is too small to be seen The AGATAGeFEM package also contains miscellaneous codes for: -Applying preamplifier response.
The detector being characterised is then mounted above the table, in either the vertical or horizontal orientation, such that the beam of γ rays impinges on an active area of the detector. The XY position of interaction within the detector is inferred from the position of the scanning table and thus the collimated source. There are no constraints on the depth of interaction, along the z-axis. The source is raster scanned across the detector in a uniform grid, typically of 0. 5-2. 0 mm steps, and a database of detector response as a function of interaction position is recorded. Transmission through the collimator is of the order 10 -6 and so a source activity of hundreds of MBqs is required to give sufficient number of γ -rays exiting the collimator. 137 Cs is typically used as the γ energy of 662 keV is high enough to allow penetration into the bulk of the detector but not so high as to be difficult to shield and collimate.
An example mean signal for the core, hit and adjacent segments is shown in Fig. 9. The final mean representation for each interaction position is then stored in a database of known detector responses, for use in subsequent PSA. The coincidence scanning system developed at the IJC Lab, Orsay, uses the same basic principle as the Liverpool system except that the secondary collimators are mounted directly onto the front of the six ancillary NaI(Tl) TOHR detectors and constrain the scattering angles to ∼ 80 • and ∼ 100 • as described in  [6]  and shown in Fig. 10. Signals from the AGATA detector are digitised using TIGRESS digitisers [46], giving the same 100 MHz clock frequency and 14 bit resolution as the Caen system used at Liverpool. The benefits of this technique are that it gives a very precise knowledge of the position of interaction (∼ 2 mm 3 ) within the detector.
The Compton kinematics ensure that each event recorded is a true single-site interaction, the position of which is defined by the primary and secondary collimation. In addition, the timing of the interaction within the AGATA detector can be deduced from the ancillary scintillator detectors. The fast and predictable response of the scintillator provides precise measure of the start of the charge collection (t 0 ) than what is possible using the AGATA detector alone. The correct identification of t 0 is crucial for comparison of experimental and simulated data and is often difficult to determine from experimental data. The disadvantages are that the secondary collimation results in a very low efficiency and so measurement times can be very long (many hours per position leading to partial scans taking several months to complete) to ensure sufficient statistics are recorded.
These data also allowed the borehole diameter to be determined. In addition to the preliminary scans, the crystal lattice orientation may be deduced from a 2D scan with a 1 mm pitch using the pulse rise time T 10-90 (i. e. , between 10% and 90% of the pulse amplitude). The variation of T 10-90 along a circle of a given radius indicates the main germanium lattice 100 and 110 axes with a precision of about 1-2 • (now improved to 0. 4 • on average  [52]  ). A 137 Cs 3D scan with a 2 mm pitch of the AGATA capsule B006, following the procedure described above. This yielded the construction of a 48,500 point pulse-shape basis which is composed of 6 signals, for each (X, Y, Z ) scanned crystal coordinates. These were, the total energy (central contact), the net charge signal of the hit segment and the four transient signals induced in the neighbouring segments.
Simulation of the PSCS technique The PSCS technique applied at IPHC was simulated for the first time in  [52, 53]. The scan of the full-volume of a symmetric crystal, was performed using GEANT4 simulations for the γ -ray interactions in the crystal coupled to the ADL software  [16]  for pulse-shape generation. The latter were convoluted with the preamplifier response and realistic noise was added. The percentages of single interactions in the selected events for different energies ranging from 122 to 1408 keV were deduced. On average for energies above 600 keV, about 50% of the events are singles. This value increases drastically for lower energies. The influence of multiple interactions in a single segment on the average pulse shape at each point was studied. The simulations indicate that: -Several interactions of multi-scattering events occur rather close to each other, on average about 2 mm away from the scan-grid point.
-The pulse shapes of single and multiple-interaction events are very similar. -The larger the distance between the first and the furthest interaction point, the smaller the energy deposit at the latter point thus reducing its influence on the final pulse shape. Following this study it was concluded that the multiscattering events do not contaminate the pulse-shape database. The impact of the input statistics, i. e. , the number of pulse shapes in each vertical and horizontal data set, shows a larger effect than γ -ray energy, the PSCS performance being improved with higher statistics. Improvements of the PSCS technique may be further studied such as the metrics used and the weight associated to the transient signals in the neighbouring segments, the use of a smaller pitch and smaller collimator diameter.
