dataesr/scientific-paragraphs-categorization huggingface dataset:
The Photometry To include the LBT/LUCI + ARGOS data in our catalog alongside the HST photometry, we first translate the central locations of our photometric apertures defined by the F160W image onto the K band using the WCS information. Although the FWHM resolution of our LUCI1 K-band data (FWHM ∼ 0. 29) is higher than that of our two HST bands (0. 22 and 0. 18 for F110W and F160W bands, respectively), we do not alter the aperture sizes and ellipticities, as there is adequate matching to detect the vast majority of the sources. The data from LUCI1 and LUCI2 are obtained under different weather conditions, and the field orientation angles and plate scales are slightly different for LUCI1 and LUCI2. As a result, we opt to conduct K-band photometry separately for LUCI1 and LUCI2 images and only afterward to compute the aperture fluxes by applying an inversevariance-weighted mean of the two values.
Arclet families, in turn, constrain the model by imposing the condition that each family member image originates from the same source. The best-fit model is the one that minimizes the an- For Arcs 1a and 1b we present the three-color image consisting of the HST WFC3-IR F110W and F160W bands and the LBT LUCI+ARGOS K-band. For Arcs 9d? and 9e?, we first subtracted off the cluster light using Galfit prior to producing the two-band color image. The high-resolution K-band data set extends the reach of HST at comparable spatial resolution, thereby enabling the discovery of the ultrared counterimage of the lensed DSFG, Arc 1b, which is also confirmed by our Spitzer image at 3. 6 µm (Figure  5 ). The image stamps all have a 1 = 5 kpc bar (assuming 10 = 50 kpc) provided for reference. The images maintain the same orientation as in Figure  2.
Arc 1a is bright, K AB = 18. 74 +0. 02 -0. 02 mag, and well suited for spatially resolved spectroscopic follow-up observations. A counterimage is also predicted, which is not detected in our HST data set but is detected in the LBT/LUCI + ARGOS K-band image at the modelpredicted location. A bright image at the exact modelpredicted location is also detected in our Spitzer/IRAC imaging data. We designate this arc as the counterimage G165 DSFG 1b (see Figure  5 ). Interestingly, while the F160W AB -K AB color is consistent between the two images, there is an offset in the F160W AB -S 3. 6,AB color by a large > ∼ 2. 6 mag. This color difference is owing at least in part to contamination. G165 DSFG 1b appears to be situated behind a bluer and lower-redshift galaxy, which influences the photometry and therefore renders the color unreliable (see Figure  5 ).
SUMMARY Searching wide-field imaging data sets for giant arcs is now fairly common, yet conducting searches for unresolved giant arcs at submillimeter wavelengths is still relatively rare. We obtained HST WFC3-IR imaging of the fields of six lensed DSFGs selected in a novel search by their rest-frame FIR color and compactness using Planck/Herschel data. We extend the analysis on the G165 field, which shows prominent giant arcs and arclet families. We find the following: 1. Each of our six sample fields unveils the HST WFC3-IR counterpart of the strongly lensed DSFG. In four fields, the DSFG image appears as an image multiplicity HST resolution (G165, G045, G145, and G080). 2. For the G165 field, we obtained ground-based spectroscopy using MMT/Hectospec and Gemini/GMOS. We measure 51 new redshifts, which augment the spectroscopic catalog of objects in this field by a factor of five.
We also make the first detection of its counterpart, Arc 1b, at the model-predicted location, which is is too faint and too red to detect in our HST data set. 4. In total, for the G165 field we identify 11 arclet families by their similar colors and morphologies, which are all new. Obvious axes of symmetry corroborate our arclet family designations. In the NIR, Arc 1a subtends 5 and is magnified by a factor of > ∼ 30. Arc 1b is fainter and detected only in our high-resolution LBT/LUCI + ARGOS Kband image and Spitzer/IRAC images. We measure an F160W AB -S 3. 6 color difference between the two images that arises because Arc 1a is a merging image and so represents only a portion of that background source, while Arc 1b uncovers the entire source image. 5. We use the LTM approach to construct a mass map in the fields for which there is at least one ar-clet family detected in our data set (G165, G045, G145, and G080).
