dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Reduction The NIRSpec data of XID 2028 were reduced following largely the same method used by  Vayner et al. (2023, submitted)  to reduce the NIRSpec data cube on J1652. We refer the interested reader to this paper for more details. Here, we describe the main steps with an emphasis on aspects that are specific to the NIRSpec data on XID 2028. Data reduction was done with the JWST Calibration pipeline version 1. 8. 4  (Bushouse et al. 2022) using CRDS version 11. 16. 16  and context file jwst 1019. pmap. The first stage of the pipeline, Detector1Pipeline, performs standard IR detector reduction steps, such as dark current subtraction, fitting ramps of non-destructive group readouts, combining groups and integrations, data quality flagging, cosmic ray removal, bias subtraction, linearity, and persistence correction.
Afterward, we ran Spec2Pipeline, which assigns a world coordinate system to each frame, applies flat field correction, flux calibration, and extracts the 2D spectra into a 3D data cube using the cube build routine. Here, we adopt the emsm weighting method instead of the standard drizzle method to build the 3D data cube from the 2D data. 24  The emsm weighting reduced the oscillating spectral pattern in the point-source spectrum compared to the drizzle method at the cost of minor degradation in the spatial and spectral resolution. Additional steps were taken to flag pixels affected by open MSA shutters. At this point, we skipped the imprint subtraction step due to increased spatial variation in the background across many spectral channels. Due to known issues with the outlier detection step in the Spec3Pipeline (JWST Help Desk, priv.
communication), we opted to use the reproject package 25  to combine the different dither positions into a single data cube using their drizzle algorithm. The dither positions were combined onto a common grid with a spatial pixel size of 0 05. The astrometry of the cube obtained by the JWST Calibration pipeline features a minor offset with respect to archival Hubble Space Telescope (HST) data. We use the position of the quasar in the HST WFC3 F814W image, previously discussed in  Brusa et al. (2010 Brusa et al. ( , 2015 Brusa et al. ( , 2018) )  and  Scholtz et al. (2020) , to align the astrometry of the JWST data with respect to the HST image. We find a small offset of ΔR. A. = -0 07 and Δdecl. = 0 03. The data cube produced by the pipeline was put on an absolute flux scale by using the data on flux standard star TYC 4433-1800-1 (PID 1128, o009) reduced in exactly the same way as the data cube of XID 2028.
Data Analysis We are interested in the faint extended emission outside of the quasar. Therefore, we need to carefully remove the bright quasar light from the NIRSpec data cube. For this delicate task, we use q3dfit 26  (D. S. N. Rupke et al. 2023, in preparation) , which is a dedicated software package for the removal of bright point-spread function (PSF) from JWST data cubes. q3dfit is a Python-based software adapted from IFSFIT (Rupke 2014;  Rupke et al. 2017) , which is an IDL-based software which has been used extensively on ground-based IFS data. The major strength of q3dfit is that it takes advantage of all of the available spectral information to reconstruct and subtract the PSF with very few priors. The only necessary condition is that the PSF must be sufficiently spectrally different from the extended emission.
It is precisely this difference that allows us to identify the PSF without prior knowledge of its spatial shape and spectral dependence. Our use of q3dfit on the NIRSpec data of XID 2028 follows that of the NIRSpec cube of J1652 by  Vayner et al. (2023, submitted). We refer the interested reader to this paper for more details. Here, we briefly describe the general philosophy of q3dfit and the key aspects relevant to the NIRSpec data on XID 2028. q3dfit extracts the quasar spectrum using the brightest spaxels. This quasar spectrum is used as the PSF spectral model. It is scaled across the data cube and then subtracted to reveal the faint extended emission. In this first iteration, the extended emission is fitted with a combination of emission lines, absorption lines, and simple featureless monotonic continuum models.
This first fit to the residuals is then used as an input for a second iteration of the quasar spectrum extraction. In this second iteration, the fit to the residuals may be improved by using more sophisticated continuum models, such as galaxy stellar population synthesis (SPS) models, if the data quality justifies it. Similarly, additional iterations on the PSF subtraction and fit of the residuals may be needed to come to stable converging results. While there have been a few other attempts to perform PSF subtraction in IFU data based on this principle (e. g. , Husemann +13), q3dfit is unique in its ability to conduct iterative fitting of quasar PSF, gas emission, and galaxy stellar population synthesis (SPS) models. In this first-look paper on XID 2028, we focus on the properties of the extended line emission.
