dataesr/scientific-paragraphs-categorization huggingface dataset:
Piezoresistive readout exhibits similar mass-equivalent noise as the optical readout We first tested our SNR devices without particles flowing through in order to evaluate their noise performance or equivalently their mass resolution. In particular, we calculated the mass-equivalent noise by measuring the resonance frequency fluctuations, extracting the Allan deviation and converting it into mass (Supporting Information 6). We used this mass-equivalent noise to compare the performance of the piezoresistive readout to the optical readout method described in previous work  13. We acquired data for 15 minutes with a sampling rate of 10 kHz for cantilevers not containing liquid. We found that the piezoresistive readout from a single cantilever (Q=1,979) exhibited a mass-equivalent noise of 3 ag with a 1 kHz bandwidth, similar to what we observed with the optical readout (Figure  3a ).
(b) Average throughput of 4 resonators in a parallel SNR array measuring suspensions of 40 nm-diameter gold nanoparticles with concentrations ranging from 0. 18x10 9 to 90x10 9 nanoparticles/mL. For all tested sample concentrations, we used the same measurement time of 5 minutes. (c) Comparison of throughput and particle transit times for the experiment in a) between a parallel SNR array with 9 resonators and single-resonator SNR. The transit time of each nanoparticle was computed using an existing MATLAB algorithm  13  that extracts the time difference between when the nanoparticle entered and exited the resonator. The inset in (c) shows a schematic of the parallel SNR array used for measuring the particles. The flow in the top bypass channel was pinched (high pressure at both inlets) to force the sample to flow from the left inlet into the embedded channel of the resonators.
The transit time of each nanoparticle was computed using an existing MATLAB algorithm 13  that extracts the time difference between when the nanoparticle entered and exited the resonator. The inset in (c) shows a schematic of the parallel SNR array used for measuring the particles. The flow in the top bypass channel was pinched (high pressure at both inlets) to force the sample to flow from the left inlet into the embedded channel of the resonators.
Funding Sources This research was supported by the  LETI Carnot Institute EVEREST project , the outgoing CEA fellowships (V. A. ) from the  CEA-Enhanced Eurotalents program , cofunded by  FP7 Marie Skłodowska-Curie COFUND program  (Grant Agreement  600382 ), the  Fulbright fellowship for the France National Researcher program  (V. A. ) and by the  Institute for Collaborative Biotechnologies  through grant  W911NF-09-0001  from the  US Army Research Office  (S. R. M).
The full 3D reconstructed complex density function is available upon request from the authors as a Matlab data file. principle possible to image micro-or nanostructures grown on a substrate or embedded in a matrix, which would not be possible with classical imaging methods. Successful reconstructions have been obtained on other strained epitaxial systems such as siliconon-insulator and SiGe nanostructures  28, 29. In the present study, we show that good quality 3D electron density and strain maps can be extracted by BCDI in an even more challenging case, where the GaAs 0. 991 N 0. 009 planar structures under investigation are directly grown on the substrate, and not supported by amorphous oxide buffer layers. Here, the contrast producing the images is created by the tiny separation of the Bragg peak of GaAs 0.
958 N 0. 042 :H walls. This is also confirmed by FEA simulations (Fig. 5d ). This result demonstrates the possibility of fabricating highly strained nanostructures of arbitrary shape. 2a  due to a different orientation of the micro-triangles with respect to the x-ray beam. In the Cartesian axis representation "z" indicates the vertical direction, "y" the direction of the x-ray beam, "x" the direction transverse to the beam. The full 3D reconstructed complex density functions for the two microstructures are available upon request from the authors as Matlab data files. However, the blurring of the reconstructed triangle profile suggests that the precision of the fabrication process still needs improvement; this consideration is possible thanks to the high resolution (~25 nm) of the reconstructed image.