Figure6. Histogram of spectroscopic redshifts in the G165 field. The redshift catalog combines results from our MMT/Hectospec and Gemini/GMOS data sets (green), and objects drawn from the literature (SDSS, DR 14; magenta). We measure a value for the lens redshift of z = 0. 351, based on 18 cluster members in the range of 0. 326 < z < 0. 376. The inset histogram peaks at the lens redshift and shows a secondary peak at higher redshift that may indicate the presence of an unrelated background galaxy group. These cluster members range in position from the center out to a clustercentric radius of ≈ 0. 8 Mpc.
We appreciate helpful discussions with  Eiichi Egami ,  Xiaohui Fan ,  Dan Marrone ,  Ann Zabludoff , and  Scott Tremaine , and we especially thank the anonymous referee for comments that significantly improved this paper. Support for program HST GO-14223 was provided by  NASA  through a grant from the  Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Inc. , under  NASA  contract  NAS5-26555. J. M. D. acknowledges the support of project  AYA2015-64508-P (MINECO/FEDER , UE). R. A. W. was funded by  NASA JWST Interdisciplinary Scientist  grants  NAG5-12460 ,  NNX14AN10G , and  80GNSSC18K0200  from  NASA Goddard Space Flight Center. M. P. and A. B. were funded by a  UA/NASA   Space Grant for Undergraduate Research. This work is based in part 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.
b. Investigation of the contrast effect : boron concentration It was previously shown that the electron energy, the surface termination, the epilayer thickness and the boron concentration have an influence on the contrast revealed by FE-SEM. As we delimited the doping concentration range required to observe defect contrasts, the effect of boron on the average grey level on FE-SEM images was also investigated. Four epilayers of different boron concentrations measured by SIMS were analyzed separately using the FE-SEM at 20kV. The working distance (2. 5 mm), the magnification (x100), the contrast (25%) and the brightness (50%) were carefully kept constant during FE-SEM imaging. The average grey level of each image taken at the center of the samples (700x700 µm²) was measured using Image J software and was plotted against the boron concentration (Figure  5 ).
Ground-based adaptive optics (AO) systems can use temporal control techniques to greatly improve image resolution. A measure of wind velocity as a function of altitude is needed to minimize the temporal errors associated with these systems. Spatio-temporal analysis of AO telemetry can express the wind velocity profile using the SLODAR technique. However, the limited altitude-resolution of current AO systems makes it difficult to disentangle the movement of independent layers. It is therefore a challenge to create an algorithm that can recover the wind velocity profile through SLODAR data analysis. In this study we introduce a novel technique for automated wind velocity profiling from AO telemetry. Simulated and on-sky centroid data from CANARY -an AO testbed on the 4. 2 m William Herschel telescope, La Palma -is used to demonstrate the proficiency of the technique.
A qualitative study of the wind velocity profiling technique is carried out using simulated natural guide star (NGS) and laser guide star (LGS) data from CANARY, an AO demonstrator for Extreme Large Telescope (ELT) technologies on the 4. 2 m William Herschel telescope (WHT), La Palma. We also demonstrate our wind velocity profiling technique using on-sky CANARY data  (Morris et al. 2014 ). On-sky wind speed profiling results are compared to European centre for medium-range weather forecasts (ECMWF). They are also compared to contemporaneous profiles that were measured using the scintillation detection and ranging (SCIDAR;  Shepherd et al. 2013)  technique. These profiles were measured by the Stereo-SCIDAR instrument that was being operated on the 2. 5 m Isaac Newton telescope (INT), La Palma  (Osborn et al.
Covariance map The   and cov(y 1 ,y 2 ) for the configuration shown in Fig. 1. Each SHWFS combination then averages cross-covariance as a function of sub-aperture separation in x and y (x sep and y sep , respectively). The resultant array is known as a covariance map. A 2-NGS 7 × 7 SHWFS covariance map is shown in Fig. 2. The covariance map in Fig. 2  corresponds to Fig. 1 , i. e. there is a turbulent layer at 0 and 3D/7θ km. Both layers are characterized by L 0 = 25 m and r 0 = 0. 1 m, where r 0 denotes the Fried parameter. The SHWFS centroids used to calculate Fig. 2  were simulated using Soapy 1 : a Monte Carlo AO simulation package  (Reeves 2016). Table  1  lists the values used in the Soapy configuration file. For this type of system it is believed that 10,000 frames optimizes its statistical convergence  (Martin 2014).
