SAUNAS. I. Searching for Low Surface Brightness X-Ray Emission with Chandra/ACIS

We present Selective Amplification of Ultra Noisy Astronomical Signal (SAUNAS), a pipeline designed for detecting diffuse X-ray emission in the data obtained with the Advanced CCD Imaging Spectrometer (ACIS) of the Chandra X-ray Observatory. SAUNAS queries the available observations in the Chandra archive and performs photometric calibration, point-spread function modeling and deconvolution, point-source removal, adaptive smoothing, and background correction. This pipeline builds on existing and well-tested software including CIAO, VorBin, and LIRA. We characterize the performance of SAUNAS through several quality performance tests and demonstrate the broad applications and capabilities of SAUNAS using two galaxies already known to show X-ray-emitting structures. SAUNAS successfully detects the 30 kpc X-ray superwind of NGC 3079 using Chandra/ACIS data sets, matching the spatial distribution detected with more sensitive XMM-Newton observations. The analysis performed by SAUNAS reveals an extended low surface brightness source in the field of UGC 5101 in the 0.3–1.0 keV and 1.0–2.0 keV bands. This source is potentially a background galaxy cluster or a hot gas plume associated with UGC 5101. SAUNAS demonstrates its ability to recover previously undetected structures in archival data, expanding exploration into the low surface brightness X-ray Universe with Chandra/ACIS.


Introduction
The Advanced CCD Imaging Spectrometer on the Chandra X-ray Observatory (Chandra/ACIS; Weisskopf et al. 2000) provides an effective balance between angular resolution and sensitivity for the study of diffuse galactic hot gas emission, with its field of view (FOV) of up to 16.9 × 16.9 arcmin 2 and 0 492 of spatial resolution.Stacking multiple observations made over Chandra's 25+ yr mission is one of the keys to obtaining the deepest observations of the Universe in X-rays.However, in most cases, the position of the target on the detector changes within the observations, introducing serious challenges to acquiring a meaningful combined image.The point-spread function (PSF) broadens and becomes more ellipse-shaped with increasing off-axis angle,7,8 necessitating an elaborate deconvolution scheme and hampering the ability to exploit the full capabilities of the archive.Consequently, Chandra observations are underexplored to date in studies advancing the low X-ray surface brightness domain.
Future studies of low X-ray surface brightness emission (∼10 −8 -10 −11 s −1 cm −2 arcsec −2 and beyond) enabled by data processed to enhance detection of low-count regions could advance progress in several currently open questions relevant to galaxy evolution, including the origins of diffuse soft X-ray emission in galaxies and feedback involvement (Henley et al. 2010;Kelly et al. 2021).
Lambda cold dark matter cosmology predicts that filaments of diffuse gas from the cosmic web will accrete during their infall onto protogalactic dark matter halos (White & Rees 1978;White & Frenk 1991;Benson & Devereux 2010), where gas is heated to approximately the halo virial temperature (T > 10 6 K).This plasma, further shaped by energy injection from active galactic nuclei (AGNs; Diehl & Statler 2008), supernovae (SNe), and stellar winds (Hopkins et al. 2012), is detected as diffuse soft X-ray band emission around galaxies (Mulchaey 2000;Sato et al. 2000;O'Sullivan et al. 2001;Aguerri et al. 2017).The origins and evolution of hot gas halos are important open questions in astrophysics, as halos are both the aftermath and active players of gas feedback processes, which modulate the star formation efficiency in galaxies (Binney 1977;Rees & Ostriker 1977;Silk 1977;White & Rees 1978;White & Frenk 1991).The largely unexplored realm of extreme diffuse gas emission, likely associated with large departures from equilibrium (Strickland et al. 2004), is likely to preserve a unique historical record of these events.Such emission is also likely to be disregarded in studies using standard pipelines that are not optimized for preservation of statistically significant but low surface brightness detections.
This project is the first in a series that will study the hot gas halos around galaxies using X-ray observations from Chandra.
Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence.Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
The first step is to test the pipeline to reduce the Chandra/ACIS data products, named Selective Amplification of Ultra Noisy Astronomical Signal (SAUNAS).This paper describes the SAUNAS pipeline processing of data from the Chandra Data Archive9 and benchmarks it to previous works.In particular, we focus on the comparison of results between our analyses and those from other investigations for two well-detected X-ray sources characterized in the literature: NGC 3079 and UGC 5101.The latter has complex and extended X-ray emission, previously unexplored and only revealed by the current work.
This paper is organized as follows.The SAUNAS pipeline is described in Section 2. The selection of published results for the SAUNAS performance comparison is discussed in Section 3.1.The benchmark analysis is presented in Sections 3.2 and 3.3.The discussion and conclusions are presented in Sections 4 and 5, respectively.We assume a concordance cosmology (Ω M = 0.3, Ω Λ = 0.7, H 0 = 70 km s −1 Mpc −1 ; see Spergel et al. 2007).All magnitudes are in the AB system (Oke 1971) unless otherwise noted.

Observational Challenges
From an observational perspective, measuring diffuse X-ray halo properties in galaxies involves at least four technical challenges.
1. Detection.The outskirts of X-ray halos are extremely faint (10 −8 -10 −11 s −1 cm −2 arcsec −2 ).Separating the faint emission associated with sources from that of the X-ray background (Anderson & Bregman 2011) within such low-count regimes is an extraordinarily challenging task.Statistical methods that assume a normal (Gaussian) distribution may not produce accurate results.2. Deblending.AGNs and X-ray binary stars (XRBs) are typically unresolved point sources that may contribute to the same X-ray bands where the hot gas halos are expected to emit (from ∼0.3-0.5 to 1.2-2 keV).While in principle, the detection of hot gas halos in nearby galaxies may not require very high spatial resolution observations or spectral capabilities, the separation of such emission from that of point sources does require them.High spatial resolution observations reduce systematic contamination in low surface brightness regimes.
3. PSF contamination.The distribution of diffuse emission is easily confused with the scattered, extended emission of the unresolved bright cores that contaminate the outskirts of the target through the extended wings of the PSF of the detector (Sandin 2014(Sandin , 2015)).Most studies do not correct for this type of scattering effect, although a few works, such as Anderson et al. (2013), have explored the combined stacked hot gas halo emission of 2165 galaxies observed with ROSAT (0.5-2.0 keV), convolving the combined surface brightness profiles by the PSF model to take into account the dispersion of light.4. Reproducibility and accessibility.The methodologies for calibration, detection, and characterization of X-ray emission have substantial differences between studies.
Due to the Poissonian nature of the X-ray emission, most studies employ different types of adaptive smoothing in their analysis.These software methods tend to be custommade and infrequently made publicly available.Likewise, the final data products (final science frames) are seldom offered to the community.
The SAUNAS methodology presented in the current paper attempts to address most of these points by (1) correcting the PSF in the images, (2) separating the emission of point sources from that of diffuse extended ones, and (3) providing a quantitative metric to determine if a detection is real or not.These two points implemented in SAUNAS are the major difference from other existing codes for detection of extended X-ray emission, such as vtpdetect (Ebeling & Wiedenmann 1993) or EXSdetect (Liu et al. 2013), as they do not attempt to deconvolve the observations using dedicated PSF models or to separate diffuse emission from point sources.

