The New Swift/UVOT+MaNGA (SwiM) Value-added Catalog

The new SwiM catalog, SwiM_v4.1, includes a subset of galaxies in the MaNGA survey (Bundy et al. 2015; Yan et al. 2016), which is an IFU spectroscopic survey included in the fourth generation of SDSS (SDSS-IV; Blanton et al. 2017). The defining features of the MaNGA sample include (1) a uniform stellar mass distribution for M_* > 10⁹ M_⊙, as estimated via SDSS i-band absolute magnitudes; (2) uniform spatial coverage in units of half-light radii (R_e ); and (3) maximized spatial resolution and signal-to-noise ratio (S/N) for each galaxy (Wake et al. 2017). We used the MaNGA Product Launch 11 (MPL-11), which is the last data release, is presented in SDSS-IV Data Release 17 (i.e., SDSS-DR17; Abdurro'uf et al. 2022), and includes 10,145 galaxies.

While full descriptions of both the MaNGA and Swift/UVOT data sets are presented in Molina et al. (2020b), we summarize each of them here. The MaNGA spectroscopic observations were obtained with hexagonal IFU fiber bundles mounted on the 2.5 m Sloan Foundation Telescope (Gunn et al. 2006), and fed into the dual-channel Baryon Oscillation Spectroscopic Survey (BOSS) spectrographs (Smee et al. 2013). The spectra span a total wavelength range from 3622 to 10350 Å at a spectral resolution of R ∼ 2000. The data cubes have a point-spread function (PSF) of ∼25, and a spatial sampling of 05, with an average exposure time of ${t}_{\exp }\sim 2.5\,{\rm{hr}}$ (Yan et al. 2016). However given that MaNGA is a ground-based survey, the measured PSFs for the individual data cubes are occasionally significantly larger than 25. We account for this in our sample selection as described in Section 2.2.

The Swift/UVOT is a 30 cm telescope, with a field of view (FOV) of ${17}^{{\prime} }\times {17}^{{\prime} }$ and an effective plate scale of 1'' pixel⁻¹ (Roming et al. 2005). The instrument has three NUV filters, two with wide bandpasses (uvw2 and uvw1), and one with an intermediate-width bandpass (uvm2). The three filters have slightly different PSFs, but all are close to 25, i.e., similar to that of the MaNGA optical spectra. The properties of these filters are given in Table 1. As in the original catalog, none of our data suffer from coincidence loss (see Section 3.3 in Molina et al. 2020b for a thorough discussion).

In order to define the SwiM_v4.1 sample, we crossmatched the MPL-11 catalog with the UVOT data archive hosted by the High Energy Astrophysics Science Archive Research Center (HEASARC) as of 2021 August, and required that each object was observed with both the uvw1 and uvw2 NUV filters. Of the 10,145 MPL-11 galaxies, 570 met these initial constraints. However, to further ensure both uniform and high-quality data across the entire SwiM catalog, we enforced a number of additional requirements for inclusion in the sample. Specifically, we included objects that had:

• 1.  
  science-ready MaNGA data cubes in MPL-11,
• 2.  
  PSF_MaNGA < PSF_uvw2, and
• 3.  
  both uvw1 and uvw2 exposures longer than 100 s and images uncontaminated by foreground stars.

While most of the data cubes in the MaNGA survey are science ready, a small fraction suffered critical errors, or had many dead fibers, and thus are not usable. We removed 10 such objects from our sample. Additionally, three objects had MaNGA PSFs that were larger than that of the uvw2 filter. Since we base the final spatial resolution and sampling for all of the images and maps in the SwiM catalog on the resolution of the uvw2 data, we excluded these objects from consideration. Finally, we required that the exposure times for both UV filters be at least 100 s in duration, and that the images were not contaminated by bright foreground stars. This excluded a further 11 objects from our sample. Thus, in total, we removed 24 galaxies from the data set based on these four constraints, creating a final sample of 546 objects. We report the UVOT exposure time statistics in Table 1.

