dataesr/scientific-paragraphs-categorization huggingface dataset:
3) The aerosol lidar simulator uses a more detailed vertical grid than the cloud simulator : eg. 320 vertical levels (typically 60m) instead of 40 (typically 480m), because the detailed vertical structure is important for aerosol emission height and transport especially in the atmospheric boundary layer. Note that, for clouds the vertical resolution used in CFMIP experiments (480m) results from a compromise between the wish to keep high horizontal resolution for sparse shallow clouds, the SNR of CALIPSO data in day time and the vertical resolution of CloudSat. The user can choose to run the new aerosol routine alone, or the standard cloud routines alone (default), or both. These new features are controlled by two new keys in the user's configuration file in COSPv2 code (cf Figure  1 ).
Observations Here we build an observational dataset consistent with the model+aerosol lidar simulator outputs SR_3 and ATB_3, that are the SR and ATB profiles observed above clouds by the CALIPSO lidar as described in the previous section. Note that contrary to SR_3 and ATB_3, the total extinction profile (EXT_3) cannot be observed directly by CALIPSO, it is an output from the simulator to support the interpretation of the difference between the observation and the model+simulator outputs. We use the CALIPSO L1. 5 orbit file (https://www-calipso. larc. nasa. gov/resources/calipso_user-s_guide/qs/cal_lid_l15_v1-00. php) dataset that contains cloud screened ATB profiles at 532nm with 60m vertical resolution and 20km long-track horizontal resolution, 90m cross-track. The CALIPSO L1. 5 data are built from the native L1 CALIPSO data (1/3km horizontal resolution long-track, 90m cross-track and 30m vertical) on which cloud screening is applied iteratively at different horizontal resolutions from 1/3km up to 80km, so that all the cloudy levels of each 1/3km profile are screened and all the cloud-free level of each 1/3km profile are retained below the altitude of 8 km.
Given the CALIPSO polar orbit, the number of profiles is larger at high latitudes than in the tropics where not all the grid boxes contain a satellite observation each day. Similarly, we build the SR gridded product from the orbit L1. 5 ATB dataset. To do so, we first compute the AMB profiles -the signal that would be measured by the lidar in a cloud-free and aerosol-free atmosphere -at 20km along track resolution, 60m vertical, from the GMAO pressure and temperature profiles included in L1. 5 data using the lidar equation. Then we compute the SR profiles by dividing the L1. 5 ATB with AMB. Finally we average all the 20km-SR profiles within the 1°x1° gidbox. Note that it is better to compare the observed and simulated SR profiles rather than the observed and simulated ATB profiles, because the model SR profile is normalized against the model pressure and temperature profiles and the observed SR profile is normalized against the pressure and temperature from GMAO re-analysis.
Hence, more work has to be done so that other GCMs also diagnose their aerosol 180° backscatter βa in a way that is consistent with their aerosol optics parameterization. For future comparisons between CALIOP data and other GCMs, or for model-to-model comparisons, one might find useful to use one single optics module, to eliminate aerosol optics as a potential source of discrepancy in the comparisons. This is beyond the scope of this study and requires future investigation. To evaluate the representation of aerosol sources in the model, the NASA product providing aerosol types from CALIPSO data is of particular interest. Indeed CALIOP level 2 data include seven aerosol classes: clean marine, dust, polluted continental, clean continental, polluted dust, smoke, other. This classification utilizes depolarization ratio, integrated attenuated backscatter coefficient, altitude, and land vs ocean  (Kim et al.
Although primarily dedicated to wind measurements, the HSRL capability in the UV allows the separation of the molecular and particulate contributions and enables the measurements of the particulate backscatter and extinction coefficients. These measurements provide new insight of very thin aerosol layers and can be very useful for the validation of models that directly compute these quantities. Later in 2023, the EarthCare mission will also provide data from the HSRL lidar ATLID at 355 nm. The COSPv2 simulator can be easily adapted to other wavelengths, which opens the way to the determination of new diagnostics for cloud susceptibility, aerosol typing and aerosol-cloud proximity metrics.
