LIGHTS. Survey Overview and a Search for Low Surface Brightness Satellite Galaxies

Galaxy outskirts, with their long dynamical times, retain the vestiges of their tumultuous formation history — a history that is central to the now-standard, hierarchical picture of galaxy formation at the heart of the $\Lambda$CDM model (Blumenthal et al., 1984; Davis et al., 1985); a history that results in testable predictions for the total stellar debris field (e.g., Font et al., 2011; Amorisco, 2017; Merritt et al., 2020), the phase-space signatures of accretion events (Bullock & Johnston, 2005; Johnston et al., 2008; Cooper et al., 2010; Martin et al., 2022), and the surviving population of gravitationally bound satellite galaxies (Mao et al., 2021, and references therein); and a history that is manifested most clearly in the deepest possible images of galaxy halos (surface brightnesses limits $>$ 30 mag arcsec^-2; Johnston et al., 2008).

The hierarchical structure formation scenario successfully reproduces a myriad of observations, and is arguably one of the triumphs of cosmology, but comparison at galaxy scales and below can be problematic (Weinberg et al., 2015). Interpreting the apparent failings is complicated by the complex details of the baryonic physics and its effects and by the unknown nature of dark matter. Nevertheless, a full accounting of the satellite mass function is now understood to provide a key test of the models, a possible path to learning more about both the effects of baryonic physics at these scales and the nature of dark matter, and motivation for numerous independent studies (e.g., Geha et al., 2017; Park et al., 2017; Xi et al., 2018; Park et al., 2019; Müller & Jerjen, 2020; Carlsten et al., 2021; Davis et al., 2021; Mao et al., 2021; Karunakaran et al., 2021; Santos-Santos et al., 2022; Nashimoto et al., 2022; Li et al., 2023; Goto et al., 2023).

Satellite galaxies represent the survivors of the accretion process, while the dispersed stars within the halos testify of the less fortunate. This interpretation is confirmed for the halo of our own galaxy, where $\gtrsim$ 95% of all halo stars outside of surviving satellite galaxies are traced back to specific progenitor satellite galaxies (Naidu et al., 2020). Among nearby galaxies there appears to be a wide variety in the amount of diffuse stellar debris that comprises the stellar halo (Sola et al., 2022), including some galaxies with almost no stellar halo (van Dokkum et al., 2014). Despite theoretical expectations of significant scatter in stellar halo properties (Cooper et al., 2010; Martin et al., 2022), the large variation observed may be in tension with the standard model (Merritt et al., 2016, 2020).

Our Large Binocular Telescope (LBT) Imaging of Galactic Halos and Tidal Structures (LIGHTS) survey is motivated by a desire to establish the range of stellar halo properties and test the basics of the galaxy formation scenario. We aim to provide the best possible measurements of the diffuse halo stellar light for as large a sample of nearby galaxies as possible. Our focus is therefore on reaching as faint a surface brightness limit as possible, while retaining the highest possible angular resolution with which to classify and mask contaminating sources. We introduced the survey in Trujillo et al. (2021) by presenting and discussing the data for our first galaxy (NGC 1042). We demonstrated there how LIGHTS images could impact our understanding of stellar halos.

Here we present our current sample of 25 galaxies and provide an overview of the sample selection and data processing, while also presenting results regarding the relation between the low surface brightness satellite populations and their hosts. In short order, we will also be presenting a determination of the point spread function in the images to extremely large angular radii (Sedighi et al. in prep.) which is essential in assessing the scattered light across the image, and the generation of object catalogs (Hosseini-Shahisavandi et al. in prep.). Both of these are critical components in making the survey as scientifically useful as possible. As stressed in our introductory paper, LIGHTS is a well-matched precursor to the Legacy Survey of Space and Time (LSST) to be completed by the Vera C. Rubin observatory in somewhat over a decade, and so also serves as a guide on what to expect from that tremendous resource.