SAUNAS Pipeline
SAUNAS generates two main products: (a) PSF-deconvolved X-ray adaptively smoothed surface brightness maps and (b) signal-to-noise ratio (S/N) detection maps.The X-ray adaptively smoothed surface brightness maps provide the flux and luminosity of the hot gas X-ray halos, while the S/N detection maps provide the probability that the flux associated with each region on those maps is statistically higher than the local X-ray background noise.
SAUNAS creates these products in four major steps (see Figure 1): (1) preprocessing of the archival Chandra X-ray observations using the Chandra Interactive Analysis of Observations10 (CIAO) software (Fruscione et al. 2006; see Section 2.2.1), (2) statistical resampling of the X-ray detection events by bootstrapping, 11 (3) PSF deconvolution of the event maps using the Bayesian Markov Chain Monte Carlo Lowcounts Image Reconstruction and Analysis (LIRA; Donath et al. 2022b; see Section 2.2.3),12 and (4) adaptive smoothing using VorBin (see Section 2.2.4). 13SAUNAS requires a few user-input parameters, including the location of the target (α, δ), FOV, and energy band.The main steps of the pipeline are described in the following subsections.

CIAO Preprocessing
First, the data are preprocessed using CIAO in the following way.
1.All available Chandra/ACIS observations containing the user-supplied sky coordinates are identified using find_chandra_obsid.The data sets and their best available calibration files are automatically downloaded using download_chandra_obsid and download_ obsid_caldb.2. The raw observations are reprocessed using chan-dra_repro (v4.16).To avoid oversubtraction of both the source and background counts necessary for the statistical analysis, the particle background cleaning subprocess is set (check_vf_pha) to "no." See the main CIAO manual14 for more information on this step.3.All the available ACIS data sets are merged into a single events file (merge_obs).This product serves as the phase 1 (first pass) observation file and is used to identify emission regions and to determine the source spectra needed for PSF construction.4. The phase 1 merged observation file is used to define the angular extent of detected emission sufficient for basic spectral characterization.The spectral information is used in the step following this one.The VorBin (Cappellari & Copin 2003) library generates a map of Voronoi bins from which a surface brightness profile is constructed.
The preliminary detection radius (R lim,0 ), defined as the radial limit having a surface brightness equal to 10% of the surface brightness at the central coordinates, is computed.If R lim,0 is undefined due to a low central surface brightness, the presence of detectable emission is unlikely.For such cases, R lim,0 is arbitrarily set to onefourth of the FOV defined by the user.The events inside this detection radius are used to construct a spectrum employed in the next step to define the deconvolution kernel (e.g., PSF) appropriate for this target.The choice of a 10% limit is an optimal compromise based on the analysis of Chandra/ACIS observations: including as much emission as possible from the source enhances the spectra used to generate the PSF.However, including a region that is too large reduces computational efficiency.Note that the spectrum derived in this step serves the sole purpose of informing PSF construction and is not intended for physical characterization of the gas.
5. CIAOʼs task simulate_psf, in combination with the spectral information provided by the previous step, is used to generate a PSF representative of each observing visit to the target.The PSF modeling is dependent on the spectra of both the source and the background region, as well as the target position within the detector (off-axis angle).The latter is unique to each visit.The preliminary detection radius defines both the circular ( < R R lim,0 ) and annular ( < < R R R 2 lim,0 lim,0 ) apertures used to measure the source and background spectra, respectively (specextract).The aspectblur15 is set to 0.25 and the number of iterations to 1000 per data set.6.Finally, the individual event files and PSFs corresponding to each visit are cropped to a cutout, with the preferred energy range selected.
The outputs from the preprocessing procedure with CIAO described above are (1) the detected event maps (named obsid_Elow-upper_flt_evt.fits, where low and upper refer to the energy range limits and obsid is the observation ID identification in the Chandra archive), (2) the exposure time maps (obsid_Elow-upper_flt_expmap.fits), (3) the flux maps (obsid_Elow-upper_flt_flux.fits), and (4) the PSF (obsid_Elow-upper_flt_psf. fits).This set of intermediate files is used in the remaining steps of the SAUNAS pipeline to generate the final maps.
To enhance the robustness of the adaptively smoothed mosaics and to reduce contamination from nonsignificant signal in the background, the X-ray events are resampled via replacement (bootstrapping) as an additional (and useroptional) step before deconvolution.Bootstrapping is especially well suited for inferring the uncertainties associated with an estimand-such as the median surface brightness in cases for which the Gaussian standard deviation regime does not apply or parametric solutions are too complicated or otherwise unknown.Bootstrapping effectively reduces the leverage that single events or very low-count sources may have in the background of the final mosaics by accounting for the photonnoise uncertainties in the PSF deconvolution and Voronoi binning steps through a nonparametric approach, allowing for a better assessment of the uncertainties in the final simulations.
In our application, bootstrapping generates N ∼ 100 (hereafter N boot ) new X-ray event samples from the observed sample, preserving size (flux) and permitting events to be repeated.While the number of bootstrapping simulations is set to 100 by default as a compromise between precision and computational resources, N boot can be defined by the user in SAUNAS.Each resampled list of events is translated into an image, which is fed into the next step, PSF deconvolution (Section 2.2.3).

LIRA PSF Deconvolution
The LIRA (Connors et al. 2011;Donath et al. 2022b) package deconvolves the emission from sources in X-ray data.Through the use of LIRA, SAUNAS removes the contamination from AGNs and XRBs, which can be significantly extended and easily confused with a diffuse halo if the PSF is not accurately corrected.LIRA uses a Bayesian framework to obtain the best-fit PSF-convolved model to the observations, allowing the user to evaluate the probability that a detection is statistically significant.LIRA was designed to provide robust statistics in the low-count Poissonian regimes representative of faint extended halos, the primary science focus of our project.
As detailed in Section 2.2.1, the PSF models are generated specifically for each target, taking into account their location in the detector and their spectral energy distributions, on a pervisit basis.SAUNAS deconvolves data from individual visits, using these PSF models as input into LIRA.Discrete hard-band emission is produced primarily by point sources, including AGNs (Fabbiano et al. 1989;Fabbiano 2019), young stellar objects, and mass transfer onto the compact stellar object within XRB pairs (Wang 2012).Because these point sources contaminate the soft-band emission, they are excised from the data.They are identified using the Chandra Source Catalog (Evans et al. 2010) and then removed from the event file by deleting events that lay within the cataloged positional uncertainty ellipse of the source.
The Python implementation of LIRA is used to deconvolve the X-ray event files, thus minimizing the effects of the off-axis dependency associated with Chandra's PSF, such that data from different visits can be combined in a later stage.LIRA accepts five input arrays: (a) counts (number of events), (b) flux (in s −1 cm −2 pixel −1 ), (c) exposure (s cm 2 ), (d) PSF, and (e) a first approximation to the background (counts).The first four inputs are generated by the CIAO pipeline (Section 2.2.1), while the initial baseline background is set to 1.The number of LIRA simulations is set to 1000 (n_iter_max), in addition to 100 initial burn-in simulations (num_burn_in).To speed up the process,16 SAUNAS splits the LIRA simulations into parallel processing blocks (defined by the number of bootstrapping simulations), to be combined after the deconvolution process has finished.While 1000 LIRA simulations are run on each of the N ∼ 100 bootstrappingresampled images described in Section 2.2.2,only the last LIRA realizations (those produced after the deconvolution process has stabilized) for each resampled image are used (hereafter N stable ), which typically is equal to ∼100.To save computational resources, N stable is adapted based on the number of bootstrapping simulations so that the deconvolved data set consists of a maximum of N = N boot × N stable = 1000 deconvolved images (posterior samples).