5933, is derived from our kinematic analysis of the q3dfit-processed data cube, discussed in Section 4. 2). Figure  3  shows that nearly all the [O III] lineemitting gas outside of the unresolved quasar lies either in a bright western plume of gas with negative radial velocities down to -1000 km s -1 (90-percentile) or in fainter blueshifted gas clouds that surround the bright plume and trace a wide-angle cone with an opening angle f ∼90°. The line emission extends to the western edge of the field of view of NIRSpec, i. e. , at least 17 kpc from the quasar. As we will discuss later in Section 4. 2, the measured negative velocities of the gas are well in excess of those expected for rotational motion in the host galaxy. Therefore, the NIRSpec data nicely confirm the presence of a one-sided fast [O III] outflow in XID 2028, which was first reported by  Cresci et al.
(2015)  and  Perna et al. (2015) , and was more recently reported by  Scholtz et al. (2020) , based on independent analyses of ground-based Xshooter and SINFONI IFS data. The improved angular resolution, PSF characterization, and sensitivity of the JWST data reveal intricate bubblelike substructures in the bright plume that were not evident in the ground-based data and resolves outflowing gas clouds, which trace a cone with a wider opening angle than previously suspected. The absence of a redshifted counterpart to the outflow, now confirmed by the NIRSpec data down to a 3σ f λ limit of 1. 4 × 10 -20 erg s -1 cm -2 Å -1 arcsec -2 , combined with the small extent (R ≈ 0 5 = 4 kpc) of the near-systemic (±300 km s -1 ) line emission from the host ISM, puts strong constraints on the outflow geometry.
Acknowledgments S. V. , W. L. , A. V. , D. S. N. R. , and N. L. Z. were supported in part by  NASA  through  STScI  grant No. JWST-ERS-01335. N. L. Z further acknowledges support by the  Institute for Advanced Study  through J. Robert Oppenheimer Visiting Professorship  and the  Bershadsky Fund. D. W. and C. B. acknowledge support through an  Emmy Noether Grant  of the  German Research Foundation , a stipend by the  Daimler and Benz Foundation  and a Verbundforschung grant by the  German Space Agency. J. B. -B. acknowledges support from grant  IA-101522  ( DGAPA-PAPIIT, UNAM ) and funding from the  CONACYT  grant  CF19-39578. The data products are available from the corresponding author upon appropriate request. Facilities: JWST(NIRSpec), HST(WFC3). Software: astropy (Astropy Collaboration et al.
This implies that, after MJD 57,500, there were no transitions back to LHS. On the contrary, the spreading of the black points is due to several short transitions to the HSS before MJD 57,500. The Imaging X-ray Polarimetry Explorer (IXPE) observed the source on 2022 March 29-31. A coordinated observational campaign with NICER and INTEGRAL was performed simultaneously with IXPE. The dates and the duration of the observations are reported in Table  1. An X-ray burst was detected in the JEM-X data in a time period not overlapping with NICER and IXPE observations (MJD 59,667). The science window containing the X-ray burst (ID: 248500190010) was excluded from data analysis. The green points in Figure  1  represent the values of HID at the time of the IXPE, NICER, and INTEGRAL observations.
IXPE Data The IXPE  (Weisskopf et al. 2022 ) is a NASA/ASI mission launched on 2021 December 9. It is observing all major classes of galactic and extragalactic X-ray sources, providing space-, energy-, and time-resolved polarimetry  (Soffitta et al. 2021). With respect to the previous X-ray polarimetric mission, OSO-8, IXPE needs about 2 orders of magnitude less exposure time to reach the same sensitivity, and it provides imaging capability The red points correspond to the HSS after the major transition in MJD 57,500 (the black points represent the HID before MJD 57,500), while the green points correspond to the times of the NICER, IXPE, and INTEGRAL/JEM-X observations reported in this paper. Flux variations among the green points are mostly due to the MAXI spurious modulation  (Mihara et al.