As just mentioned, our focus here is on low surface brightness (LSB) satellite candidates (central surface brightness in the $g$-band, $\mu_{0,g}$ $>$ 24 mag arcsec^-2). We refer to our cataloged satellite galaxies as candidates because they are not yet confirmed to be at the same distance as the apparent host. The surface brightness boundary is artificial, but does roughly correspond to the types of systems that are now being found in large numbers but were previously seen as rare (e.g., van Dokkum et al., 2015; Koda et al., 2015). We adapt the methodology used by Zaritsky et al. (2019, 2021, 2022, 2023), which was specifically designed to identify LSB galaxies in Legacy Survey images (Dey et al., 2019) beyond the local flow field.

The resulting catalog from LIGHTS images provides information on the nature of the satellite galaxy population at the limits of surface brightness and total luminosity that can be probed currently from the ground in galaxies well outside the Local Group and it complements Local Group or nearby galaxy studies by providing measurements for a larger set of parent galaxies. These results will eventually be matched to those on the diffuse halo light for these same galaxies to provide a complete accounting of the stellar halos of galaxies.

In complementing our presentation here of the LIGHTS survey sample with some example results, we focus on two extremes of surface brightness properties. First, we discuss ultra-diffuse galaxies (UDGs), which are those LSB satellites that have effective radii larger than 1.5 kpc ($r_{e}>1.5$ kpc). These galaxies are of interest because they include some of the most massive of the LSB galaxy population (van Dokkum et al., 2017; Zaritsky, 2017). Our satellite candidate catalog enables tests of previous results that use different data, methodology, and parent galaxy type (e.g., Park et al., 2017; Carlsten et al., 2021; Poulain et al., 2021; Goto et al., 2023). For example, we will use our catalog to test the extrapolation of the relationship between the number of UDGs and parent host halo mass (van der Burg et al., 2016a; Román & Trujillo, 2017; Mancera Piña et al., 2019; Karunakaran & Zaritsky, 2023; Goto et al., 2023). Second, we discuss the incidence of high surface brightness central clusters (nuclear star clusters; NSCs) found in some LSB galaxies. The origin of NSCs remains in question (Neumayer et al., 2020) and their presence in LSB galaxies poses additional challenges to any formation scenario (Lambert et al., 2024). Here we explore if there is evidence for a dependence on the incidence of NSCs in satellite galaxies with parent galaxy morphological type.

This paper is structured as follows: in §2, we present and describe the LIGHTS sample and the LIGHTS data reduction procedure through to the generation of final image mosaics. In §3 we describe the modifications to the Zaritsky et al. (2023) pipeline, our recovery of LSB satellite galaxy candidates, and our estimation of the sample completeness. In §4 we present the basic properties of the candidate satellite sample, such as the number of satellites per parent and the number of UDGs. In §5 we compare the results to those of other studies. In §6 we discuss our results for the number of UDGs per host halo mass relative to the trend observed for more massive host halos and the subsample of nucleated LSB satellite candidates. Magnitudes are provided in the AB system (Oke, 1964; Oke & Gunn, 1983).

We base our pipeline for identifying LSB satellite galaxy candidates on that used for our processing of the Legacy Survey (Zaritsky et al., 2023), but major modifications are required for LIGHTS because of its much greater depth, larger file sizes and the availability of only $g$ and $r$ bands (the Legacy Survey also has $z$ band observations; Dey et al., 2019). The major steps for identifying potential LSB satellites in LIGHTS are: 1) image processing that produces a preliminary list of candidates; 2) visual confirmation of remaining candidates; 3) estimation of completeness using simulated sources; and 4) creation of the catalog.

This procedure is a significant departure from Zaritsky et al. (2023) where we also rejected candidates based on thresholds for cirrus contamination and screened survivors with a machine-learning classifier before visual confirmation. These steps are unnecessary in LIGHTS because target galaxies were already selected in regions of low extinction and its relatively small total footprint, as opposed to the $\sim$20,000 deg² of the Legacy Survey, allows for visual confirmation of all candidates.