Adaptive Voronoi Smoothing
The deconvolved data cubes, hereafter referred to as "Bootstrapping-LIRA" realizations, serve as a proxy of the probability density distribution of the true X-ray emission on a pixel-per-pixel basis at the Chandra/ACIS spatial resolution (a minimum of 0 492 pixel −1 , depending on the binning set by the user).To facilitate the detection of extended, low surface brightness structures such as hot gas halos-with apparent sizes substantially larger than the spatial resolution limit for the galaxies-the use of spatial binning enhances the detection of regions with very low S/N.
Voronoi binning (VorBin; Cappellari & Copin 2003) is applied to each of the N posterior samples in the deconvolved data cube.This process generates N Voronoi tessellation maps, each one differing from the other because they were calculated from the Bootstrapping-LIRA realizations.This data set is a Voronoi map data cube representing the probability density distribution of the surface brightness of the target.
A consequence of this binning approach is the loss of spatial resolution in the faintest regions of the image (halos, background) compared to the brightest regions (i.e., the galactic cores).This loss is caused by the fact that the Voronoi technique varies the bin size in order to achieve a fixed S/N in the resulting map.As we are primarily interested in mapping the large-scale halo structures, this loss in spatial resolution does not significantly impact our science goals.
A surface brightness map is created by calculating the median across one of the axes of the Voronoi data cube.To prevent background emission from contaminating the final image, the scalar background level is determined individually for each realization of the Bootstrapping-LIRA data cube.All sources, both resolved and unresolved, must be meticulously masked prior to measuring the background level to prevent systematically oversubtracting the background in the final mosaics.The source masking and background correction process are conducted iteratively.
1.After the LIRA deconvolution process and before the Voronoi binning is performed, point sources from the Chandra Source Catalog (CSC 2.0;17 likely XRBs, SNe, AGNs) that lay in the image footprint are removed from the associated event file.Point-source removal prevents the associated emission from impacting the adaptive Voronoi maps and resulting in diffuse contamination that could be confused with a gas halo component.2. A secondary mask is generated using CIAO's routine vtpdetect. 18This mask identifies the regions with detectable extended X-ray emission that are removed from the maps before measuring the background level.A mask is generated for each CCD of each visit through independent analysis.The masks are then combined into a single master extended source mask.3.If a source was detected in the preliminary surface brightness profile generated as a part of the CIAO preprocessing step (see Section 2.2.1, step 4), then those pixels with < R R lim, 0 are also masked before the background assessment.4.After removing all the masked pixels using the masks from the three previous steps, the first approximation of the background level (B 0 ) is made by measuring the median value of the unmasked sigma-clipped (σ = 3) pixels.The background value is then subtracted from the Voronoi-binned maps.
Once the individual observations have been backgroundcorrected, all the flux maps are combined using mean weighting by the respective exposure times.Finally, a refined background value (B 1 ) is calculated using the combined observations by repeating the process described above.The noise level is then estimated from the background distribution as the ratio between the median background level and the lower limit of the 1σ error bar (equivalent to the 15.8th percentile).The final background-subtracted, PSF-corrected, and Voronoibinned surface brightness maps are derived by using a median of the background-corrected Bootstrapping-LIRA realizations.The final mosaics and the noise level are used to generate three different frames to be stored in the final products: (1) an average adaptive X-ray surface brightness map, (2) a noise level map, and (3) an S/N map.

Quality Tests
This section presents the results from a series of quality tests designed to evaluate specific aspects of the output mosaics generated with SAUNAS.
1. Identify the fraction of false-positive and false-negative detections (Section 2.3.1).

Estimate the flux conservation of the deconvolution/
Voronoi binning process (Section 2.3.2) 3. Quantify the quality of SAUNAS performance as compared to that of other methods (arestore; Section 2.3.3).

False-positive/False-negative Ratio
For quality assessment, SAUNAS is tested using two different models varying the exposure time to reduce the photon flux and the detectability conditions: 1. a model of an idealized edge-on galaxy with two lobes emerging from a jet (double-jet model) and 2. a shell-like structure with a central bright source (cavity model).
The models are created as combinations of Gaussian 2D probability distributions (astropy.convolution.Gaus-sian2DKernel) with different ellipticities and rotations as described in Table 1.Following PSF convolution, a synthetic observed events map is generated using a random Poisson distribution (numpy.random.poisson).
The double-jet model includes the emission from three sources: the galactic disk, a bright core, and the lobes.The range of surface brightnesses is ∼10 −6 -10 −8 s −1 cm −2 arcsec −2 , excluding the considerably brighter (3-5 orders of magnitude brighter) peak surface brightness of the core.Its morphology mimics the predominant structure observed in double-jet radio galaxies such as Centaurus A (Hardcastle et al. 2007).
The other test simulation, a cavity model, contains a hollow shell with a central bright source.This model provides an important pipeline test for the reconstruction of cavities found in the intergalactic medium.The detection of cavity rims seen in projection against the diffuse emission from the hot intracluster and/or intergalactic medium is challenging.These large bubbles potentially provide a useful record of interactions between AGNs and the intergalactic medium, in which the expansion of the associated radio lobes excavates the surrounding medium (Pandge et al. 2021).Our test model is designed to be particularly challenging: an X-ray cavity with a dominant central source representing an AGN (Blanton et al. 2001;Dunn et al. 2010).The surface brightness background level of both models is fixed at 5 × 10 −9 s −1 cm −2 arcsec −2 , and the equivalent exposure time is assumed to be flat and varying from t = 10 exp 8 to t = 10 exp 4 s cm 2 .For reference, t = 5 10 exp 5 s cm 2 equals ∼10 ks at the 0.3-1.0keV band. 19he synthetic data are generated using the real PSF associated with the Chandra/ACIS data sets of NGC 3656 (Arp 155, PID: 10610775, PI: G. Fabiano; Smith et al. 2012).This PSF, which displays the characteristic ellipsoid pattern of off-axis ACIS observations, is selected as a worst-case scenario, given its extreme ellipticity due to its off-axis position in the detector array.The readout streak20 is visible as a spike departing from the center of the PSF at a position angle of approximately −70°(north = 0°, positive counterclockwise).
The simulated observed events are passed to the SAUNAS pipeline for processing, followed by a comparison between the detected (3σ) maps and truth models.The quantitative quality test includes identification of the fraction of pixels that were incorrectly identified as false negatives and false positives.
Figure 2 demonstrates the deconvolution and smoothing process for a mock galaxy with t = 5 10 s exp 6 having both diffuse X-ray emission and an extended PSF.The position angle selected for the model galaxy (Table 1) is selected specifically to offer a nontrivial test for the PSF deconvolution method.By using a position angle of 45°, the resulting convolved image displays two elongated features with apparently similar intensity (center left panel in Figure 2; PSF-convolved source): one real and one created by the PSF.If the PSF elongated feature is removed in the final images, we can conclude that the image reconstruction was successful.
After Poisson sampling (see simulated observation panel in Figure 2), the resulting events map is equivalent to the processed CIAO event files.The events map shows broad emission for the core of the galaxy model in which the disk is indistinguishable.The two lobes are still present but Note.Columns: (1) name, (2) component, (3) size, (4) surface brightness, (5) eccentricity, (6) position angle.
considerably blended with the emission from the inner regions.
The events are then processed using SAUNAS (LIRA deconvolution, bootstrapping, and VorBin steps).
The results from the PSF deconvolution (LIRA-deconvolved panel in Figure 2) show a removal of most of the PSF emission, recovering the signal from the disk of the galaxy and removing the PSF spike emission.However, a significant amount of noise is still visible, and the background level is difficult to estimate (lower left panel of Figure 2).
After applying the bootstrapping and Voronoi binning methods, the resulting final corrected mosaic (final smoothed mosaic panel, Figure 2) clearly shows the signal from the X-ray lobes, the disk, and the central bright core over the background.The 2σ and 3σ contours show the detected features following the calibration procedures described in Section 2, demonstrating complete removal of the PSF streak in the final mosaics (at a 99.7% confidence level).The original shape and orientation of the disk are recovered, with the flux correctly deconvolved into the bright core of the model galaxy.Due to its dim brightness, the jet that connects the lobes with the main disk is notably distorted in the final mosaic but still visible at a 2σ confidence level.For this test, the fraction of pixels unrecovered by the pipeline that were part of the model sources (false negatives) is 3.2%.On the other hand, the fraction of misidentified pixels that were part of the background (false positives) is 4.0%.The maps of false positives and false negatives for this test are available in Appendix B.
The test for the cavity model is repeated, sampling different equivalent exposure times.The results are shown in Appendix B. Figure 3 presents a comparison of the falsepositive and false-negative fractions as a function of the equivalent exposure time and model.For equivalent exposure times higher than t = 10 exp 6 s cm 2 , the false positives and false negatives are lower than 5%-10%.These fractions increase toward shorter exposures as expected, showing a notable increase to 20% of false negatives (true source emission that is unrecovered by SAUNAS) at approximately t = 5 exp 10 5 s cm 2 .The reason for this increase is the lack of detection of the dimmer outer regions in contrast with the brighter core (the lobes in the case of the double-jet model and the outer shell in the cavity model).Interestingly, the fraction of false positives does not increase substantially even at extremely low equivalent exposure times, remaining stable at ∼10% down to t < 10 exp 4 s cm 2 .This result demonstrates that even in cases of extremely short exposure times, SAUNAS is not expected to generate false-positive detections, which is a critical requirement for our study.