2022). with 30″ angular resolution over >11′ field of view, together with 1-2 μs timing accuracy and a moderate spectral resolution typical for proportional counters. It consists of three X-ray telescopes with identical mirror modules and polarizationsensitive imaging detector units (DUs) at their focus. The IXPE observation took place on 2022 March 29-31 for a total net exposure time of 85 ks after taking into account Earth occultations. The IXPE data extraction was performed by means of the IXPE collaboration software tool IXPEOBSSIM  (Baldini et al. 2022)  version 26. 3. 2, with xppicorr to apply the energy calibration with in-flight calibration sources (as such, a correction was not yet implemented in the official pipeline at the time of the observation), xpselect to filter data, and xpbin to apply different binning algorithms for generating images and spectra.
Rebinning and spectropolarimetric analysis was performed with FTOOLS and XSPEC (HEASOFT version 6. 30. 1). We compared the results of the polarimetric analysis obtained with both XSPEC and IXPEOBSSIM tools (pcube). While XSPEC requires the definition of a spectropolarimetric model, IXPEOBSSIM allows a model-independent analysis that computes the polarization only on the basis of detected photons. The IXPEOBSSIM response matrices version v010 were employed, corresponding to the latest available version in the HEASARC database. Data analysis was performed following the unweighted method. 53 he statistical uncertainties of PD and PA when using IXPEOBSSIM are calculated with the assumption that the Stokes parameters are normally distributed and uncorrelated, and that PD and PA are considered independent, as described in  Kislat et al.
INTEGRAL Data INTEGRAL observed the source from 2022 March 28 17:25 to 2022 March 30 23:40 UT for a total of 186 ks. INTEGRAL data were reduced using the latest release of the standard Online Scientific Analysis (version 11. 2), distributed by the INTEGRAL Science Data Centre  (Courvoisier et al. 2003)  through the multimessenger online data analysis platform  (Neronov et al. 2021). These target-of-opportunity observations were performed using hexagonal dithering to maintain GS 1826-238 in the fully coded field of view of JEM-X, the INTEGRAL X-ray telescope  (Lund et al. 2003). The JEM-X spectra were extracted in the range 3-35 keV with a response matrix with 16 standard channels. A systematic error of 1. 5% was added in quadrature for the spectral analysis. Even if the INTEGRAL observation did not exactly overlap the IXPE and NICER observations, the JEM-X spectrum was in good agreement with the NICER one.
However, these two sources are classified as Z sources, while GS 1826-238 is the first atoll source observed by IXPE. A comparison between the two kinds of sources is not always possible. For example,  Long et al. (2022)  reported that in Sco X-1, the PD has a strong dependence on the luminosity and the spectral branch. Instead, the IXPE data of GS 1826-238 present a quite stable light curve and HR. Therefore, it is impossible to extract any information about the evolution of the PD as a function of luminosity and the spectral state, unlike the case of Sco X-1. As reported by  Lapidus & Sunyaev (1985)  and  Schnittman & Krolik (2009) , the reflection from the accretion disk of the radiation produced by the SL or self-illumination of the disk can produce substantial polarization. However, in GS 1826-238, we do not detect, at least with the spectral resolution of NICER, the iron line that is a typical signature of disk reflection in the HSS sources (e.
This work made use of the MAXI light curves, publicly available at http://maxi. riken. jp/top/slist. html. INTEGRAL is an  ESA  project with instruments and a science data center funded by  ESA  member states (especially the PI countries:  Denmark, France, Germany, Italy, Switzerland , and  Spain ) and with the participation of Russia and the  USA. We thank  Keith Gendreau ,  Craig Markwardt , and the  NICER SOC  for granting and performing the NICER observations of the source and helping with data reduction. J. P. and S. S. T. acknowledge support from  Russian Science Foundation  grant  20-12-00364  and  Academy of Finland  travel grants  349144 ,  349373 , and  349906. J. P. and J. J. E. K. were supported by  Academy of Finland  grant  333112. Facilities: IXPE, Swift (XRT and UVOT), INTEGRAL.
The increasing statistical power of cosmic microwave background (CMB) datasets requires a commensurate effort in understanding their noise properties. The noise in maps from ground-based instruments is dominated by large-scale correlations, which poses a modeling challenge. This paper develops novel models of the complex noise covariance structure in the Atacama Cosmology Telescope Data Release 6 (ACT DR6) maps. We first enumerate the noise properties that arise from the combination of the atmosphere and the ACT scan strategy. We then prescribe a class of Gaussian, map-based noise models, including a new wavelet-based approach that uses directional wavelet kernels for modeling correlated instrumental noise. The models are empirical, whose only inputs are a small number of independent realizations of the same region of sky.