Details of the Legacy Survey pipeline have been previously described and, other than brief summaries, we only address modifications here. Because of the reasons noted in §3, Step 3, we make two passes through the pipeline and the number of potential candidates surviving each step discussed below represents the final total after the two runs. Processing and analysis of calibrated LIGHTS images is performed using the facilities of the University of Arizona High Performance Computing center³³3public.confluence.arizona.edu/display/UAHPC/Resources.

We identify potential LSB satellite galaxy candidates using the following steps and summarize these in Table 3:

1. 1.

   Because of the large size of the LIGHTS mosaics, we start by binning them by a factor of four in each direction, which also increases the signal-to-noise ratio. While this increases the pixel scale from 0.224 arcsec pixel^-1 to 0.896 arcsec pixel^-1, the effective pixel size is still significantly smaller than our minimum allowable candidate satellite size (3.5 rebinned pixels or $r_{e}=3.2\arcsec$, which we find to be a coarse lower size limit on the resolution needed for reliable model fitting and classification). The LIGHTS pipeline flags saturated regions, bad pixels, and image regions outside of the observation area with NaN values. We replace these NaN values using the Python ASTROPY interpolate_replace_nans function.

2. 2.

   To limit the number of candidates requiring further processing in the Legacy Survey, Zaritsky et al. (2023) subtracted sources that were clearly too bright to be their intended targets. This step required masking out to the periphery of these bright objects. However, the significantly greater depth of the LIGHTS images results in a much higher density of such objects and we skip this step to avoid excessive masking.

3. 3.

   As described in detail by Zaritsky et al. (2019), the detection pipeline uses wavelet transforms with tailored filters to isolate candidates of different angular scales. Higher wavelet levels will preferentially accentuate objects of larger sizes. However, clusters of small objects can be detected across multiple wavelet levels. To limit the number of false detections when processing Legacy Survey images, Zaritsky et al. (2023) required that higher order wavelets have a peak value of at least 25% of lower order wavelets within its footprint. Because of the greater depth in LIGHTS, and the resulting larger number of overlying contaminants, we found that we initially missed some obvious LSB candidates when using this criterion and, therefore, chose to repeat our processing without this requirement. With this change, we find a total of 717,124 wavelet detections.

4. 4.

   We limit spurious detections by requiring that a potential candidate have coincident detections in both filter bands (defined as center-to-center separations $<$ 4 arcsec) with its resulting location defined as the midpoint of the two centers. This criterion results in the acceptance of 541,086 wavelet detections which are, after eliminating nearby duplicates (defined to be other detections within 4 arcsec), associated with 261,046 groups of detections needing further processing.

5. 5.

   The vast majority of detections surviving the previous steps are still not viable LSB satellite galaxy candidates and require further filtering. Rather than using time-consuming GALFIT modeling (Peng et al., 2002) at this point, we obtain rough parameter estimates by fitting an exponential Sérsic model ($n=1$) with the much faster LEASTSQ function from the Python SciPy library (Jones et al., 2001). Because this modeling is only used as a coarse selection screen, we require that results meet conservative parameter thresholds of $r_{e}\geq 2.7^{\prime\prime}$ and $\mu_{0}\geq$ 23.0 and 22.0 mag arcsec^-2 for $g$ and $r$ bands, respectively, to avoid prematurely rejecting good candidates. An important clarification, particularly with respect to our discussion of nucleated sources in §6.2, is that we mask bright regions in the center of each candidate when fitting models (see Zaritsky et al., 2023, for details). Despite this initial use of a fixed $n$, final samples from this procedure show no bias against higher $n$ objects at least up to $n=2$ (Zaritsky et al., 2021). A total of 211,104 detections comprising 153,552 distinct candidates survive this step.

6. 6.

   We now revisit the modeling using the combination of the $g$ and $r$ band images to reach greater depth, a slightly refined model, and additional selection criteria. We now do use GALFIT to model each candidate using a fixed Sérsic index, $n=1$, and again use generous acceptance thresholds of $r_{e}\geq 2.7\arcsec$, $b/a\geq 0.34$, and $\mu_{0,g}\geq$ 23 mag arcsec^-2 or $\mu_{0,r}\geq$ 22.2 mag arcsec^-2 if there is no available measurement of $\mu_{0,g}$. Once again the values are set to avoid prematurely rejecting acceptable candidates. A total of 27,209 candidates meet these criteria.