In addition to the wide-bandpass images, we also included the medium-bandpass uvm2 images in the final data cubes, when available. For our sample of 546 objects, 496 had archival uvm2 observations, six of which had exposure times of less than 100 s. We thus excluded the uvm2 images for those six objects, leaving a total of 490 objects with uvm2 observations.

In addition to the 546 objects with archival data, we performed dedicated Swift/UVOT observations of a further 13 galaxies. The new observations were taken between 2020 May and 2021 January as part of the GI program 1619136 (PI: M. Molina), with the goal of creating a catalog with similar properties to the MaNGA sample. Thus the final SwiM_v4.1 catalog comprises 559 galaxies. We required that the new observations have sufficiently high S/N at large galactocentric radii (S/N ≳ 10 for star-forming galaxies and S/N ≳ 5 for systems currently being quenched), resulting in ∼1–2 ks per filter for each object. Since uvm2 was not required for inclusion in our sample, we only completed dedicated observations in the uvw2 and uvw1 filters. We summarize the new observations in Table 2.

Table 2. Summary of New Swift/UVOT NUV Observations

MaNGA	R.A.	Decl.	Date Obs.	Date Obs.	${t}_{\exp ,{\rm{w}}1}$	${t}_{\exp ,{\rm{w}}2}$
ID	(deg)	(deg)	(uvw1)	(uvw2)	(s)	(s)
(1)	(2)	(3)	(4)	(5)	(6)	(7)
1-23687	262.2574	60.0894	2020-07-31	2020-10-17	862	892
1-38856	54.8918	−0.5119	2020-11-03	2020-11-08	1864	1839
1-71891	119.5533	38.5353	2020-12-07	2020-12-09	4346	3674
1-153077	119.2220	32.3456	2020-09-30	2020-09-21	2112	1922
1-153246	119.0444	33.2451	2020-12-06	2020-09-06	2011	3563
1-210866	245.8423	39.2153	2020-07-21	2020-07-24	890	861
1-351792	121.1725	50.7185	2020-11-26	2020-11-29	138 a	823
1-352130	122.6799	52.5219	2020-08-24	2020-08-27	2663	1611
1-419427	182.2852	35.6358	2021-01-21	2021-01-22	1679	1930
1-457869	204.6236	26.0776	2020-11-10	2020-11-13	3530	2869
1-458124	203.5957	27.4613	2020-05-17	2021-02-10	2026	1612
1-592984	215.7184	40.6226	2020-12-01	2020-12-03	1951	1972
1-593748	229.3245	29.4005	2020-05-03	2020-05-10	819	903

Notes. Column (1): MaNGA ID from MPL-11. Columns (2)–(3): R.A. and decl., in degrees. Columns (4)–(5): the date of the first observation of each galaxy with the uvw1 and uvw2 filters, respectively. Columns (6)–(7): total exposure time in seconds in the uvw1 and uvw2 filters, respectively.

^a The longer of the two uvw1 observations for 1-351792 did not have star tracking so we did not achieve the requested ∼1 ks exposure time.

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We processed the raw UVOT data from HEASARC using the Swift UVOT Pipeline, ⁹ which is an improved and automated version of the subroutine uvot_deep.py written by Lea Hagen. ¹⁰ The pipeline was originally described in detail in Molina et al. (2020b), and has been updated for this work. The new pipeline automates a modified version of the uvot_deep.py subroutine and creates multiple science-ready mosaics in a single execution. One of the notable new features introduced in the updated version of the pipeline is a rebinning subroutine, which allows for a more complete use of the UVOT archive. We briefly describe the data reduction process and highlight the updated changes to the pipeline below.

The UVOT data are reduced following the data-processing procedures outlined in the UVOT Software Guide. ¹¹ Each UVOT image is a summation of a series of ∼11 ms frames taken over the specified observation time. These images are then stacked to create the final image mosaic.

The individual images and exposure maps are first aspect corrected, i.e., aligning the individual ∼11 ms frames to the position of known sources. The counts images are then corrected for large-scale sensitivity issues associated with the detector, and all bad pixels are masked. Occasionally, uvot_deep.py will produce errors that cause the exposure map to have 0 or NaN values for small regions. These errors only occurred for one galaxy in our sample and do not affect the galaxy itself. We provide masks to correct this issue, as described in Appendix B.