Flux Conservation
In an ideal scenario, the total flux of the events processed by SAUNAS should be equal to the total flux in the preprocessed frames by CIAO.In practice, the baseline model assumptions during the deconvolution process may affect the total flux in the resulting frames.LIRA assumes a flat background model that-combined with the counts in the source-tries to fit all the events in the image.However, deviations from this ideal scenario (nonuniform background, regions with different exposure time) generate differences between the input and output flux.In order to understand the impact of flux conservation in LIRA-deconvolved images, we must (1) analyze the relative difference of flux before and after deconvolution and (2) determine if the residuals of the deconvolution process generate any systematic artificial structure (i.e., photons may be preferentially lost around bright sources, generating holes in the image or erasing the dim signal from halos).
Total flux conservation is tested by measuring the ratio between the total flux in the input frames (those obtained at the end of the CIAO preprocessing; see Section 2.2.1) divided by the total flux in the final, SAUNAS-processed frames.We perform this test on real (UGC 5101; see Section 3.3) and synthetic observations (Section 2.3.1).The results are shown in Figure 4.
A total flux loss of ∼5% is detected in the SAUNASprocessed frames when compared with the preprocessed event maps by CIAO.The results are consistent in real observations (recovered flux ratio of 95.0% ± 1.7%) and in synthetic observations ( -+ 95.4 2.4 2.7 %).Using different simulations, we determined that this small flux loss is independent of the size of the FOV (in pixels), remaining stable at ∼5%.For the total area of the images analyzed, 5% of lost flux is negligible and well within the stochastic uncertainty of typical photometry (see the error bars in the profiles described in Figure 5).We consider a flux conservation ratio lower than 100% (i.e., 90%-99%) as erring on the side of caution from a statistical perspective: the bias of LIRA to lose flux implies that SAUNAS will not generate false-positive detections of hot gas halos.

Quality PSF Deconvolution Test
While Section 2.3.2 reported on the conservation of total flux in the image as a whole, this section discusses whether SAUNAS introduces unwanted artificial structures (fake halos or oversubtracted regions) in the processed maps.For this test, two additional types of test sources are used: (1) a point source and (2) a circular extended source.Both of these sources have been previously combined with a Chandra/ACIS PSF.To provide context, the results of LIRA are compared with those from CIAO/arestore. 21he results are displayed in Figure 10 (point source) and Figure 11 (circular extended source) and detailed in Appendixes A and B. To quantify the quality of the different deconvolution methods, radial surface brightness profiles of the truth (nonconvolved) model, the convolved simulated observations, and the resulting deconvolved maps are constructed.The profiles show that arestore tends to oversubtract the PSF, generating regions of negative flux around the simulated source.In the point-source case scenario, arestore oversubtracts the background by more than 5 × 10 −8 s −1 cm −2 pixel −1 , while LIRA recovers the background level with 5 times fewer residuals.The superiority of LIRA over arestore to recover diffuse structures is even more obvious in the extended source scenario (Figure 11): arestore shows a clear ringlike oversubtraction region around the source, dipping the background level to 10 −7.8 s −1 cm −2 pixel −1 as compared to the real (truth model) level of 10 −7 s −1 cm −2 pixel −1 .LIRA fits the background level significantly more faithfully, at a level of ∼10 −7.2 s −1 cm −2 pixel −1 .
We conclude that LIRA deconvolution results are better suited for the detection of diffuse X-ray emission, such as extended hot gas halos, compared to other PSF correction techniques, such as CIAOʼs arestore.Despite the model limitations described in Section 2.3.2,SAUNAS suppresses false-positive extended emission detections without overfitting the PSF while recovering the true morphologies of X-ray hot gas distributions.Thanks to the modularity of SAUNAS, future updates of the LIRA deconvolution software will be automatically implemented in our pipeline, improving the quality of the processed frames.

Sample Selection
We identified two astrophysical targets of interest for testing the pipeline: 1. NGC 3079, a highly inclined barred spiral galaxy with a prominent Fermi bubble (Hodges-Kluck et al. 2020; the primary benchmarking target; see Section 3.2), and 2. UGC 5101, an ultraluminous IR galaxy that is undergoing a galactic merger (Sanders et al. 1988;Imanishi et al. 2001; the secondary benchmarking target; see Section 3.3).
The targets used to demonstrate SAUNAS's capabilities were selected because they were known a priori to have extended soft X-ray emission detected by telescopes other than Chandra (NGC 3079), and the characterization of the extended emission was well documented with a detailed methodology that could be replicated in the published research.Insisting that the data come from a different platform provides a truth model independent of systematic effects inherently associated with Chandra.Finally, these specific targets were selected in order to test SAUNAS against simple and complex emission structures associated with the different morphologies (a disk galaxy and an interacting system).