Acknowledgments: We thank  R. Guzman  for his work on the development of the aerosol lidar simulator,  Steve Klein  for his inputs on the improvements of the vertical re-gridding within  COSPv2  and  N. Schutgens  for his scientific advice. P. -L. Ma was supported by the " Enabling Aerosol-cloud interactions at GLobal convection-permitting scalES (EAGLES) "  project  (project no. 74358 ), funded by the  U. S. Department of Energy, Office of Science, Office of Biological and Environmental Research ,  Earth System Model Development (ESMD) program  area. The  Pacific Northwest National Laboratory  (PNNL) is operated for DOE by  Battelle Memorial Institute  under contract  DE-AC05-76RL01830. Financial support: This work was supported by  CNES / EECLAT  project, and by the  U. S. Department of Energy, Office of Science, Office of Biological and Environmental Research ,  Earth System Model Development (ESMD) program area  (project no.
Code availability: the aerosol lidar simulator presented in this paper has been released within the COSPv2 infrastructure. It is available on the GitHub : https://github. com/CFMIP/COSPv2. 0 Data availability: the CALIPSO observational dataset that was used in the comparison with the model+simulator outputs is available at : https://www-calipso. larc. nasa. gov/resources/calip-so_users_guide/qs/cal_lid_l15_v1-00. php. The ATB and SR CALIOP proOiles after data processing (application of the AMB/SR thresholds) are available at : https://www. researchgate. net/publication, doi : 10. 13140/ RG. 2. 2. 27891. 81442 ; 10. 13140/RG. 2. 2. 32924. 97929.
Overview of the Pipeline The core of the pipeline, coded in Python 3. 7 (Van Rossum & Drake 1995), can be divided into nine basic steps, which will be briefly introduced here and described in more detail later in the following sections. Due to the highly interactive nature of the analysis, the default version of the pipeline (called StraKLIP) organizes the different steps in a series of Jupyter notebooks 7  (hereafter simply notebooks). The main objective is to detect faint companions by performing PSF subtraction using the KLIP algorithm, Step 6. This requires two types of input: 1. a set of initial _flt HST images, as delivered by the standard HST pipelines 2. an initial catalog of primary targets to be searched for companions (note that the existence of an initial catalog is a soft requirement-see point 2 later in this section) During the following discussion, we will distinguish between the initial catalog of target sources, typically the result of previous data analysis aimed at deriving a photometric source catalog, and the derived input catalog in the format actually accepted by the pipeline.
Step 1: Loading Images The input images accepted by the pipeline are the flt HST images delivered by STScI MAST. While each FITS file is composed of five layers, the pipeline considers only three of them: 1. the science image, extension SCI, where the actual science data reside; 2. the data quality, extension DQ, containing coded information about bad pixels, saturated pixels, cosmic rays (CRs), etc. ; and 3. the error layer, extension ERR, providing the variance of the science image. The initial split_chip. py routine takes advantage of ACSmask/ WFC3mask from the DOLPHOT package  (Dolphin 2000)   12  to mask out all pixels flagged as bad in the data quality image, so that they will be ignored in future steps (sky determination, photometry, etc. ) and multiplies the initial image by the PAM.
While the WFC3-IR data are single-chip image data cubes, in the case of ACS-WFC and WFC3-UVIS, the routine distinguishes between the two CCDs of each camera splitting the initial images into two. chipN FITS data cubes, where N represents the chip number 1 or 2. Both data cubes contain their relative SCI, DQ, and ERR sublayers. Before running this step it is best practice to back up all the initial images because the ACSmask/WFC3mask from the DOLPHOT package will alter them.
The original subpixel position of the source is preserved during this process. Once the final positions are established, a data cube comprising the following three (four if the option to remove CRs has been enabled) different tiles is saved for each target: 1. the tile itself containing science data, labeled Data 2. the corresponding tile cut from the ERR layer of the input image, labeled eData Gray dots mark the position on the instrument of selected good candidates, while yellow dots mark PSF stars. Blue dots mark the positions of identified wide doubles. In this example we settle on 100 cells with an average number of isolated targets (good candidates) of ∼44 and an average number of PSF stars of ∼32. 3. a similar tile obtained from the DQ layer of the input image, labeled DQ 4. If the option to remove CRs has been enabled, a CR-free data tile is also recorded with label CR_Clean.