With the source and γ -camera in a given location, a database containing pulse shapes for all the trajectories coming inside the coincidence cone of the characterisation detector and the γ -camera are recorded. In this way, data are collected from all positions simultaneously, rather than one at a time as in collimated beam measurements, leading to a significant reduction in data collection time. A second measurement is then taken with the source and γ -camera together rotated by 90 • around the characterisation detector (moving from position a to position b in Fig. 13 ) and data for this new configuration are recorded. The data sets from the two source/camera positions are then compared in a similar method as described in Sect. 3. 4. The point for which identical signals are observed in both data sets correspond to the crossing point of two lines inside the coincidence cone.
2015 Merlin et al. , 2016 Merlin et al. , 2019)) , and a module to estimate so-called concentration, asymmetry, smoothness (CAS) morphological parameters (non-parametric morphological features that can be used to distinguish between discs, ellipticals, compact, diffuse, symmetric and asymmetric, or clumpy objects by means of a dimensional reduction, see  Conselice 2003). However, the pipeline is foreseen to also include a profile model-fitting algorithm. The Euclid Morphology Challenge (EMC) was organised with the aim of analysing and comparing the performance of various model-fitting software tools on Euclid data, in order to establish the foundations for choosing the tools that will be integrated into the official processing pipeline. The final choice will be driven by many factors, including computational performance, robustness of the algorithm, and compatibility with the current version of the pipeline; however, the accuracy of the parameter estimates will of course be the main driver.
Therefore, assessing the performance of the different software packages on simulated data, for which the ground truth is known, is a necessary and fundamental step for a sound selection. Eight development teams of model-fitting software packages were invited to participate in the challenge, and five provided at least partial results. In this paper we present the dataset created for the EMC, and we discuss the results concerning photometry. In fact, albeit not being the central focus of the challenge, flux measurements obtained via model-fitting techniques will have great relevance, providing a crucial complement to the more straightforward methods already included in the pipeline. A companion paper is dedicated to the analysis of such morphological estimates  (Euclid Collaboration 2023). This paper is structured as follows.
Section 2 describes the technique used to create the simulated dataset for the challenge, with some technical details given in Appendix A. In Sect. 3 we briefly present the software tools being used in the challenge, and in Sect. 4 we describe the methods used to analyse and rank the data provided by the participants. The results are then presented in Sect. 5, where we investigate the general accuracy of the photometric measurements, as well as the reliability of the estimated uncertainty budgets, with a further focus on each software package's performance given in Appendix B. Finally, Sect. 6 presents a summary of the work and provides conclusions. All magnitudes are given in the AB system.
Simulating the Euclid universe Simulated datasets are being produced and used by the Euclid Science Ground Segment to test the full processing pipeline from image reduction to data analysis. These simulations consist of raw single exposures, including observational features and defects, and they must be processed and stacked to reach the nominal depth and be ready for scientific analysis, with background light and defects removed. To simplify this complex procedure, and to have all details under control, for the EMC we decided to produce a tailored dataset, directly simulating background-subtracted images at the expected nominal depths of the final stacked mosaics in all bands. With this approach, we were also free to try different options, producing simulations with single and double component analytical profiles, and also with realistic morphologies.
Catalogues and images creation We started by creating mock cosmological catalogues with the code  Egg (v1. 3. 1, Schreiber et al. 2017). Egg uses the statistical distributions of real galaxies as detected and classified in the five CANDELS fields  (Grogin et al. 2011; Koekemoer et al. 2011)  to build a simulated catalogue of a patch of the sky, complete with the properties of the objects as observed by a chosen set of pass-band filters, with a chosen pixel resolution, and to a chosen limiting magnitude. We refer the reader to the paper describing the code for a detailed description of its workflow; here we provide a short summary. Egg draws redshifts and stellar masses from observed galaxy stellar mass (M * ) functions, and subsequently attributes a star-formation rate (SFR) to each galaxy from the observed SFR-M * main sequence; dust attenuation, optical colours and simple disc-plus-bulge morphologies are obtained from empirical relations established from the highquality Hubble and Herschel observations of the CANDELS fields.
e. beyond ∼1 Mpc at z > 0. 5). We created five catalogues, each one with a size of 0. 482 deg 2 (41. 66 per side, which is comparable to the typical area on which each single photometric catalogue will be extracted from real data), with limiting magnitude I E = 27. 1 (the nominal 1σ limit as given in  Laureijs et al. 2011). The total number of simulated galaxies was about 1. 5 million. We then used an Egg built-in script to obtain all the observational properties of the sources. In particular, for each galaxy the following parameters are given: position of the centroid in pixels; total flux in the simulated band; bulge-to-total flux ratio (b/t); scale length of the bulge and of the disc (defined as the radius at which the component is a factor of e less bright than it is at its centre); axis ratio for both components; and position angle for both components.