NGC 3079
Large-scale bipolar winds and Fermi and radio bubbles are examples of extended structures observed around the center of the Milky Way in multiwavelength observations, including radio (MeerKAT, S-PASS), microwave (Wilkinson Microwave Anisotropy Probe), mid-infrared (Midcourse Space Experiment), UV (XMM), X-rays (Chandra, XMM-Newton, ROSAT), and gamma rays (Fermi Large Area Telescope) (Sofue 1995;Bland-Hawthorn & Cohen 2003;Finkbeiner 2004;Su et al. 2010;Carretti et al. 2013;Heywood et al. 2019).While the presence of these structures is well known in our own galaxy, Li et al. (2019) reported the first nonthermal hard X-ray detection of a Fermi bubble in an external galaxy, NGC 3079 (α = 150°.491, δ = +55°.680, D = 18.68 ± 1.32 Mpc, 11 04 kpc −1 ; Springob et al. 2005), using Chandra observations.Further works in the X-ray and UV using XMM-Newton and the Galaxy Evolution Explorer (GALEX) revealed a 30 kpc long X-ray galactic wind cone in NGC 3079 (up to 60 kpc in the far-UV; Hodges-Kluck et al. 2020) potentially associated with material that has been shocked by Type II SNe.
The length of the X-ray wind cone of NGC 3079 ( ~¢ R 3 , 16.3 kpc) contrasts with that of the bubble found by Li et al.  detector window, and as a consequence, these Chandra/ACIS observations were only used for point-source identification on NGC 3079 and subsequent masking for XMM-Newton.
Additionally, the available Chandra observations were much shallower (124.2 ks, with only 26.6 ks of usable exposure time due to contamination) than those of XMM (300.6 ks).Despite Figure 6 in Hodges-Kluck et al. (2020) showing signs of faint extended emission in the Chandra/ACIS data sets, the authors did not attempt to characterize it.Because ancillary X-ray observations from XMM-Newton are available for this object, NGC 3079 is an ideal case for benchmarking the low surface brightness recovery capabilities of the SAUNAS pipeline.
To detect the X-ray galactic wind in NGC 3079, the same bandpass (0.  Mimicking the methodology in the original article, an amplitude of ±20°is set for all the cones around their central axis.Surface brightness profiles are generated from the reprocessed Chandra observations, providing a direct comparison with previous results. The results show that the extended X-ray wind emission is detectable using Chandra observations, up to a limit of 4.1 kpc in the northeast filament) at a confidence level of 95% (2σ).The filament in the southwest of the galaxy is shortest at R ∼ 16-20 kpc.Interestingly, the XMM observations reveal a slightly larger extent in the X-ray emission on the west side (40 kpc) compared to the east side (30-35 kpc) according to Hodges-Kluck et al. (2020). 22The average limiting surface brightness (95% confidence level) is m = -+ -1.66 10 0.5 0.5 10 s −1 cm −2 arcsec −2 .Limiting surface brightness reaches its lowest limit when combining all the filaments, suggesting that the observations are limited by noise and not by systematic effects (if dominated by systematic gradients, a lower S/N would result from combining all the regions).

UGC 5101
UGC 5101 (z = 0.039, D = 161.8Mpc, 0.784 kpc arcsec −1 ; Rothberg & Joseph 2006) is an irregular galaxy that is undergoing a potential major merger.This object has previously been identified as a Seyfert 1.5 (Sanders et al. 1988), a low-ionization nuclear emission-line region galaxy (Veilleux et al. 1995), and a Seyfert 2 galaxy (Yuan et al. 2010).UGC 5101 has a very extended optical tidal tail (∼40 kpc) to the west from the nucleus, with a second semicircular tidal tail that surrounds the bright core of the galaxy with a radius of 17 kpc (Surace et al. 2000).Radio (Lonsdale et al. 2003), IR (Genzel et al. 1998;Soifer et al. 2000;Imanishi et al. 2001;Armus et al. 2007), and X-ray observations with Chandra and XMM-Newton (Ptak et al. 2003;González-Martín et al. 2009) suggest the presence of a heavily dust-obscured AGN in the nucleus of this galaxy.
The total exposure time and other information relevant to the Chandra/ACIS observations of UGC 5101 are provided in Table 2.The diffuse X-ray emission of UGC 5101 has been previously analyzed in the literature.Huo et al. (2004) found evidence for an inner hot gas halo of 8.7 kpc (10 4) and an outer halo of 14.3 kpc (17 0).Grimes et al. (2005) found that 95% of the 0.3-1.0keV emission is enclosed in the inner 8.75 kpc galactocentric radius (10 5).Smith et al. (2018Smith et al. ( , 2019) ) analyzed the Chandra/ACIS observations, finding that the 0.3-1.0keV emission has a size of 24 0 × 14 2 (∼19.1 × 11.3 kpc, position angle of 90°) and a total X-ray luminosity of = L log 41.6 X erg s −1 .Given these known robust detections, we employ SAUNAS in the characterization of the low surface brightness emission from UGC 5101.Three bandpasses are used to ensure a direct comparison to the analyses by Smith et al. (2019): soft (0.3-1.0 keV), medium (1.0-2.0 keV), and hard (2.0-8.0 keV).The flux conservation ratio after PSF deconvolution in this exposure is 96.0%± 0.02% in the three bands.The processed X-ray emission maps are presented in Figure 6, in comparison with the optical/near-IR observations from the Hubble Space Telescope (HST), as well as ancillary radio observations for reference.The PSFs and unprocessed events of the UGC 5101 observations in the three bands analyzed are available in Figures 17 and 19 in Appendixes C and D, respectively.
The results are summarized in Figure 6.The analysis of the Chandra/ACIS observations with SAUNAS reveals that even after PSF deconvolution, the soft X-ray emission of UGC 5101 still shows extended emission around its core.The 0.3-1.0 and 1.0-2.0keV bands present X-ray emission with an elongated morphology, with a characteristic bright plumelike structure in the core, oriented in the north-south direction (μ soft = 1-2 × 10 −8 s −1 cm −2 arcsec −2 ), very similar to the results of Smith et al. (2018).In contrast, the hard band only shows a bright core in the center, compatible with an unresolved source.In the soft band, the diffuse X-ray emission is detectable down to levels of Both soft-and medium-band emissions are centered over the main core of UGC 5101, showing the same orientation as observed by Smith et al. (2019).The soft-band emission extends up to 25″ (20 kpc) to the north and 17″ (13.5 kpc) to the south (3σ).
The spatial distribution of X-ray emission around UGC 5101 is generally comparable to that detected in previous works (Smith et al. 2019).However, at approximately 40″-60″ radius to the northeast (α, δ = 143°.980, +61°.363), the SAUNAS map reveals a diffuse bridge connecting with UGC 5101 at a ∼2σ level (μ soft ∼ 6.2 × 10 −10 s −1 cm −2 arcsec −2 in the soft band).For clarity, we will refer to this extended emission as X1.confidence level with a comparable angular area to UGC 5101 but with a maximum surface brightness 20-30 times lower than the main object (see Figure 7).has been discussed previously in the literature not as part of the UGC 5101 system but rather as a potential higher-z galaxy cluster (Clerc et al. 2012;Koulouridis et al. 2021) in need of spectroscopic confirmation.
Observations of the Giant Metrewave Radio Telescope (GMRT) 150 MHz all-sky radio survey23 (Intema et al. 2017; see bottom left panel in Figure 6) confirm the detection of an adjacent source centered over the recovered X-ray emission, with a surface brightness of μ = 10 −4 Jy arcsec −2 .The GMRT flux maps are shown as contours in Figure 6, revealing a peak of radio emission over the center of X1 in addition to UGC 5101.GALEX UV observations provide a near-ultraviolet flux of 5.14 ± 0.15 × 10 −6 Jy (Seibert et al. 2012) but only upper limits in the far-ultraviolet band (9.8 × 10 −6 Jy).Recent JWST observations (GO 1717; PI: Vivian U.; MIRI) of UGC 5101 were inspected for this work, but they suffer from extreme saturation of the bright core of the galaxy, and the outer X-ray-emitting region lies outside the footprint, so they were discarded for this study.While investigating the nature of this extended X-ray emission is beyond the scope of this paper focused on the presentation of the SAUNAS pipeline, we briefly discuss the main hypotheses (hot gas plume or high-z galaxy cluster) in Section 4.