Steps 6 and 7: PSF Subtraction and Candidate Detection The pipeline performs PSF subtraction using a pipeline derived from pyKLIP 14   (Wang et al. 2015)  modified to perform reference differential imaging (RDI) of many sources at once. The pyKLIP module is a Python implementation of the KLIP algorithm, invented to achieve accurate PSF subtraction through an operation that can be represented as (for more details see  Soummer et al. 2012 ) ( ) ˆ( ) ( ) - = y T n I A n. 1 0  Here T(n), the target image, is the nth input tile (either Data or CR_Clean label); ˆy I 0 is the best representation of y I 0 , the model of an isolated source with no other astronomical signal in the search area ; and òA(n) is an extra astronomical source that may (ò = 1) or may not (ò = 0) be present in the input tile T(n).
Step 8: Photometry The pipeline offers three different photometry methods: aperture, matched filter (MF), and PSF photometry. While aperture photometry always provides an accurate value if the stars are well isolated and there are no companions, the two other methods can provide a more accurate estimate of the flux of a star if the PSF is well sampled and modeled. This is typically the case for ACS and the visible channel of WFC3, but not for WFC3-IR due to the coarser pixel scale. We therefore illustrate each method with an application to ACS simulations, underlining the strengths and flaws of each strategy in dealing with the data produced by the pipeline.
Aperture Photometry To perform aperture photometry the pipeline uses the photometry AP routine, which integrates some of the photutils 15  features to perform aperture photometry. The two  frames at the top of Figure  6  show the two tiles required by this routine. The routine uses the CircularAnnulus module of photutils to evaluate the median sky background (C sk ). Specifically, it takes a 3σ cut median of the sky in an annulus between two radii (r a and r b ) (Sky tile in Figure  6 ). This sky estimate is then subtracted from the data tile creating a skysubtracted tile. Then, using the CircularAperture module of photutils, the routine sets to zero all pixels outside the circular area of radius r i creating a final sky-subtracted aperture tile (or simply Aperture tile in Figure  6 ), whose total counts ( ¢ C ap ) provide the source counts inside r i.
MF Photometry For well-sampled data, the pipeline offers a second method to extract photometry, MF photometry  (Turin 1960). This approach solves the problem of detecting with the highest S/N a signal of known shape in noisy data. The solution is given by the cross-correlation between a known signal template (the reference PSF in this case) and an unknown noisy signal (the target). The pipeline implements this method through the photometry AP routine, which takes advantage of the MF routines present in the pyKLIP package  (Ruffio et al. 2017 ). These perform convolution of the template with an image using the scipy signal processing library of fast Fourier transformations. Figure  7  shows the different tiles used by the photometry AP routine. First, the sky is evaluated using the same approach adopted by the photometry AP routine, adopting an annulus between r a and r b (Sky annulus tile in Figure  7 ).
Also in this case, the sky estimate can be further refined using the growth curves as explained in Section 7. 1. The sky is then subtracted from the Data tile to produce a sky-subtracted target tile (or just Target in Figure  7 ). This tile is convolved with the reference tile, i. e. , the PSF tile obtained for example from Tiny Tim,  16  and the output of this operation is stored in the MF output tile. The counts of the star can then be recovered by dividing the brightest pixel in the MF output by the throughput (i. e. , the normalization factor that will rescale the count MF output to the real flux of the star): ( ) ( ) = C max MF throughput 9 MF output | ( )| ( ) = S throughput abs PSF 10 i j i j , , 2 where PSF represents the input template tile. To estimate the uncertainties on C MF , the routine follows the same Equations (3) to (5) used for photometry AP with the difference that since no aperture is used in this photometry, a "noise-equivalent area" needs to be defined to derive the number of pixels (N ap ) contributing to the uncertainty estimate.
PSF Photometry The third option to perform photometry of well-sampled data is PSF photometry. The photometry PSF routine performs PSF fitting making extensive use of the BasicPSFPhotometry package from photutils. 17 As the initial guess for the coordinates and flux, photometry PSF evaluates a centroid on the tile and uses the flux estimated by photometry AP. Once the flux of the star has been evaluated through PSF fitting, photometry PSF takes advantage of equations similar to Equations (  11 ) and (  12 ) to estimate the uncertainties on the counts. Then it converts counts and errors to magnitude units using Equations (  6 ) and (7). Moreover, the reduced χ 2 is evaluated for each performed photometry as ( ) c m s = - å - = = - å m n x m n 1 1 Residual eData 13 i j i j i j i j i j i j i j 2 , , , , 2 , , , 2 ⎜ ⎟ ⎜ ⎟ ⎛ ⎝ ⎞ ⎠ ⎛ ⎝ ⎞ ⎠ where m is the number of pixels in the tile; n is the number of free parameters for the fit, typically n = 3 fitting both the positions i,j and the flux; and x i,j -μ i,j is the difference between the observable (x i,j ) and the model (μ i,j ).