We evaluate the performance of these models against the ACT DR6 data by drawing ensembles of noise realizations. Applying these simulations to the ACT DR6 power spectrum pipeline reveals a ∼ 20% excess in the covariance matrix diagonal when compared to an analytic expression that assumes noise properties are uniquely described by their power spectrum. Along with our public code, mnms, this work establishes a necessary element in the science pipelines of both ACT DR6 and future ground-based CMB experiments such as the Simons Observatory (SO).
Introduction Increasingly precise cosmic microwave background (CMB) instruments and data products have the potential to place state-of-the-art constraints on astrophysical, cosmological, and theoretical models. Currently, ground-based instruments collect data at both small angular scales, including the Atacama Cosmology Telescope (ACT)  [35, 43, 83]  and the South Pole Telescope (SPT)  [5, 11] ; and large angular scales, including POLARBEAR  [80] , CLASS  [24] , and BICEP/Keck  [49]. While these instruments observe the sky with O(10 3 -10 4 ) detectors, next-generation projects, such as the Simons Observatory (SO)  [37] , the BICEP Array  [76] , and AliCPT  [75]  will increase this count by an order of magnitude once fully operational. Thereafter, the CMB-S4 experiment  [1, 9]  plans to deploy yet another order of magnitude increase in detector count, to several hundred thousand.
A unique difficulty in the noise modeling of ground-based CMB instruments, however, is the dominance of complicated, spatially-red atmospheric noise from broad microwave water lines (e. g. [30, 61, 82] ) at large scales. Importantly, increasing detector counts do not circumvent this noise component. Therefore, our experience with current CMB experiments offers a realistic preview of the noise modeling challenge facing future surveys. Existing noise modeling frameworks either do not scale well to ground-based CMB data volumes, or simply ignore prominent features in the data. A reasonably successful, though resource-intensive, approach has been to model instrumental noise in the time-domain, and then propagate noise timestream realizations into map space (e. g. [3, 12, 20, 65] ) through a mapmaking pipeline.
In particular, both collaborations used a model that captures spatially-varying, uncorrelated noise variance, as well as a stationary, two-dimensional (2D) "stripy" component (defined in either Fourier space or spherical harmonic space) resulting from the scan strategy. 2  Nevertheless, by limiting the non-white noise to a stationary representation, these models do not generalize to surveys with wide sky coverage, such as the complete ACT survey, or the SO and CMB-S4 planned surveys. ACT's sixth data release (ACT DR6) will deliver microwave sky maps in three frequency bands (f090, f150, and f220), covering ∼40% of the sky to a cumulative depth of 15 µKarcmin ( 20 µK-arcmin) in temperature (polarization). In this paper, we develop novel noise models for ACT DR6, which aim to capture the observed, complicated map space noise structure inherent to ground-based CMB instruments.
In addition, our directional wavelets allow the lossless transform to and from wavelet space. To our knowledge, these models represent the first application of 2D wavelet analysis in the context of astrophysical instrument noise modeling. The models' primary user interface is the drawing of simulated noise maps, and we make our codemnms 3  -publicly available. These simulations are also consumed by downstream ACT DR6 analyses, to be described in upcoming publications (e. g. [23, 71] ). This paper is structured as follows. In section 2, we summarize the ACT instrument and DR6 data products that enter the noise modeling. In section 3, we define the map-based noise and establish its most salient properties in the ACT data. In section 4, we detail the construction of the three noise models, including their mathematical formalism.
In 2019, PA6 was exchanged with a new detector array, PA7, observing in the f030 and f040 frequency channels. These "low frequency" measurements are not discussed in this paper. For arrays PA4-6, we use the DR6 "night only" maps, which include only data taken between 23:00 and 11:00 UTC; however, the methods we develop are equally applicable to ACT daytime data and null test maps. 5  We make use of three ACT DR6 map-based products in this paper as follows. Sky maps. The mapmaking for DR6 will be described in a forthcoming paper; the following is a summary. The detector timestreams of each array are reduced into Stokes I, Q, and U sky maps by a maximum-likelihood mapmaking algorithm  [2, 30, 55, 62]. The ACT mapmaker assumes the following data model: d t = Pm + n t (2. 1) where d t are TODs, P is a projection matrix mapping the pixelized sky m from map space to TOD space (using telescope pointing), and n t is additive, Gaussian noise in each detector sample.