2015, v2. 2. 1), a Python package that produces simulated astronomical images (it is also used for the official Euclid simulations). We created the images at their expected native pixel scale: 0. 1 for I E (25 000 × 25 000 pixels); 0. 3 for NIR bands; and 0. 2 for LSST bands. After the procedure described in the following paragraphs, we resampled all the images to the I E pixel scale, since this is the procedure that will be followed in the real pipeline. To simulate the effects of point spread functions (PSFs), we provided GalSim with the Euclid Mission Database models for the I E and NIR bands (as provided by the corresponding Euclid working groups; both of them are over-sampled by a factor of 6), while for LSST we provided custom simulated PSFs created using PhoSim  (Peterson et al. 2015) , at the expected observed pixel scale of 0.
(2021)  and Euclid Collaboration (2022a); each simulated galaxy mimics the properties of its corresponding analytical realisation. In this dataset, the biggest and brightest objects (r e > 0. 2, I E < 20. 5) are not simulated due to technical limitations; the list of excluded sources was provided to the participants, and accounts for approximately 1% of the total simulated galaxies. Also, the position angles of the galaxies are not constrained to be close to those of the Egg catalogue (and therefore they were not considered in the final analysis of the results). We point out that this is the first time that such a demanding test has been performed: the codes must provide an analytical fit on non-analytical shapes for which a groundtruth value is known. This is inherently a very challenging task.
For example, looking at the plot one can see that galaxies at z = 3 typically begin to be detectable at I E = 23. Figure  3  shows the distribution of simulated galaxies in the magnitude-size-b/t space for the same field (note how there is a non-negligible fraction of bulge-only objects with b/t = 1). Finally, in Fig. 4  we show the distributions of various input parameters for the I E band components  and realisations (magnitude, effective radius, axis ratio, Sérsic index, bulge-to-total ratio and redshift), for the full dataset (the five fields), down to the nominal 5σ limit I E = 25. 35  (Laureijs et al. 2011) ; we also show the COSMOS distributions  (Mandelbaum et al. 2012)  for reference. As expected, simulations and observations agree remarkably well, with the exception of the b/t distribution, which is more skewed towards disc galaxies in the Egg catalogue.
Observational noise Once the seed images containing the sources were produced, we proceeded to add simulated observational noise. First of all we replaced the smooth analytical profiles with stochastic realisations from a Poissonian distribution, to simulate the effects of photon shot noise. We used the Python module scipy. stats. poisson. rvs for this purpose. We paid particular attention to keep the units of the images always consistent during the whole process: we first converted the noiseless images from µJy/pixel to observational units, using the correct image observational ZP at 1 s, to obtain consistent Poissonian realisations, which depend on the total exposure time. Only after this step did we convert the images back to µJy. To calculate the ZPs we followed the procedure described in Euclid Collaboration (2019), which we summarise in Appendix A.
7  shows a small crop of the DS, SS and RM F0 I E images. A sample of the ground-truth input values of the simulated galaxies was also provided to the participants (in particular, a small portion of the multi-band F0 dataset and the whole F4 I E dataset), allowing for a check that their procedures were reasonably correct without evident errors. The output requested from each participant consisted of the estimates of (i) flux, (ii) Sérsic index (in the SS and RM realisations), (iii) half-light semi-major axis, (iv) axis ratio, and (v) position angle, each with a corresponding 1σ uncertainty, for each component of the simulated objects. In particular, while the SS and RM realisations only required a single fit with free n, for the DS realisation we asked for two estimates, namely: one fit with fixed indices (n disc = 1 and n bulge = 4, consistent with the way the images were simulated); and another with n disc = 1 and n bulge left free to vary.
Model-fitting software packages Eight development teams of model-fitting software packages were invited to participate to the challenge; of them, five (DeepLeGATo, Galapagos-2, Morfometryka, ProFit, and SourceXtractor++) provided at least partial results. All but one are based on parametric methods, using functional forms to fit the observed light distributions, the exception being DeepLeGATo, which exploits a convolutional neural network (CNN). Here we briefly summarise the basic properties and features of these five software packages, and point out a few important details about the procedures each one followed. It is instructive and important to notice how various subtleties in the interpretation of the requests, usage of the provided input datasets, and processing methods used by each participant led to differences in the format and accuracy of the provided outputs.