Performance on Isolated Stars To test the performance of the photometry AP and photometry PSF routines, thousands of isolated stars have been generated to closely resemble the stars present in the ONC both in flux and in position on the detector. Using Tiny Tim, we create 50 PSF data cubes in five different target filters-F435W, F555W, F658N, F775W, and F850LPwith the sources equally spaced on both chips of the ACS/WFC focal plane. Each PSF data cube is created assuming effective temperatures between 2000 and 3000 K in steps of 250 K, appropriate to sample the lower end of the temperature range of young low-mass companions and M-dwarfs. Each PSF data cube comprises 25 different subpixel-shifted PSFs created with the standard Tiny Tim SUB = 5 option, which splits each native pixel into 25 subpixels.
The CCD and the coordinates of the catalog source, as well as the random pixel phase and the magnitude-dependent temperature, are then used to select the closest PSF in the PSF data cubes previously generated. This PSF is rescaled to the selected flux using the standard HST zero-points and the background is added to each pixel. This last step is performed using make_noise_image 18  from photutils, which extracts each background pixel value from a Gaussian distribution with a mean given by the 3σ cut median of the sky evaluated at the source's real on-sky coordinates and a standard deviation given by the corresponding scatter. To conclude this process, Poisson noise is added to the final tile using apply_poisson_noise 19  from photutils as well. The simulated star is then passed to the photometry AP and photometry PSF routines to extract photometry.
No system is perfect though, so even if the routines try to account for missing flux due to their use of a finite aperture (aperture AP ) the limited extent of the PSF used for the fit (aperture PSF ) and the imperfect estimate of the background (despite having applied for all the three systems the growth curve correction) cause an offset between the input flux and the measured value. Evaluating these deltas as we have done in our specific test can help mitigate this problem (see Table  2 ), providing an insight about when to correct real data or not, in cases where the input magnitudes of the sources are unknown. Comparing the results in Table  2 , in our simulation we observe that photometry AP and photometry PSF yield systematic errors that overall are smaller than the uncertainties on the deltas.
Context. AU Mic is a young and nearby M-dwarf star harbouring a circumstellar debris disc and one recently discovered planet on an eight-day orbit. Large-scale structures within the disc were also discovered and are moving outwards at high velocity. Aims. We aim to study this system with the highest spatial resolution in order to probe the innermost regions and to search for additional low-mass companions or set detection limits. Methods. The star was observed with two different high-angular resolution techniques probing complementary spatial scales. We obtained new K s -band sparse aperture masking observations with VLT/SPHERE, which we combined with data from VLT/NACO, VLTI/PIONIER and VLTI/GRAVITY. Results. We did not detect additional close companions within the separation range 0. 02-7 au from the parent star.
9σ and that with SPHERE being 1. 6σ. We applied the pyMESS2 code to estimate the detection probability of companions by combining radial velocities, multi-band SPHERE imaging, and our interferometric detection maps. We show that 99% of the companions down to ∼0. 5 M jup can be detected within 0. 02 au or 1 M jup down to 0. 2 au. The low-mass planets orbiting at 0. 11 au ( 11 mas) from the star will not be directly detectable with the current adaptive optics (AO) and interferometric instruments because of its close orbit and very high contrast (∼10 -10 K). It will also be below the angular resolution and contrast limit of the next Extremely Large Telescope Infrared (ELT IR) imaging instruments.
08 M jup orbits its star with a period of 18. 9 d and a semi-major axis of 0. 11 au (∼11. 3 mas). In this paper, we aim at setting detection limits for additional orbiting companions within the range 0. 04-7 au (4-730 mas) using high-angular-resolution observations, including infrared (IR) long-baseline interferometry and SAM. Section 2 presents new SAM observations acquired in 2018 with the SPHERE/IRDIS instrument mounted at the Very Large Telescope (VLT), together with literature data from the VLTI/GRAVITY and VLT/NACO instruments. We present the details of our search for a companion and estimate our detection limits in Sect. 3 using the CANDID algorithm. We then discuss our results and provide conclusions in Sect. 4.