The updated version of the pipeline then corrects each image for the degradation of the detector as described in the 2020 update of the UVOT Software Guide, ¹² as well as the dead time which accounts for approximately 2% of the full frame's exposure time.

The UVOT software then requires that each image included in the stacked mosaic be aspect corrected, have a single frame time (currently defined as the standard full frame exposure time of 11.0322 ms), and be 2 × 2 binned to produce a plate scale of 1'' pixel⁻¹. The last step involves binning groups of four pixels and is usually completed by the UVOT onboard processing before the data are sent down. However, there are instances where images in the archive are 1 × 1 binned. The new version of the pipeline rebins these images and updates the world coordinate system (WCS) accordingly, thus allowing the frames to be used in the mosaic. After completing these data quality checks, all science-ready images are stacked to create the final mosaic.

We note that cosmic-ray corrections are not necessary for UVOT images given that the images are the sums of ∼11 ms frames. In this observing regime, any cosmic ray that hits the detector will only affect a few frames and will produce only a few counts in a single location. The hit will therefore be incorporated into the sky background counts of the summed image.

Finally, coincidence loss, which occurs when two or more photons arrive at a similar location within the same ∼11 ms frame, is negligible for all the galaxies in our sample. In particular, for extended sources, coincidence loss can be neglected when the count rates are less than 10 counts s⁻¹ pixel⁻¹ (Breeveld et al. 2010). For comparison, the maximum count rate across all the UVOT observations in our sample is ∼2.1 counts s⁻¹ pixel⁻¹, which implies a correction factor of <0.3%. This is significantly smaller than the dead-time correction of 2%, allowing us to ignore the effects of coincidence loss. A more thorough description of coincidence loss is provided in Section 3.3 in Molina et al. (2020b).

While a detailed description of the UVOT sky subtraction is given in Section 3.4 of Molina et al. (2020b), we briefly summarize the procedure here. We calculate the sky background counts using an elliptical annulus, where the inner radius is twice the Petrosian semimajor axis (R_p in the NASA-Sloan Atlas (NSA); Blanton et al. 2011), and the outer circular aperture radius is 4 R_p. We then mask all astrophysical contaminants in the sky annulus using Source Extractor (Bertin & Arnouts 1996), as well as any pixels that do not have the same exposure time as the center of the galaxy of interest. This issue affects the six galaxies in our sample listed in Table 3: four where significant regions of the surrounding background have different exposure times (denoted as "background" in Table 3), and two where different regions of the galaxies themselves have different exposure times (denoted as "galaxy" in Table 3). We urge caution when using the data for these galaxies and provide the original non-sky-subtracted Swift data in the catalog for all galaxies in the sample. Finally, we use the biweight estimator from Beers et al. (1990) to calculate the final background counts for the object. The background counts for each galaxy in each filter are given by the keywords SKY_W1, SKY_M2 and SKY_W2 in header data unit (HDU) 17 for each data cube.

We use the fully reduced MaNGA emission-line flux, EW, and spectral indices maps that are presented in MPL-11 and are identical to those of SDSS-DR17 (Abdurro'uf et al. 2022). These finalized data products are created via a two-step process. First, all MaNGA spectra are reduced using the updated MaNGA Data Reduction Pipeline (DRP; Law et al. 2021), which processes the raw data and creates flux-calibrated, sky-subtracted, coadded data cubes for each galaxy. After this initial reduction, the spectra are then fed into the MaNGA Data Analysis Pipeline (DAP; Westfall et al. 2019). The DAP takes the reduced IFU spectral data cubes and constructs 2D maps, including the flux, EW, and indices maps that we incorporate into our catalog. The DAP also provides measured quantities such as emission-line fluxes for various apertures within the galaxy.

The MaNGA spectra are fit by the DAP using multiple analysis methods which are differentiated by the keyword "DAPTYPE." We adopt the "HYB10-MILESHC-MASTARHC2" analysis method for two reasons: (1) the "HYB10" scheme is optimized for emission-line measurements and (2) the results from this analysis method were available for all of the galaxies in the SwiM_v4.1 catalog.