Limitations
We have demonstrated the SAUNAS methodology to be successful in recovering dim, extended surface brightness X-ray features under low-S/N conditions through performance tests using both synthetic (Section 2.3) and real (Section 3) X-ray data sets.
There are, however, several limitations of SAUNAS in its current form that will be addressed in future versions of the pipeline.Among them, SAUNAS does not attempt to provide a quantitative separation between extended sources, such as a segmentation map.Deblending of extended X-ray sources is one of the main objectives of a complementary code, EXSdetect (Liu et al. 2013), using a friend-of-friends algorithm.Other specialized pipelines for X-ray observations, such as CADET, based on machine-learning algorithms, allow for the identification of specific source morphologies, such as X-ray cavities (Plšek et al. 2024).The potential combination of SAUNAS for generating low surface brightness detection maps with existing morphological identification and segmentation software will be explored in the future.
Another limitation of the SAUNAS pipeline is the precision of the PSF.The generation of the Chandra/ACIS PSFs depends on multiple factors, including, but not limited to, the position of the source on the detector, the spectral energy distribution of the source, or the specific parameters fed into the MARX simulation software (like the aspect blur).For example, LIRA deconvolution software only accepts one PSF for the whole image, and as a consequence, the shapes of sources at large distances from the center of the image might be inaccurate.This phenomenon can cause residuals if observations present bright sources at high angular distances from the center of the source, since the deconvolution will be based on the PSF at the center of the observation but not at the location of the secondary contaminating source.As an attempt to quantify this effect, we estimate in Figure 8 the variation of the PSF size (R 90% , radius that contains 90% of the flux of a point source) versus angular separation to the source using CIAO psfsize_srcs,24 based on the Chandra/ACIS observations of UGC 5101.The results show that the PSF increases by a factor of ×2 in ∼2′ (×10 in ∼10′).In our science cases, no bright object was observed in the environment of the main sources (NGC 3079, UGC 5101), so the main contributors to the scattered light are the sources for which the PSF was calculated.However, observers must be wary of strong residual PSF wings from nearby sources at ∼2′ and longer distances.While a complete analysis of the uncertainties of the PSF in Chandra is outside the scope of the current paper, we refer to the Appendix in Ma et al. (2023) for a review in the field.

NGC 3079
The analysis of the Chandra/ACIS observations in the field of NGC 3079 revealed signs of X-ray wind out to galactocentric distances R ∼ 30 kpc, compatible with previous observations using XMM-Newton (Hodges-Kluck et al. 2020).While XMM-Newton is able to trace the extended X-ray emission out to larger distances (∼40 kpc) in some directions, some considerations must be made in order to compare XMM-Newton results with the benchmark study provided here.

XMM-Newton observations of NGC 3079 combine an
∼11 times longer exposure time (300.6 ks) than the usable time in Chandra/ACIS (26.6 ks) observations.2. XMM-Newton has a larger effective area (4650 cm 2 at 1 keV) than Chandra (555 cm 2 ), at the expense of a lower spatial resolution25 (XMM-Newton/FWHM = 6″ versus Chandra/FWHM = 0 2).While the aperture is smaller, proper masking of point sources improves detectability of dim structures by reducing the background noise.3. The analysis of the X-ray emission by Hodges-Kluck et al. (2020) is based on the inspection of the quadrant stacked images with a certain signal and radial threshold (see their Figure 4, center panel).The methodology they use to calculate the limiting radius of the diffuse X-ray emission is not clearly stated in their analysis, making a direct and accurate comparison of results difficult.
Despite the differences of the detection methods, we conclude that SAUNAS is able to recover extended, low surface brightness X-ray emission using Chandra/ACIS X-ray observations of NGC 3079, in excellent agreement with the deeper exposure taken by XMM-Newton.

10
1.3 1.5 9 s −1 cm −2 arcsec −2 , 0.3-1.0keV) located in the northeast of the UGC 5101 merging galaxy.X1 has been previously detected in X-rays by Smith et al. (2019), but its emission was not discussed or treated as part of UGC 5101ʼs outskirts.Other works (Clerc et al. 2012;Koulouridis et al. 2021) tentatively classified X1 as a potential background galaxy cluster, but this feature remains unconfirmed, as spectroscopic observations are unavailable.X1 is detected also in GMRT 150 MHz observations as a secondary source adjacent to UGC 5101, confirming the existence of a feature at this location.Two main hypotheses regarding the nature of X1 are that 1. X1 is part of the extended X-ray-emitting envelope of UGC 5101, and 2. X1 is a background source, potentially the extended envelope of a higher-z object, such as a massive earlytype galaxy or a cluster.
Although the X-ray emission in the soft and medium bands of X1 is adjacent to that of UGC 5101, and both objects have a dominant emission in the soft band compared to the medium and hard (see Figures 6 and 7), the emission could still be part of a hot gas halo at higher-z.In fact, the center of the Chandra/ ACIS X-ray emission overlaps remarkably well with that of a background galaxy.Figure 9 shows the HST Advanced Camera for Surveys (ACS) imaging (bands) centered over X1, with the soft-band X-ray emission contours overlapped for reference.The peak of X-ray emission is coincident with the  position of a background galaxy (WISE J093555.43 +612148.0).Unfortunately, WISE J093555.43 + 612148.0does not have spectroscopic or photometric redshifts available.
While resolving the nature of X1 is beyond the scope of this paper, we conclude that the test performed with the Chandra/ ACIS observations of UGC 5101 using SAUNAS demonstrates the pipeline's capabilities in successfully producing adaptively smoothed, PSF-deconvolved X-ray images in different bands.The image reduction process presented here allows for a better calibration of the background to recover details at both high resolution and high surface brightness (inner core structure of the merging galaxy) as well as extended ultralow surface brightness regions, such as the previously unknown extended emission around UGC 5101.