10 µm, ∆λ = 0. 30 µm) and a seven-hole aperture mask. This provides 21 baselines, that is 21 fringes with a unique spatial frequency and direction. Data were acquired in a data cube mode with tens of frames of 1 s exposure time. Similar sequences are executed on an empty sky region for background subtraction and on a calibrator star. The full log is summarised in Table  1. AU Mic observations were interleaved with the calibrator star HD 196919 (K4III, R = 6. 94 mag, K = 4. 31 mag, θ LD = 0. 717 ± 0. 023 mas) in order to monitor the interferometric transfer function and calibrate the AU Mic visibilities. HD 196919 was chosen with the SearchCal software 1 to be as close as possible to AU Mic in the sky and with a similar brightness. Our data reduction steps were as follows. First, each image was corrected for detector cosmetics using a standard procedure, including sky subtraction, flat-fielding, and bad-pixel correction.
Calibrated V 2 were obtained by dividing the AU Mic visibilities by those of the calibrator, while calibrated CPs were derived by subtracting those of the calibrator. The transfer functions are displayed in Figs. A. 1 and A. 2. We have a total of 294 V 2 and 490 CPs calibrated measurements, which are displayed in Fig. B. 1. Our SAM observations with SPHERE allow us to probe the spatial scale ∼0. 6-5. 3 au (60-550 mas) from the star, with the lower limit set by the longest baselines and the upper    HD 197540, HD 194432 and HD 200750 for #4, and HD 196387 for #5. limit set by the bandwidth smearing. To improve the detection limits on a wider angular scale, we collected additional high-angular-resolution data from the VLTI/GRAVITY  (Eisenhauer et al. 2011) , VLTI/PIONIER  (Le Bouquin et al. 2011)  and VLT / NACO  (Rousset et al.
NACO/SAM observations We retrieved the reduced data published by  Gauchet et al. (2016). 2016 ) is reported in Table  1. Data reduction (flat-field, bad-pixels, and sky subtraction) were performed in the same way as for the SPHERE/SAM observations (see more details in  Gauchet et al. 2016 ). The direct fringe-fitting method was applied instead of Fourier transform as we did previously ; however both methods provide similar results. To calibrate the data,  Gauchet et al. (2016)  used HD 197339 (K2III, R = 6. 47 mag, K = 4. 47 mag, θ LD = 0. 646 ± 0. 021 mas). These data allow to probe the spatial scale ∼0. 7-7 au (70-730 mas). Although these authors already published detection limits with this data set, we included it so that we can re-analyse the data with our CANDID tool, and perform a uniform analysis with the other datasets.
VLTI/PIONIER observations PIONIER calibrated data were retrieved from the Optical interferometry Database (OiDB 2 ) developed by the Jean-Marie Mariotti Center (JMMC 3  ). PIONIER  (Le Bouquin et al. 2011 ) is an interferometric instrument at the VLTI, combining the light coming from the four auxiliary telescopes (AT) in the H band, either in a broad band mode or with a low spectral resolution, where the light is dispersed across a few spectral channels. The observations were performed on several dates from 2011 to 2014 for different observing programs. Data before 2014 were collected when PIONIER was a visitor instrument at the VLTI, and therefore the calibrated data are only available in the OiDB (not in the ESO archive). We listed these observations in Table  1. To monitor the instrumental and atmospheric contributions, the standard observational procedure was used which consists of interleaving the science target with reference stars.
These stars are listed in Table  1  and taken from SearchCal. The data were reduced with the pndrs package described in Le  Bouquin et al. (2011 Bouquin et al. ( , version v2. 3 for 2011 data, v2 data, v2. 591 for 2013 and v2. 71 for 2014). The main procedure is to compute squared visibilities and triple products for each baseline and spectral channel, and to correct for photon and readout noises. The combination of four telescopes provides six visibility and four closure phase measurements per spectral channel. Although the PIONIER observations were performed mostly with small telescope configurations, the baselines range from 10 m to 128 m, allowing us to probe the spatial scale 0. 02-2 au (2-200 mas; where the interferometric FoV for our data set defined from the optical path difference separation of the fringe packet of two components).