A complete description of the spectral fitting process is given in Section 5 of Westfall et al. (2019) and Section 4.2 of Law et al. (2021); we only briefly describe the process here. First the individual spaxels are Voronoi binned (Cappellari & Copin 2003), and the stellar continua are fit with the penalized pixel-fitting (pPXF) code (Cappellari & Emsellem 2004) that uses a combination of stellar templates from the MILES stellar library (Sánchez-Blázquez et al. 2006). This initial fit fixes the stellar kinematics for subsequent fits. Next, the entire spectrum for each individual spaxel (both emission lines and stellar continuum) is fit with pPXF using a combination of stellar templates presented in Maraston et al. (2020), which are derived from the MaNGA Stellar Library (MaStar; ¹³ Yan et al. 2019). The DAP uses the MaStar template library, rather than the MILES library, since the former covers the entire MaNGA spectral range. This allows all the data to be analyzed and enables more lines to be fit, as described in Section 4.2.1 of Law et al. (2021). We note that we recalculate both the Lick indices and D_n (4000) maps to allow for binned measurements, as described in Section 4.

The SDSS and Swift images are not corrected for either foreground extinction or internal attenuation. However, the MaNGA maps are corrected for foreground extinction by the DAP, which uses the Schlegel et al. (1998) E(B − V) value and assumes the Milky Way extinction curve from O'Donnell (1994), with R_V = 3.1. We keep this foreground extinction correction in our maps. Finally, the SFR measurements provided in the Swim_all catalog file are additionally corrected for internal attenuation, assuming the O'Donnell (1994) law, R_V = 3.1, and an Hα/Hβ ratio of 2.86 (Osterbrock & Ferland 2006, chapter 11). See Section 5.1 for more details on the SFR calculation.

In order to transform the uvm2, uvw1, u, g, r, i, and z images of Swift and SDSS to the resolution of uvw2, we first convolve each image with a 2D Gaussian kernel whose standard deviation is given in Equation (1) in Molina et al. (2020b). We assume the PSFs for the Swift filters have the FWHM presented in Column 4 of Table 1, while the SDSS images have a PSF FWHM of 14.

After the images are degraded to the same resolution as uvw2, we reproject the data onto the same WCS and spatial sampling using the reproject.reproject_exact function in Astropy (Astropy Collaboration et al. 2018). We compensate for any broadening in the Swift images associated with the reprojection process by applying a correction factor, , which is a polynomial that depends on the fractional pixel shift between the pixel grids. The correction accurately estimates the broadening effect to less than 0001, which is significantly smaller than the 292 PSF of uvw2. The broadening effect is much smaller for SDSS data, so we adopt a median correction for all of the SDSS images; this results in an error that is <1% of the final PSF width.

The entire process described above is also applied to the exposure maps and masks. All masked pixels were ignored in the computation. For the new (and in the case of SDSS, larger) pixels, if more than 40% of the new pixel area comes from bad pixels then the final pixel is masked.

We also account for the covariance introduced by the convolution and reprojection processes. In lieu of providing covariance matrices, we give the functional forms for f_covar = σ_covar/σ_{no_covar} in Equations (3)–(5) in Section 5.2 of Molina et al. (2020b).

Since the EW maps are the ratio of the line flux and continuum maps, they must be deconstructed to transform them accurately to the same spatial resolution and sampling as uvw2. We create the continuum maps by simply taking the ratio of the emission-line flux and EW maps. After this step, we apply the same convolution and reprojection process described in Section 4.1 to both the MaNGA emission-line flux and continuum maps. All masked pixels are again ignored in the computation, and each pixel in the mask map is rounded to 0 or 1 to create the final mask. When calculating binned EWs from the final maps, we recommend that the user bin the flux and continuum maps separately before taking the ratio to compute the EW.

Unlike the small broadening effect in the direct images, there is significant covariance in the MaNGA maps. To account for this variation, we multiplied the pixel correlation matrix by the refitted variance maps to build a final covariance matrix. We then distill this information into a functional form for f_covar, which is given by Equation (11) in Molina et al. (2020b).