Acknowledgements This work benefited from the SOLEIL beam time allocations No. 20131027  and No. 20141387  (CRISTAL beamline) and in-house research time (SIRIUS beamline). We thank  Synchrotron SOLEIL  for general facilities placed at our disposal; we are grateful for technical support by  F. Legrand  and  N. Aubert. We acknowledge  S. Rubini ,  F. Martelli , and  L. Grenouillet  for providing the GaAsN samples. G. P. , M. F. , and  A. Po. acknowledge funding from the  Italian MIUR  (under  FIRB  project  DeLIGHTeD ,  Protocollo   RBFR12RS1W ). A. Po. and M. F. acknowledge funding from the  Regione Lazio programme "Progetti di Gruppi di ricerca" legge Regionale  n. 13/2008  ( SIN-FONIA  project, prot. n. 85-2017-15200 ) via LazioInnova spa. A. Pa. acknowledges the support of the  Engineering and Physical Sciences Research Council  (grant number  EP/ J015296/1 ).
See Table  2  for the on-axis results, and for the observational setup at each band. Note that, as no other calibrators besides 3C 279 were observed in the 11×11 tests, we perform every step of the reduction process using 3C 279. We first split out the scans where 3C 279 was located at the pointing center (i. e. , the central point of the 11×11 grid) and concatenate them into a separate dataset; we use these on-axis data to derive the on-axis polarization calibration via the current ALMA polarization calibration scheme. For further details about standard, on-axis reduction of ALMA polarization data, see  Nagai et al. (2016)  as well as the 3C 286 Polarization 15 The actual size of the ALMA primary beam is slightly larger than the standard assumed beam size of 1. 22 λ/D, where λ is the observing wavelength and D is the antenna diameter.
With the correct model of 3C 279 in hand, 17  we then re-derive the gains (amplitude and phase) using the model in order to remove the polarization response from the gain amplitudes. Finally, we derive the D-terms. After doing so, we apply all of the calibration tables (the bandpass; the modified gains after removing the source polarization; the cross-hand delay; the XY -phase; and the D-terms) to our on-axis dataset. After applying the standard on-axis calibration to all of the on-and off-axis data, we perform one round of phase-only self-calibration (selfcal) to correct the residual phase errors as a function of time for both the on-axis data and each off-axis pointing. With the exception of the selfcal phase solutions, all calibration tables that we apply to the off-axis data are solved for using on-axis data only-this includes D-terms and XY -phase.
22 21 The theoretical sensitivity per pointing in the Band 3 Stokes I maps σ I is estimated to be ∼ 30 µJy beam -1 given 7 min of integration time per field, whereas the estimated noise level in all of the Band 3 mosaic images is ∼ 100 µJy beam -1. We see a similar situation in the Band 6 data, where the theoretical sensitivity σ I is ∼ 79 µJy beam -1 given 2. 2 min of integration per field, whereas the estimated noise from the Stokes I maps is ∼ 2 mJy beam -1. The even larger difference between the theoretical versus actual noise in the Band 6 maps is not surprising given the higher angular resolution of the Band 6 data (and thus limited ability to recover extended structure, and larger resultant imaging artifacts) relative to the Band 3 data. 22 The Band 6 mosaics have strong negative and positive "bowls" and "ridges" at the edges of the image, parallel to the main axis of the star-forming filament.
5. 2. Comparison with CARMA Another mosaicked polarization observation of Orion-KL was performed by  Hull et al. (2014)  using the CARMA interferometer's full-polarization system  (Hull & Plambeck 2015). Here we compare those archival data from an independent instrument with our ALMA results in order to characterize the performance of ALMA linearpolarization mosaics. The 1. 3 mm CARMA data were taken in 2013, have a resolution of 3. 07 × 2. 45, and comprise a seven-pointing mosaic with a field of view similar to that of our ALMA observations. In order to perform the most accurate possible comparison between the CARMA and ALMA datasets, we match the uv-range (using the uvrange keyword in the CASA task clean) and the synthesized-beam size (using the CASA task imsmooth) of the Band 3 and Band 6 ALMA data to those of CARMA.
We also re-grid the ALMA images to match the coordinates of the CARMA maps. We use the standard Nyquist mosaic pattern from our ALMA data for the comparison with the CARMA observations, which are also Nyquist sampled. See Figure  8  for an overlay of the inferred magnetic field from the CARMA data with both the Band 3 and Band 6 ALMA data. We can see that the magnetic field orientation in both the Band 3 and the Band 6 ALMA maps matches the CARMA observations remarkably well, especially in the Northern Ridge. This consistency of inferred magnetic field orientation as a function of frequency is expected in optically thin material (which, we should note, might not be the case toward the very central region near Source I), under the standard assumption that dustgrain alignment via radiative alignment torques (RATs; Lazarian 2007) is the cause of the dust polarization.