The CAW results from the NGS simulated dataset are summarized in Table  2. The NGS study was repeated for sodium LGSs that had an apparent magnitude of 8 and were focussed at an altitude of 90 km. The SHWFSs were simulated to measure a monochromatic wavelength of 589 nm. All of the other simulated parameters were the same as those listed in Table  1. Due to the cone effect the maximum altitude of sub-aperture optical path intersection for the LGS asterism was roughly 19 km. Therefore, when repeating the CAW 1 measurement on the simulated dataset a layer was not fitted at 24 km. The LGS optical turbulence profiling results are shown in Fig  5. As the fitted layer at 20 km has its bin extend past the maximum altitude of sub-aperture optical path intersection, it is not included in LGS F md analysis.
The 6 layers fitted in CAW 1 were fitted during CAW 2 and CAW 3. Out of the 10 simulations, the layers fitted at 16 and 20 km were validated 6 and 0 times, respectively. The mean validated layers are shown alongside the NGS results in Fig. 5. The CAW results from the LGS simulated dataset are summarized in Table  2. LGS F md is less than NGS F md. However, if the NGS optical turbulence profile only considers layers from 0 to 16 km then NGS F md = 0. 03. As mentioned in Section 3. 3, the wind velocity across an altitude range will not necessarily be constant. We refer to this process as wind shear and it is frequently observed on-sky  (Osborn et al. 2017). It is therefore important to understand the detrimental effects of wind shear when using CAW to study the wind velocity profile. To do this we repeated the NGS and LGS simulations, but with a wind direction profile that had exaggerated wind shear.
Wind velocity profiling from on-sky AO telemetry In this section we demonstrate the operation of CAW for on-sky data. The analyzed datasets were recorded using the CANARY instrument that was being operated on the 4. 2 m WHT. The MOAO capabilities of CANARY have been previously demonstrated  (Gendron et al. 2014; Vidal et al. 2014; Martin et al. 2017). Each dataset was in a 4-NGS con-   The time interval between a measurement from Stereo-SCIDAR and CANARY centroid-data retrieval was limited to 20 minutes. It was assumed that both the optical turbulence and wind velocity profile would not drastically alter within this timescale. In total there were 27 useful datasets. These observations were made in July and October of 2014. Although all datasets could be used to compare optical turbulence profile measurements, Stereo-SCIDAR postprocessing had filtered out the wind velocity measurements from 4, i.
e. only 23 wind velocity datasets could be compared between CANARY and Stereo-SCIDAR. However, we were able to compare all 27 measurements to the ECMWF. CAW 1 was performed on the CANARY dataset, fitting 7 evenly-spaced layers between 0 and 24 km. No layer above h max was fitted. For each dataset the six 7 × 7 SHWFS com-binations were fitted to simultaneously. The Stereo-SCIDAR C 2 n (h) profiles were binned to the resolution of CAW 1 to give C 2 n (h) r (the same binning process as outlined in Section 4. 1). The noise-floor of C 2 n (h) r dh was 10 -16 m 1/3. To be included in F md analysis, each fitted layer in C 2 n (h) m was required to be below h max and within the maximum altitude bin of Stereo-SCIDAR. The C 2 n (h) log-log plot between CANARY and Stereo-SCIDAR is shown in Fig. 7. CAW 2 and CAW 3 were performed on the CANARY dataset.
CONCLUSIONS We have presented a novel technique for automated wind velocity profiling from AO telemetry. This enables AO system control techniques that significantly improve image resolution. The introduced wind velocity profiling technique is referred to as CAW. We tested CAW using simulated data from CANARY. The simulated optical turbulence and  wind speed profiles were parameterized with the 35-layer ESO profile. For the NGS simulation, the wind speed bias and root mean squared deviation were measured to be B = 0. 2±0. 2 m/s and A rms = 2. 2±0. 3 m/s, respectively. CAW was also shown to be equally applicable to LGS analysis. An additional set of simulations explored what happens when CAW fits to layers that are experiencing wind shear. We saw that wind shear can cause erroneous results.
ACKNOWLEDGEMENTS All of the plots were rendered in Matplotlib (Hunter 2007). The  William Herschel Telescope  and the Isaac Newton Telescope are operated on the island of  La Palma  by the  Isaac Newton Group in the Spanish Observatorio del Roque de los Muchachos  of the  Instituto de Astrofísica de Canarias. The work carried out at both of these telescopes was supported by the  Science and Technology Funding Council, UK  (grant CG  ST/P000541/1 ) and the  European Commission  ( FP7 Infrastructures OPTICON  grant  226604 ,  312430 , and  H2020  grant  730890 ). We thank the  UK Programme  for the  European Extremely Large Telescope  ( ST/N002660/1 ) for supporting this research. We would also like to thank the reviewer of this publication for their constructive comments.