In addition to the errors cited above, we note that testing by Belfiore et al. (2019) demonstrated that the Hα and EW errors are larger than the formal error by 25%. Therefore, users should multiply their errors by 1.25 for more realistic uncertainty estimates.

4.3.1. D_n (4000)

4.3.2. Lick Indices

The description of the catalog file can be found in Appendix A. This file holds all the basic physical properties, MaNGA and Swift observation properties, and integrated flux measurements for the galaxies in the sample. The only difference in format between the SwiM_v3.1 and SwiM_v4.1 catalog file is the addition of the DRP3QUAL value from the the MaNGA drpall file. DRP3QUAL is a bitmask that describes the quality of the data reduction. We also note that NSA_ELPETRO_THETA has been replaced with NSA_ELPETRO_TH50 to maintain consistency with the latest MaNGA drpall file. More information on the DRP3QUAL can be found in the MaNGA DAP documentation. ¹⁵

We note that the integrated Swift fluxes presented in the catalog file are aperture corrected to the SDSS r band; this maintains consistency with the integrated fluxes reported from the MaNGA drpall file. In the catalog file, we include the aperture correction factors in addition to the Swift integrated fluxes and inverse variances. We provide attenuation and foreground extinction-corrected SFR estimates within one effective radius, which are calculated by converting the foreground extinction-corrected Hα emission measurement provided in the dapall file using the relation provided in Kennicutt & Evans (2012). We additionally correct for internal attenuation with the O'Donnell (1994) law, R_V = 3.1, and an assumed intrinsic Hα/Hβ ratio of 2.86 (see chapter 11 of Osterbrock & Ferland 2006). For more details on the SFRs and integrated flux calculations; see Sections 2.3 and 4 of Molina et al. (2020b), respectively.

Finally, we present the correction factors for the volume-limited weights for the galaxies in our sample. We note that not all of the galaxies in the SwiM_v4.1 catalog are included in the MaNGA primary+full secondary samples (see Wake et al. 2017 for more detailed descriptions of the different MaNGA sample selections); these additional objects do not have correction factors. A more detailed discussion of the volume-limited weights is presented in Section 6.2.

The maps are the main component of the SwiM catalog. Each galaxy has a set of maps containing all of the SDSS and Swift/UVOT data, which have been transformed to the same WCS, spatial resolution, and sampling as its uvw2 image. The only difference in format between the SwiM_v3.1 and SwiM_v4.1 maps is the addition of 13 new lines that were not measured in previous versions of the DAP. The data models for the maps can be found in Appendix B.

The final cataloged data set is the SwiM_eline_ratios file, which is new to SwiM_v4.1. In order to provide as much information as possible, we have included all emission-line maps for every galaxy, regardless of the amount of spaxels that are masked. As a result, there are some emission-line flux maps that have few science-ready spaxels in the final product. For each emission-line map of each galaxy, we store the ratio of unmasked-to-total spaxels within one elliptical Petrosian radius in the SwiM_eline_ratio file. The file provides a convenient way to determine which galaxies, and thus which maps, will be useful for the user to download. The data model for this file is provided in Appendix C.

The original SwiM catalog (SwiM_v3.1) was created using the MPL-7 catalog which had a total of 4706 galaxies. In contrast, SwiM_v4.1 draws from the MPL-11 catalog which has 10,145 galaxies. Meanwhile, the Swift archive has also grown in size at a faster rate than the MaNGA survey between the MPL-7 and MPL-11 data releases. Given both the large difference in the parent MaNGA samples and the growth of the Swift archive, we do not directly compare SwiM_v4.1 and SwiM_v3.1. Instead, we discuss how each SwiM release compares to its associated MaNGA release. We briefly summarize the comparison of the SwiM_v3.1 catalog to the MPL-7 version of the MaNGA survey in Section 6.1.2; a more in-depth comparison is provided in Molina et al. (2020b).