Conclusions
In this paper, we have presented SAUNAS, a pipeline to detect extended, low surface brightness structures on Chandra X-ray observations.SAUNAS automatically queries the Chandra Archive, reduces the observations through the CIAO pipeline, generates PSF models, and deconvolves the images, identifying and masking point sources and generating adaptatively smoothed surface brightness and detection S/N maps for the sources in the final mosaics.We have demonstrated through tests of simulated data and comparisons to published results that the SAUNAS pipeline distinguishes itself from other existing X-ray pipelines by meeting the following main objectives: 1. generating X-ray detection maps for extended sources in a consistent, statistically reproducible way and 2. providing a modular framework for reduction of Chandra/ACIS observations focusing on the detection of faint extended sources, simplifying the access to X-ray archival observations for multiwavelength studies.
Our approach to meeting these objectives is to assess the statistical probability that signal in low-count areas is real.This strategy can both produce detections of previously overlooked diffuse emission and minimize false-positive detections of extended hot gas emission.In Section 3, we compare SAUNASprocessed archival Chandra/ACIS data to published results.This section demonstrates that the proposed methodology succeeds in recovering the extended emission detected in a selection of local Universe targets.While the CIAO pipeline provides a canonical and highly efficient procedure to reduce the Chandra observations, the secondary analysis of the resulting event files is usually performed in an independent way by the observers.Such a situation results in two suboptimal consequences: (1) most X-ray studies are focused on single objects or very small samples (three or four objects), and (2) most studies develop their own procedure to correct the PSF effects (if considered), generate smoothed maps, and determine the significance of emission over the background.Planned future work includes an analysis of the extended emission of nearby galaxies using Chandra/ACIS archival data and releasing the tools to the astronomical community.In this first article, we made the processed maps available26 for the community through the Zenodo open repository.
A benefit of the automated functionality provided by this tool is its provision of straightforward access to high-level archival Chandra products and facilitation of their use in multiwavelength studies.In future works of this series (A. S. Borlaff et al. 2024, in preparation), we will explore the X-ray emission of a sample of targets using the SAUNAS pipeline, focusing on the evolution of lenticular galaxies based on Chandra/ACIS data in combination with HST and Spitzer observations.The serendipitous discovery presented in this work in one of the galaxies studied, UGC 5101, an ongoing merger galaxy, demonstrates that the combination of multiwavelength legacy archives, such as those of Chandra, GMRT, and HST, may already hold the information to disentangle the impact of the different evolutionary drivers in galaxies.
arestoreʼs PSF oversubtraction.Given that the main aim of SAUNAS is the detection of extended sources, we extend the analysis from Appendix A to SAUNAS processing of an extended source model.
Figure 11 shows the result from this analysis.A simulated source with a central surface brightness of μ = 10 −3 s −1 pixel −1 and a background level of μ = 10 −7 s −1 pixel −1 is convolved with the same PSF used by the point-source tests described in Appendix A. The resulting event file of convolved data is then processed by SAUNAS and deconvolved by a standard application of CIAO/arestore.A comparison of the associated surface brightness profiles provides both quantitative and qualitative assessments of the different light reconstruction methods.
The top right panel of Figure 11 shows that the methodology adopted in SAUNAS produces a result that is more closely aligned with our science-driven requirements.Proper treatment of the fainter regions surrounding objects is a critical factor for the detection of faint extended emission, such as hot gas X-ray halos around galaxies.While SAUNAS produces a wellbehaved profile that smoothly transitions to the background level at large radii, CIAO/arestore manufactures an oversubtracted background region surrounding the object, similar to its treatment of point sources (Appendix A).
Figures 12 and 13 show the results of the false-positive/ false-negative quality test described in Section 2.3.1 for the double-jet model.In Figures 14 and 15, the equivalent results are shown for the cavity model.Each row represents different equivalent exposure times, from t = 5 10 exp 7 s cm 2 to t = 5 10 exp 4 s cm 2 .We refer to the captions in the figures for details.Table 1).The top row shows the simulated event images.The middle row shows the final recovered surface brightness maps after processing with SAUNAS.Dashed contours represent the 3σ and dotted contours the 2σ detection level of X-ray emission.The bottom row represents the false-positive (red) and false-negative (blue) detection maps for each simulation (see Section 2.3.1).In the columns from left to right, the equivalent exposure times are t = 10 exp 7 , 5 × 10 6 , and 10 6 s cm 2 .See the labels in the panels.Color bars represent the number of events per pixel (event images) and the surface brightness flux (final mosaics).Table 1).The top row shows the simulated event images.The middle row shows the final recovered surface brightness maps after processing with SAUNAS.Dashed contours represent the 3σ and dotted contours the 2σ detection level of X-ray emission.The bottom row represents the false-positive (red) and false-negative (blue) detection maps for each simulation (see Section 2.3.1).In the columns from left to right, the equivalent exposure times are t = 5 10 exp 5 , 10 5 , and 5 × 10 4 s cm 2 .See the labels in the panels.Color bars represent the number of events per pixel (event images) and the surface brightness flux (final mosaics).Table 1).The top row shows the simulated event images.The middle row shows the final recovered surface brightness maps after processing with SAUNAS.Dashed contours represent the 3σ and dotted contours the 2σ detection level of X-ray emission.The bottom row represents the false-positive (red) and false-negative (blue) detection maps for each simulation (see Section 2.3.1).In the columns from left to right, the equivalent exposure times are t = 10 exp 7 , 5 × 10 6 , and 10 6 s cm 2 .See the labels in the panels.Color bars represent the number of events per pixel (event images) and the surface brightness flux (final mosaics).

Figure 1 .
Figure 1.SAUNAS pipeline flowchart.From left to right: SAUNAS precalibrates the Chandra observations by first using Chandra X-ray Center (CXC)/CIAO, which generates the event files, extended source masks, and PSFs.The events in each individual visit are first resampled via bootstrapping and then deconvolved using LIRA.Voronoi binning is applied to each deconvolved observation and merged into a single flux map after sky background correction.

Figure 2 .
Figure 2. SAUNAS analysis test on a synthetic data set.Top left: underlying distribution of the simulated test source.Top right: PSF of the simulated observation.Center left: simulated underlying distribution of the test source convolved by the PSF.Center right: simulated observed events based on the PSF-convolved distribution.Bottom left: LIRA PSF-deconvolved average posterior image.Bottom right: adaptively smoothed final mosaic.Dashed contours represent the 3σ and dotted contours the 2σ detection level of X-ray emission.The equivalent exposure time for this test is t = 5 10 exp

Figure 3 .
Figure 3. Fraction of false positives and false negatives in the SAUNAS detection maps derived from two truth models as a function of the equivalent exposure time (cm 2 s).Blue symbols and lines represent the fraction of false negatives, while red represents the fraction of false-positive detections in the mock maps.Cross symbols correspond to the double-jet model, and filled circles represent the cavity model (see Table1).Vertical dashed lines indicate the median equivalent exposure times for the analyzed real observations in their respective bands.
(2019) using Chandra observations ( ¢ R 0.75  , 4.1 kpc).Hodges-Kluck et al. (2020) argued that the sensitivity of the longest Chandra observations in the soft X-ray band (E < 1 keV) is affected by the molecular contaminant buildup on the

Figure 4 .
Figure 4. Flux conservation in SAUNAS frames.The histogram represents the probability distribution of the ratio between the recovered flux after SAUNAS processing and the total flux of the input, preprocessed frames.
3-2.0 keV) as in Hodges-Kluck et al. (2020) is used.The available Chandra/ACIS observations of NGC 3079 are detailed in Table 2.Each visit was reprocessed with independent PSF deconvolution, and then the visits were combined for Voronoi binning.Observations 19307 and 20947 were processed but discarded due to the presence of very largescale gradients and unusually high background levels in the detectors where the main emission from NGC 3079 is located.After processing the remaining observations (2038 and 7851) with SAUNAS, extended emission observed by Chandra is compared to the results from XMM-Newton.The PSFs of the 2038 and 7851 observations and their unprocessed events are available in Figures 16 and 18 in Appendixes C and D, respectively.Following the results from Figure 2 in Hodges-Kluck et al. (2020), four angular cone regions display diffuse emission: northeast (θ = 40°), southeast (θ = 110°), southwest (θ = −140°), and northwest (θ = −60°) (θ is measured counterclockwise, and north corresponds to 0°; see Figure 5).