8. Finally, we perform a comparison of both the 3 mm and 1. 3 mm ALMA mosaics with a 1. 3 mm CARMA mosaic from  Hull et al. (2014). After matching the uv-coverage of the ALMA and CARMA data and then smoothing the ALMA data to the coarser CARMA resolution, we find good consistency in the polarization position angles in both bands, especially in the Northern Ridge region of Orion-KL. The distribution of differences in χ in the CARMA versus the ALMA maps has a width of ± 12 •. Our results show that, due to the large angular extent of the beam-squash pattern of the ALMA 12 m antennas, the errors introduced by this wide-field effect are modest. We show that mosaicking reduces the magnitudes of these errors because multiple pointings are overlapped, and because the mosaic-imaging algorithm puts a higher weight on emission that is on-axis versus emission that is far from the pointing center.
However, ultimately we would like to be able to calibrate out these effects, thus yielding accurate wide-field polarization images even in single-pointing observations. Efforts to implement full-polarization voltage-pattern corrections (i. e. , primary beam models) at ALMA are underway, and will allow us to achieve this goal in the future. Hull et al. B. Error estimates in a polarization mosaic using the 11×11 observations We perform a simple statistical analysis based on the the 11 × 11 observations of the highly linearly polarized blazar 3C 279 (see Sections 2 and 3) in order to use those data to estimate the errors in a polarization mosaic. These estimates are an effort to connect the 11×11 results with the error results from the Orion-KL mosaic data that we report in Section 5. In single-pointing observations, the image can be divided into the inner region (which we define as r < 1 3 FWHM) and the outer "donut" region (r > 1 3 FWHM).
acknowledges the support of both the  NAOJ  Fellowship as well as  JSPS KAKENHI  grants  18K13586  and  20K14527. V. J. M. L. G. acknowledges the support of the  ESO Studentship Program. This paper makes use of the following ALMA data: ADS/JAO. ALMA# 2011. 0. 00007. E and ADS/JAO. ALMA# 2011. 0. 00008. E (Orion-KL mosaics), and  ADS/JAO. ALMA# 2011. 0. 00009. E (11×11 data ). ALMA is a partnership of  ESO  (representing its member states),  NSF (USA)  and  NINS (Japan) , together with  NRC (Canada) ,  MOST  and  ASIAA (Taiwan) , and  KASI (Republic of Korea) , in cooperation with the  Republic of Chile. The Joint ALMA Observatory is operated by  ESO ,  AUI/NRAO  and  NAOJ. Facilities:  ALMA.
(2009). For the catalogue of detections (4XMM-DR9), source parameterisation was done before cross-correlation of the source list with a variety of archival catalogues, image databases, and other archival resources. The creation of spectra and light curves for the brightest sources was then carried out. Automatic and visual screening procedures were carried out to check for any problems in the data products. The data from this processing have been made available through the XMM-Newton Science Archive 4  (XSA), but see also Sec. 10.
Other changes carried out specifically for the production of 4XMM include a revised systematic position error (see Sec. 3. 3), the modelling of the EPIC background (see Sec. 3. 4) and finer binning of EPIC lightcurves (see  Sec. 4. 1). A small rotation of ∼0. 4 • was noted in 3XMM fields, but analysis of 4XMM data shows that the recent improvements to calibration have resolved this issue. Below we describe some of the more recent developments specifically implemented for 4XMM.
However, where catcorr fails to obtain a statistically reliable result (poscorrok=false), a systematic error of 1. 5 ′′ was used to create the 3XMM catalogue. In the framework of creating 4XMM, this systematic error was re-evaluated. In order to determine an improved systematic error, we identified fields in 3XMM-DR8 where catcorr failed. We used sources from the Sloan Digital Sky Survey Data Release 12 quasar (SDSS DR12 QSO) catalogue  (Pâris et al. 2017 ) with good quality spectra (ZWARNING=0) and point-like morphology (SDSS_MORPHO=0). To avoid mismatches between targets and matched photometry 6  we chose non-empty OBJ_ID values. We then cross-matched with the SDSS DR9 photometry catalogue  (Ahn et al. 2012 ) in Vizier 7  with a maximum distance of 5 ′′. This step provided the uncertainty in the astrometric position of SDSS.