6.1.1. SwiM_v4.1 and MPL-11 Version of MaNGA

While there are many configurations of the "MaNGA sample," ¹⁶ we cross-referenced all unique galaxies in the MaNGA catalog, including objects observed in ancillary programs. As a result, the final catalog of MaNGA galaxies that we crossmatched with the Swift archive has slightly different properties than the predefined MaNGA Primary, Primary+, and Full Secondary samples. We discuss the differences in these samples in detail in Section 6.2. Here we compare all unique galaxies in MaNGA MPL-11 to the SwiM_v4.1 catalog.

Figure 1 shows the redshift, g − r color, size, and axial ratio distributions for the SwiM_v4.1 and MaNGA galaxies. All quantities are taken from the NSA (Blanton et al. 2011), and size is defined as the elliptical Petrosian half-light semimajor axis measured for the r band (R₅₀). A majority of the objects in our catalog are nearby and relatively face on; 78% the galaxies have z ≲ 0.05, 97% have ${R}_{\mathrm{Pet},50}\lt {20}^{{\prime} ^{\prime} }$, and 77% have b/a ≳ 0.6. The SwiM_v4.1 distributions presented in Figure 1 are qualitatively similar to those of the MaNGA MPL-11 catalog. In fact, we performed a two-sample Kolmogorov–Smirnov (K-S) test, and found that both the g − r and R₅₀ distributions have p-values p > 0.05, meaning the SwiM_v4.1 and MaNGA distributions for these properties are not distinct at the 95% significance level.

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Figure 2 presents the distributions of stellar mass, reddening, and attenuation-corrected SFR, as described in Section 5.1, and the "star-forming main sequence" for SwiM_v4.1 and the MaNGA MPL-11 catalog. Approximately 87% of the SwiM_v4.1 catalog galaxies have $\mathrm{log}({M}_{* }/{M}_{\odot })\gtrsim 9.5$. The SwiM_v4.1 catalog recovers both the star-forming and passive galaxy sequences seen in the MaNGA MPL-11 catalog, but is slightly overpopulated at the lower-mass blue end of the diagram and underpopulated in the "green valley," i.e., the transition region between the star-forming and red passive galaxy sequences. A more thorough discussion of these trends are provided in Section 6.2.

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6.1.2. Comparison of SwiM_v4.1 and SwiM_v3.1

The MaNGA sample was not selected to be either magnitude or volume limited; instead weight corrections were provided by Wake et al. (2017) to correct each of the different MaNGA sample configurations statistically to a volume-limited data set. We rely on the "ESWEIGHT" volume-limited weight corrections (see Wake et al. 2017 for details), which includes the Primary, Color-Enhanced, and full Secondary samples. The combination of these three samples is defined as the MaNGA "main sample." The Primary sample is designed such that 80% of the galaxies in the Primary sample are covered by the MaNGA IFU out to 1.5 R_e . Meanwhile, the full Secondary sample is defined such that 80% of the galaxies have coverage out to 2.5 R_e . Both the Primary and full Secondary samples are further constrained to have a flat number density distribution in the absolute i-band magnitude, and comprise 47% and 37% of the MaNGA main sample, respectively. Finally, the Color-Enhanced supplementary sample fills in the undersampled regions of the NUV – i versus M_i plane, and includes low-luminosity red galaxies, high-luminosity blue galaxies, and "green valley" objects. These additional systems comprise 16% of the main MaNGA sample. Different configurations and their weights are described in detail in Wake et al. (2017), while the final target selection criteria can be found on the SDSS-IV DR17 website. ¹⁷ In addition to the MaNGA main sample, a number of ancillary programs were completed by the MaNGA survey, which produced objects that had MaNGA observations but did not have "ESWEIGHT" corrections. Out of the 559 objects in the SwiM_v4.1 catalog, 47 were from these ancillary programs, and thus are excluded from the calculations described below.

If the SwiM_v4.1 catalog is consistent with a random sampling of the MaNGA main sample, then the "ESWEIGHT" corrections would allow us to scale our new catalog to a volume-limited galaxy sample. To test this possibility, we compared the SwiM_v4.1 data set to randomly selected sets of objects drawn from the MaNGA main sample in the g − r versus M_* plane using a methodology similar to that presented in Molina et al. (2020b). Specifically, we created 1000 samples of 512 galaxies randomly drawn from the MaNGA main sample, and computed the K-S test statistic between the random data sets and SwiM_v4.1. The distribution of these values is presented in Figure 3. SwiM_v4.1 lies at the 13.6th percentile of the distribution, and is thus unlikely to be a true random sample.