Figure 5 .
Figure 5. Extended X-ray wind cones in NGC 3079, recovered in the Chandra/ACIS observations using SAUNAS.(a) Broadband (Chandra: 0.3-2.0keV) surface brightness profiles of the four filaments identified by Hodges-Kluck et al. (2020) using XMM-Newton and GALEX observations.Top to bottom: all filaments, northeast, southeast, southwest, and northwest.Radial detection limits are in the panels (95% confidence level).(b) SAUNAS-processed image showing 2σ contours (black; shown in white in panel (c)) with filament sectors in yellow.The radial detection limit indicated in panel (a) for each of the four filaments is shown as solid yellow sectors, while that of "all filaments" is shown as dashed yellow, following the methodology found in Hodges-Kluck et al. (2020).The thick dark red circle in (b) shows the maximum detection limit found with XMM-Newton, compatible with our results.(c) Comparison of the optical morphology (Pan-STARRS gri) of NGC 3079 with the extended X-ray emission.
Figure 7 displays surface brightness profile analysis results and associated comparisons with X1.The central surface brightness of X1 is μ soft =-−1 cm −2 arcsec −2 in the soft band and μ medium = -−1 cm −2 arcsec −2 in the medium band.The emission of X1 is detectable at a 3σ

Figure 27 in
Figure 27 in Smith et al. (2019) shows a hint of what might be emission jutting to the northeast of UGC 5101 where we see X1 but at a considerably lower detectability.The X1 feature

Figure 6 .
Figure 6.Diffuse X-ray emission of UGC 5101 as detected with SAUNAS/Chandra in the 0.3-1.0keV band (top), 1.0-2.0keV band (center), and 2.0-8.0 keV band (bottom).Left: HST/ACS color image (red: F814W; green: F435W+F814W; blue: F435W).Right: SAUNAS map of the diffuse X-ray emission, corrected for PSF effect, point sources, and background.Solid contours represent 3σ detections and dotted contours the 2σ detection level of X-ray emission, represented in white (left panels) and black (right panels) for contrast.Solid red contours show GMRT 150 MHz data.The white dashed ellipse represents the previous detection limits reported by Smith et al. (2019) of UGC 5101 in the same band.

Figure 7 .
Figure 7. Surface brightness profiles of the diffuse X-ray emission of UGC 5101 and the extended diffuse northeast source (X1) detected with SAUNAS/Chandra in the 0.3-1.0 and 1.0-2.0keV bands.Radially averaged surface brightness profile (blue upward triangles: 0.3-1.0keV band; purple downward triangles: 1.0-2.0keV band).Shaded areas represent the 1σ and 2σ error bars.Solid blue and dashed purple vertical lines represent the 2σ detection limits for the 0.3-1.0keV and the 1.0-2.0keV bands.Blue and purple stars show the average surface brightness of the northeast extended emission X1, represented at the measured galactocentric distance from UGC 5101.

Figure 8 .
Figure 8. Variation of the Chandra/ACIS PSF size as a function of the angular separation to the center of the FOV.Vertical axis: radius enclosing 90% of the flux from the PSF at 1.0 keV, based on the observations of UGC 5101.Horizontal axis: angular separation to the center of the source, approximately the center optical axis.The horizontal dotted lines mark the PSF sizes that correspond to ×2, ×5, ×10, and ×20 the PSF size at its center (×1).

Figure 11 .
Figure 11.Extended source PSF deconvolution test.Top left panel: emission from a circular source object with a central surface brightness of μ = 10 −3 s −1 pixel −1 and a background level of μ = 10 −7 s −1 pixel −1 convolved with a reference Chandra/ACIS PSF (NGC 3862, α = 176°.2709, δ = +19°.6063; ObsID: 514).Top right panel: surface brightness profiles of the ground-truth (non-PSF-convolved) test source (red solid line), PSF-convolved source (gray dashed-dotted line), SAUNASdeconvolved image (blue dashed line), and CIAO/arestore-deconvolved (orange dotted line) images.The horizontal dotted line represents the sky background of the model, and the vertical dotted line represents the radial limit of the circular test source (R = 15 pixels).Bottom left panel: SAUNAS-deconvolved image.Bottom right panel: CIAO/arestore-deconvolved image.Note that the convolved image (events map) and the CIAO/arestore-deconvolved image have been processed using Voronoi binning for visualization purposes of the surface brightness.See the legend and color bar in the figure.

Figure 12 .
Figure12.SAUNAS processing test using the double-jet model as a function of the equivalent exposure time (see Section 2.3.1 and Table1).The top row shows the simulated event images.The middle row shows the final recovered surface brightness maps after processing with SAUNAS.Dashed contours represent the 3σ and dotted contours the 2σ detection level of X-ray emission.The bottom row represents the false-positive (red) and false-negative (blue) detection maps for each simulation (see Section 2.3.1).In the columns from left to right, the equivalent exposure times are t = 10

Figure 13 .
Figure 13.(Continuation of Figure 12) SAUNAS processing test using the double-jet model as a function of the equivalent exposure time (see Section 2.3.1 andTable 1).The top row shows the simulated event images.The middle row shows the final recovered surface brightness maps after processing with SAUNAS.Dashed contours represent the 3σ and dotted contours the 2σ detection level of X-ray emission.The bottom row represents the false-positive (red) and false-negative (blue) detection maps for each simulation (see Section 2.3.1).In the columns from left to right, the equivalent exposure times are t = 5 10

Figure 14 .
Figure14.SAUNAS processing test using the cavity model as a function of the equivalent exposure time (see Section 2.3.1 and Table1).The top row shows the simulated event images.The middle row shows the final recovered surface brightness maps after processing with SAUNAS.Dashed contours represent the 3σ and dotted contours the 2σ detection level of X-ray emission.The bottom row represents the false-positive (red) and false-negative (blue) detection maps for each simulation (see Section 2.3.1).In the columns from left to right, the equivalent exposure times are t = 10

Figure 19 .
Figure 19.Event maps of the UGC 5101 observations before processing with SAUNAS.Left to right: UGC 3079 on the 0.3-1.0keV band, UGC 3079 on the 1.0-2.0keV band, and UGC 3079 on the 2.0-8.0 keV band.The binning (pixel scale) for the UGC 5101 images is 1 × 1 (0 492 pixel −1 ).Solid black contours represent the 3σ and dashed contours the 2σ detection level of X-ray emission.

Table 1
Photometric and Structural Properties of the Synthetic Test Models