Modelling the EPIC background For each input image to the source detection, the background is modelled by an adaptive smoothing technique. The method was initially applied to the data in the 3XMM-DR7s catalogue which treats overlapping XMM-Newton observations and is described by  Traulsen et al. (2019). Since 3XMM-DR7s was based on a selection of clean observations, the smoothing parameters were revised for the 4XMM catalogues, which cover observations of all qualities. The three parameters of the smoothing task are the cut-out radius to excise sources, the minimum kernel radius of the adaptive smoothing, and the requested signal-to-noise ratio in the map. Their best values were determined in a three-fold assessment which involved real observations, randomised images, and visual screening.
Lightcurve generation Lightcurves are corrected using the SAS task, epiclccorr, to take into account events lost through inefficiencies due to vignetting, bad pixels, chip gaps, PSF and quantum efficiency, dead time, GTIs and exposure. epiclccorr also takes into account the background counts, using the background lightcurve, extracted over the identical duration as the source lightcurve. The time bin size for the pn lightcurves was previously set to a minimum of ten seconds and could be as poorly sampled as tens of thousands of seconds for the faintest sources. To exploit the high time resolution and high throughput of the pn, for 4XMM we now extract the pn lightcurve such that each bin is 20 times the frame time, usually 1. 46 s. The binning of the MOS data remains as it was for 3XMM.
Variability characterisation As in 3XMM, the χ 2 test was used to determine if a source is variable during a single observation. Variability was defined as P(χ 2 ) ≤ 10 -5. We also gave the fractional variability, F var , to provide the scale of the variability  (Rosen et al. 2016 ). These values are still provided in the 4XMM catalogue. The χ 2 statistic can be applied to binned data sets where the observed number of counts in a bin deviates from expectation approximately following a Gaussian distribution. Cash (1979)  showed that when the number of counts per bin falls below ∼10-20, the χ 2 statistic becomes inaccurate. Therefore, as the pn lightcurves are now binned to 20 × frame time (Section 4. 1), these data are rebinned to contain 20 counts per bin before applying the variability tests.
Despite the new naming convention that aims at preserving SRCID numbers across catalogue versions, a certain number of SRCID can disappear from one catalogue version to another. This is a normal consequence of the algorithm that groups detections together into unique sources (see section 6 of  Rosen et al. 2016). When new data are added and statistics are improved, the algorithm might find a better association of detections into unique sources. As an example, a total of 134 SRCIDs listed in 3XMM-DR7 are absent in 3XMM-DR8.
Missing detections and DETID change In addition, the pipeline reprocessing of the full public dataset for the 4XMM version of the source catalogue led to significant modifications of the detection list. There are 10 214 observations that are common between the 3XMM-DR8 and 4XMM-DR9 catalogues, resulting in 773 241 detections in 3XMM-DR8 and 726 279 detections in 4XMM-DR9. Of these, there are 608 A&A proofs: manuscript no. 4XMMv7 071 point-like detections with a SUM_FLAG 1 in 3XMM-DR8 and 607 196 in 4XMM-DR9. However, amongst these observations, there are ∼ 128 000 detections that appear in 3XMM-DR8 that are not matched with a detection in the same observation in the 4XMM-DR9 catalogue within a 99. 73% confidence region (that is, 2. 27 × POSERR). About 67 000 of these were classified as the cleanest (SUM_FLAG 1), point-like sources in 3XMM-DR8 -these are referred to as missing 4XMM detections in what follows.
Multi-wavelength data taken as a part of the XID (X-ray identification project) run by the SSC over the first fifteen years of the mission are also provided in the XIDresult database  16. The XMM-SSC catalogue server 17 provides access to each source and regroups information concerning all of the detections for a unique source. It also provides the XMM-Newton lightcurves and spectra and permits the user to undertake simple spectral fitting, as well as overlays of the same region of sky in all wavelengths. The catalogue can also be ac-N. A. Webb et al. :  The XMM-Newton serendipitous survey cessed through HEASARC 18 and VIZIER 19. The results of the external catalogue cross-correlation carried out for the 4XMM catalogue (section 9) are available as data products within the XSA or through the XCat-DB.