Acknowledgments: I thank  Laboratoire LePrince-Ringuet ,  Ecole Polytechnique ,  Universite Paris-Saclay  and the  Ministerio de Ciencia e Innovacion of Spain  under projects  FPA2014-58293-C2-1-P  and  Unidad de Excelencia Maria de Maetzu   MDM-2016-0692  for financial support. I also thank the organisers for inviting me to give this talk.
The change in microwave power is measured and averaged over 1-10 min depending on the photon flux. The photon flux is reduced with a series of neutral density filters (increasing the averaging time with decreasing photon fluence) from 10 15 to 10 13 cm -2 to determine where higher order recombination processes no longer limit the signal. The mobility values reported here are given at a photon fluence where the mobility is independent of fluence (∼10 14 cm -2 ). Fractal Dimension Measurement. The two-dimensional fractal dimension was determined by using the box-counting method implemented as a Matlab script. Further details are available in the Supporting Information. Pair Potential Modeling. QD pair potentials were determined as the superposition of van der Waals attraction, critical Casimir attraction, and screened electrostatics.
In particular,  Lovisari et al. (2017)  characterised the global properties and the morphological state of the ESZ clusters covered by XMM-Newton observations. We use their results on the morphological properties of the 118 ESZ clusters at 0. 05 < z < 0. 5 for which R 500 is within the field of view, and the relative error on the morphological parameters is less than 50%. This is about 80% of the corresponding parent ESZ subsample defined with the same z and size criteria, so we do not expect any major bias due to the incomplete XMM-Newton coverage. These objects cover one decade in mass, M 500 ∼[3-20] × 10 14 M , and are shown with blue open squares in Fig. 1. Henceforth we refer to this sample as the 'Low-z' sample. The spatially resolved thermodynamic properties of an ESZ subsample were analysed by the Planck Collaboration, who combined X-ray and SZ data to calibrate the local scaling relations (Planck Collaboration XI 2011) and measure the pressure profiles (Planck Collaboration Int.
Data preparation The observations used in this work were taken using the European Photon Imaging Camera (EPIC;  Turner et al. 2001 and Strüder et al. 2001)  instrument on board the XMM-Newton satellite. This instrument is composed of three CCD arrays, namely MOS1, MOS2, and PN, which simultaneously observe the target. Datasets were reprocessed using the Science Analysis System 3 (SAS) pipeline version 15. 0 and calibration files as available in December 2016. Event files with this calibration applied were produced using the emchain and epchain tools. The reduced datasets were filtered in the standard fashion. Events for which the keyword PATTERN is <4 and <13 for MOS1, 2, and PN cameras, respectively, were filtered out from the analysis. Flares were removed by extracting a light curve, and removing from the analysis the time intervals where the count rate exceeded 3σ times the mean value.
We created the exposure map for each camera using the SAS tool eexpmap. We merged multiple observation datasets of the same object, if available. We report in Table  B. 1 the effective exposure times after all these procedures. We corrected vignetting following the weighting scheme detailed in  Arnaud et al. (2001). The weight 3 cosmos. esa. int/web/xmm-newton for each event was computed by running the SAS evigwieght tool on the filtered observation datasets. Point sources were identified using the Multi-resolution wavelet software  (Starck et al. 1998) on the exposure-corrected [0. 3-2]  keV and  [2] [3] [4] [5]  keV images. We inspected each resulting list by eye to check for false detections and missed sources. We defined a circular region around each detected point source and excised these from the subsequent analysis.
Acknowledgements. The authors would like to thank  Barbara Sartoris  for helpful comments and suggestions. The results reported in this article are based on data obtained with  XMM-Newton , an  ESA  science mission with instruments and contributions directly funded by  ESA Member States  and  NASA. This work was supported by  CNES. The research leading to these results has received funding from the  European Research Council  under the European Union's  Seventh Framework Programme  ( FP72007-2013 )  ERC  grant agreement no  340519. LL acknowledges support from  NASA  through contracts  80NSSCK0582  and  80NSSC19K0116.