Each data vector in equation (2. 1) contains intensity and polarization components. The maximum-likelihood map under this model is: m = (P T N -1 t P) -1 P T N -1 t d t (2. 2) where N t is the covariance of n t. TODs are grouped from the same array and frequency band into the same vector d t , thus producing per-array, per-frequency, polarized (Stokes I, Q, U) maps. In ACT DR6, equation (2. 2) is solved in eight disjoint "splits" of the timestreams: oneday-long TOD blocks are distributed exclusively into eight sets, such that the noise in each split map is independent. As in ACT DR4  [2, 58] , the maps are produced in the Plate-Carrée cylindrical projection. Further properties of the DR6 maps are given in table  2   1. Properties of the Advanced ACT detector arrays used in DR6. We will refer to particular polarized maps by their array and band (e.
g. , PA4 f220) throughout the text. The frequency ranges contain the central 99% of the band power. Beam sizes refer to the average beam full-width at halfmaximum (FWHM). Dec. , R. A. Shape Pix. Size   2. Properties of the ACT DR6 maps for all arrays, bands, and Stokes polarization components. In addition to the data maps, these also apply to the inverse-variance and cross-linking maps. The max gives the Nyquist spherical-harmonic bandlimit of the pixelization. Inverse-variance maps. In addition to maps of the microwave sky signal, the mapmaking algorithm produces auxiliary products used later in this paper. Of particular importance are "inverse-variance" maps, which give the inverse of the white-noise covariance between Stokes components in each pixel. Following the ACT convention  [19, 57] , we label them h.
2 and to guide our modeling decisions in section 4. These are maps which contain information about the ACT receiver's local scanning direction (see e. g. ,  [2, 19, 58] , for a more complete description). In particular, the "polarization fraction", f p , of the cross-linking map encodes the degree to which a given pixel is observed by many different scanning directions. The cross-linking maps are produced in such a way that f p = 0 corresponds to maximal cross-linking (many crossing scans), and f p = 1 corresponds to no cross-linking (all scans in the same direction). Therefore, we use the quantity 1 -f p in later sections, such that high values of 1 -f p correspond to high cross-linking and vice versa. An large-scale transfer function and polarization noise levels. We expect to use a second version of the maps for further science analyses and for the DR6 public data release, and will update these noise simulations as those maps are produced and released.
JCAP11(2023)073 Figure  2. Ratios of BB pseudospectra between the corrected difference maps, ν i , and the individual split maps, m i , for each array and frequency channel in ACT DR6. All pseudospectra are measured within this paper's "pseudospectrum mask" described in section 4. 5. 3. The dashed black line is the nominal result (ratio of unity), the solid lines (shaded bands) are the means (1σ range) over the eight splits of each dataset. To mitigate any expected BB signal, in addition to masking the Galaxy, all maps are point-source subtracted and filtered using the ACT DR4 ground pickup filter  [19] , where we remove Fourier modes with |k x | ≤ 90 and |k y | ≤ 50. Spectra are smoothed with a tophat kernel of size ∆ = 250 to better visualize their overall trend. to the large-scale polarized noise.
Acknowledgments We thank  Reijo Keskitalo ,  Bruno Régaldo-Saint Blancard , and  Karim Benabed  for insightful conversations and comments. ZA and JD acknowledge support from  NSF  grant  AST-2108126. CS acknowledges support from the  Agencia Nacional de Investigación y Desarrollo (ANID)  through  FONDECYT  grant no. 11191125  and  BASAL  project  FB210003. EC acknowledges support from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (Grant agreement No. 849169 ). KM acknowledges support from the  National Research Foundation of South Africa. SKC acknowledges support from  NSF  award  AST-2001866. This work was supported by a grant from the  Simons Foundation  ( CCA 918271 , PBL). The  Flatiron Institute  is supported by the  Simons Foundation.
ACKNOWLEDGMENTS We would like to acknowledge discussions with  Pavel Kovtun ,  Ashish Shukla  and  Amos Yarom. A. and I. M. have been partially supported by the  "Curiosity Driven Grant   2020 " of the  University of Genoa  and the  INFN Scientific Initiative SFT: "Statistical Field Theory, Low-Dimensional Systems ,  Integrable Models  and  Applications". This  project has also received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie Grant  Agreement No. 101030915.