Acknowledgements. We are grateful to the anonymous referee for careful reading of the manuscript and for providing useful feedback. We are grateful for the strong support provided by the  XMM-Newton SOC. We also thank the  CDS team  for their active contribution and support. The French teams are grateful to  Centre National d'Études Spatiales (CNES)  for their outstanding support for the SSC activities. SSC work at AIP has been supported by  Deutsches Zentrum für Luft-und Raumfahrt (DLR)  through grants  50OX1701  and  50OX1901 , which is gratefully acknowledged. FJC acknowledges financial support through grant  AYA2015-64346-C2-1P  ( MINECO/FEDER). MTC and FJC acknowledge financial support from the  Spanish Ministry MCIU  under project  RTI2018-096686-B-C21  ( MCIU/AEI/FEDER/UE ) cofunded by  FEDER  funds and from the  Agencia Estatal de Investigación ,  Unidad de Excelencia María de Maeztu , ref.
The purpose for using two chambers, with each containing two independent sensitive volumes, is to reduce the statistical uncertainty for the 16 O(n, α) reaction measurement. In addition, one part of the DFGIC contains 235 U and is used to monitor the neutron flux. All detector signals were digitized with waveform digitizers (SPDevice, ADQ14, 14bit), recording each of the 10 detector signals coming from the two chambers, by a digital data acquisition (DAQ), which is illustrated in Figure  1 b ). The waveforms were recorded, with an accelerator frequency of 0. 8 Hz, up to 1 ms time window with a sampling rate of 250MS/s. Due to the high instantaneous neutron flux of the facility the pile-up of neutron induced background reactions induces charges in the DFGIC with up to two orders of magnitude higher than one induced by α-particles.
The DFGIC data analysis requires, besides the amplitude information, also the time information of the start and end of the signal edges  [6]. For this purpose the storage of waveforms and analysing the signal edges would be suited. However, the accelerator frequency at nELBE and data rate are quite high that usually a storage of waveforms is very difficult. On the other hand, extracting all needed information from the signal edges is difficult to perform with an analogue DAQ. Since it was in advance unclear which type of DAQ would be the best, a complex data acquisition, shown in Figure  4 , consisting of both an analogue and digital branch, has been developed. At nELBE an analogue DAQ, like it is illustrated in Figure  4  a) for the fission ionization chamber, is routinely used. Both the time and energy information are measured by the DAQ in list-mode.
The digital DFGIC DAQ, illustrated in Figure  4 b ), uses digitizers (CAEN DT5730, 14bit) to record the waveforms of the 10 DFGIC detector signals with a time window of 3500 ns and a sampling rate of 500 MS/s. Furthermore the time stamp-and amplitude information extracted with the internal firmware of the digitizer were recorded. All the data of the digital DFGIC DAQ is stored as ROOT trees  [9]. In the final configuration 6 cm thick lead filters, attenuating the intensity of the γ-flash, in order to reduce the data rate were used. This allowed for the first time at nELBE to run an experiment with a fully working waveform recording digital DAQ, at the maximum accelerator frequency of 400kHz. For all types of DAQ's the detector signals were independently triggered and correlated with the accelerator.
The MBS-list-mode data from the analogue DAQ are analysed with the software package Go4  [10]. Although the internal firmware of the digitizer and Go4 were a good tool for performing an online analysis of the DFGIC signals during the beam time, the informations from that CSP signal edges could not be extracted with sufficient precision. For this reason the main analysis has been focussed on the stored waveforms of the digital DAQ. First step of the analysis is building the coincidence between Anode, Grid and Cathode signal of the corresponding chamber part. Afterwards a waveform analysis, extracting amplitudes and time stamps of signal start-and end from the edges of Anode and Cathode signal, has been performed. The last step of analysis is the discrimination of (n, α) reactions from background.
For the first time at nELBE this measurement has been successfully performed with a digital DAQ system. The data analysis is still ongoing, but already reveals (n, α) reactions. Furthermore the neutron induced background could be identified and disentangled. From the measurement at nELBE it is clear that the largest background contribution was resulting from the electrode foils. For the experiment at the n_TOF facility hydrogen containing aluminized Mylar electrode-foils were used, which were originally chosen to reduce the impact of the γ-flash. In retrospect, it turned out that this Mylar foil was the main source of the neutron induced background. For future experiments, using the DFGIC, the choice of the.