This study analyzes the characteristics of GPS tropospheric estimates (zenith wet delays -ZWDs, gradients, and post-fit phase residuals) during the passage of mesoscale convective systems (MCSs) and evaluates their sensitivity to the research-level GPS data processing strategy implemented. Here, we focus on MCS events observed during the monsoon season of West Africa. This region is particularly well suited for the study of these events due to the high frequency of MCS occurrences in the contrasting climatic environments between the Guinean coast and the Sahel. This contrast is well sampled with data generated by six African Monsoon Multidisciplinary Analysis (AMMA) GPS stations. Tropospheric estimates for a 3-year period (2006)(2007)(2008), processed with both the GAMIT and GIPSY-OASIS software packages, were analyzed and intercompared.
First, the case of a MCS that passed over Niamey, Niger, on 11 August 2006 demonstrates a strong impact of the MCS on GPS estimates and post-fit residuals when the GPS signals propagate through the convective cells as detected on reflectivity maps from the MIT C-band Doppler radar. The estimates are also capable of detecting changes in the structure and dynamics of the MCS. However, the sensitivity is different depending on the tropospheric modeling approach adopted in the software. With GIPSY-OASIS, the high temporal sampling (5 min) of ZWDs and gradients is well suited for detecting the small-scale, short-lived, convective cells, while the post-fit residuals remain quite small. With GAMIT, the lower temporal sampling of the estimated parameters (hourly for ZWDs and daily for gradients) is not sufficient to capture the rapid delay variations associated with the passage of the MCS, but the post-fit phase residuals clearly reflect the presence of a strong refractivity anomaly.
In this paper we compare the GPS Analysis at Massachusetts Institute of Technology (GAMIT) software  (Herring et al. , 2015)  and the GNSS Inferred Positioning System and Orbit Analysis Simulation (GIPSY-OASIS) software from the NASA Jet Propulsion Laboratory (JPL)/Caltech (https://gipsy-oasis. jpl. nasa. gov/, last access: 25 July 2019). The first performs a least squares adjustment on a regional network with one zenith tropospheric delay (ZTD) estimated every hour, whereas the second uses a Kalman filter to process the data in PPP mode with one ZTD estimated every 5 min. Both software packages implement tropospheric delay models that also include gradient parameters. Gradient parameters allow for the representation of the effect of first-order azimuthal asymmetry in the atmospheric refractive index  (MacMillan, 1995; MacMillan and Ma, 1997; Chen and Herring, 1997).
GPS data analysis with GAMIT The RINEX data from the six AMMA GPS stations are processed from 1 January 2006 to 31 December 2008 within a regional network of around 25 IGS stations using the GAMIT scientific software  (Herring et al. , 2015)  release 10. 6. The settings of the GPS data processing are designed to meet the recommendations of the International Earth Rotation Service (IERS) conventions  (Petit and Luzum, 2010)  and correspond to an update of those used by  Bock et al. (2008). The IGS final orbits are held fixed. A priori station positions expressed in ITRF2014  (Altamimi et al. , 2016)  are constrained with 10 and 30 cm a priori standard deviations for the horizontal and vertical components, respectively. GPS phase observations are corrected by applying absolute antenna phase calibration models  (Schmid et al.
At the end of each session processing, the estimated tropospheric delays, tropospheric gradients, and the GPS one-way phase residuals  (Herring et al. , 2015)  are saved for each AMMA station (Table  1 ). The processing of GPS data with the GAMIT software has some limitations: the ZWD temporal sampling interval of 1 h and the gradient temporal sampling interval of 24 h are too coarse to document the rapid meteorological extreme events like the passage of MCSs. Indeed, in such situations, rapid variations in the ZWD and strong anisotropy in the refractivity field are observed  (Bock et al. , 2008). As a consequence of the severe mis-modeling of the propagation delays, the estimated ZWD and gradient parameters are likely biased. Inspection of the post-fit phase residuals will help identify the severity of the mis-modeling.
GPS data analysis with GIPSY-OASIS in PPP mode The GIPSY-OASIS II v 6. 3 software is based on a Kalmanfilter estimator  (Zumberge et al. , 1997). We used it in PPP mode to reprocess the AMMA GPS data with a parameterization that is as similar as possible to the settings used with the GAMIT software described in Sect. 2. 1. Table  1  summarizes the different strategies used to process the GPS data. Compared with GAMIT, a major difference with GIPSY-OASIS II is the sampling interval of the estimated parameters (ZWDs, tropospheric gradients, and receiver clocks) of 5 min, which is achievable thanks to the Kalman-filter technique in which the parameters are updated at each time step. A minor drawback is that the GPS phase observations are thus reduced to a 5 min interval, i. e. , this estimation procedure uses 10 times fewer observations than the GAMIT processing.