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We then followed the same methodology as Molina et al. (2020b) and calculated the scaling corrections needed to match the data sets. First, we binned the SwiM_v4.1 and MaNGA main samples into 10 × 10 linearly spaced intervals in the g − r versus M_* plane, as shown in Figure 4. Based on the two top panels, the SwiM_v4.1 catalog appears on average to be redder and higher mass compared to the MaNGA sample. We divided the 2D histograms, both of which are normalized by their respective sample sizes, to create the density ratio plot shown in the bottom panel of Figure 4. The bottom panel illustrates a clear qualitative trend where the inner region red sequence is undersampled, and the lower-mass, red-end of the distribution is oversampled. We used the nonnormalized, binned density ratio distribution to provide the scaling factors to the "ESWEIGHT" values. These scaling factors and their uncertainties are provided in the Swim_all catalog file. We do note that there is a significant number of bins that the SwiM_v4.1 catalog does not cover, and caution users to only use scaling factors when the galaxies of interest lie within bins populated by SwiM_v4.1.

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The criteria for inclusion in the SwiM_v4.1 catalog included the availability of uvw1 and uvw2 observations of at least 100 s in duration with no foreground star contamination. As no further quality cuts were made, there are objects in the SwiM catalog that have relatively low S/N Swift images. We quantify this by providing the distributions of the Swift exposure times and the S/Ns of the integrated galaxy flux measurements in Figure 5. The integrated flux calculations are described in Section 5.1 above.

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A majority of the observations in all three Swift NUV filters are less than 5 ks, with the median exposure time provided in Table 1. As a result, the Swift integrated flux measurements for most of the galaxies have a relatively low S/N. In fact, ∼20% of the galaxies in our sample have S/N < 10 in at least one of the two wide-bandpass Swift NUV filters (uvw1 and uvw2). We provide more detailed statistics of the S/N for each filter in Table 4. While there is a wide range in the S/Ns between objects within the SwiM_v4.1 sample, we purposely do not exclude any objects in order to not bias the sample toward any specific type of object. We provide the integrated flux and inverse variance for all three Swift filters in the Swim_all catalog file, which is described in more detail in Section 5.1 and Appendix A.

While SwiM_v4.1 is by definition a subset of the MaNGA survey, our catalog also has significant overlap with other well-known data sets. In order to determine the overlap, we searched for cross-listings for all the galaxies in SwiM_v4.1 using the NASA/IPAC Extragalactic Database (NED) (2019) ¹⁸ and stored the results in the "NAME" column of the SwiM_all catalog file. Given the large number of galaxy catalogs, we focused our search on the following nine: the Two Micron All-Sky Survey (2MASS) Flat Galaxy Catalog (2MFGC; Mitronova et al. 2004), the Catalog of Galaxies and of Clusters of Galaxies (CGCG; Zwicky & Kowal 1968), the Index Catalog (IC; Dreyer 1910), the Kisosurvey for Ultraviolet-excess Galaxies (KUG; Takase & Miyauchi-Isobe 1985), the Morphological Catalog of Galaxies (MCG; Vorontsov-Vel'Yaminov & Arkhipova 1962), the Markaryan objects (Mrk; Markaryan 1967), the New General Catalog (NGC; Dreyer 1888), the Principal Galaxies Catalog (PGC; Paturel et al. 1989), and the Uppsala General Catalog of Galaxies (UGC; Nilson 1995). The main selection criterion for each of these catalogs is provided in Table 5. If an object was not in any catalog, we recorded NED's preferred object name. Meanwhile, if an object was cross-listed in more than one catalog, we preferentially select the NGC or UGC name, when available.

We present the overlap between SwiM_v4.1 and each of the nine catalogs listed above in Table 5. In total, 237/559, or approximately 42% of the galaxies in SwiM_v4.1 were cross-listed in at least one of the nine catalogs described above. Of those 237 objects, 173 or 73% are cross-listed in at least two catalogs.