7. 7.

   In our final image processing step, we model the remaining candidates using GALFIT with a variable Sérsic index and an estimate of the PSF derived from the FWHM parameter given in Table 2 for both $g$ and $r$-band images. To avoid having a nuclear star cluster distort the fit, we mask the center if those pixels are a factor of two brighter than our central surface brightness limits (24 and 23.6 mag arcsec^-2 for $g$ and $r$, respectively). After applying our final, tighter LSB candidate criteria of $r_{e}\geq 3.2\arcsec$, $\mu_{0,g}\geq$ 24 mag arcsec^-2 (or $\mu_{0,r}\geq$ 23 mag arcsec^-2 if GALFIT failed to model the $g$-band image), $b/a\geq$ 0.37, and $n<2$ we are left with a total of 6044 candidates requiring further evaluation. These parameter choices mirror those in Zaritsky et al. (2023) and are discussed in more detail there.

8. 8.

   The remaining candidates are visually reviewed by DZ and RD with each candidate labeled as a potential LSB satellite or a false positive. Those with disagreements are classified again by both reviewers. This procedure results in both reviewers labeling 62 as LSB candidates with the remainder classified as a false positive by at least one of the reviewers. To minimize the number of false candidates in our final list, we consider any disagreements between the two reviewers to be false positive (5 objects are in this category).

Visual classification, in fact any type of morphological classification, has the potential to introduce unintended biases in the properties of the final sample. We attempt to reject only objects that are evidently not galaxies (tidal tails, merged groups of background galaxies, image artifacts) but biases could nevertheless creep in. In our comparison of the SMUDGes catalog with other catalogs (Zaritsky et al., 2021, 2023) and our comparison here to independent work (§5) we do not identify any significant classification disagreements among galaxies in common that satisfy the stated selection criteria of either survey, suggesting that we are not introducing any peculiar biases with our visual classification, although it is possible that all surveys share similar biases.

We identify 62 low-surface brightness ($\mu_{0,g}>24$ mag arcsec^-2) candidate satellites around 25 target galaxies (Table 1). We reject eight of these that upon further review we consider to be background objects (Figure 3). In some cases, these appear to have spiral structures, and are therefore likely to be background disk galaxies. In others, they are comprised of a set of small, distinct sources that are likely to be distant galaxy groups or clusters (e.g., Gonzalez et al., 2001). These are among the smallest, highest surface brightness galaxies in our sample, which is the parameter range where one would expect to find background objects. We present the remaining 54 candidate satellites in Figure 4 and Table 4. Uncertainties are not included because the internally estimated errors are underestimates. Improved estimates can be obtained from simulations (as done in Zaritsky et al., 2023), but uncertainties in the physical parameters, which are of primary interest here, are dominated by distance uncertainties, which are given in Table 1.

The number of low-surface brightness satellites candidates per parent galaxy (Table 1) varies between 0 and 7 and is reasonably reproduced by a Poisson distribution with mean 2.2 (54/25) that integrates up to 25 systems (Figure 5). The sample size is small however and there is a hint that there are more than the expected number of systems with $\geq$ 5 satellites. Perhaps these systems are not a population of bound satellite galaxies, but rather belong either to a clustered background population or to a rich local environment if the associated parent galaxies are themselves members of a group. In two cases (NGC 1042 and NGC 4307) the larger N_LSB is matched with a large N_LEDA, suggesting that these are indeed found within group environments. Removing these systems from consideration, we find that the mean number of LSB satellite candidates per parent drops to 1.8 (42/23).

A closer inspection of the numbers, however, must include consideration that our sample spans a range of distances, host galaxy stellar masses, and environments. We expect the first to affect the radial range over which we identify satellite candidates and the physical size distribution of the candidates, and the second, assuming a connection between stellar mass and total mass, to be reflected in the numbers of satellites. The third we have attempted to mitigate by not considering targets in dense environments.

The FOV of the nearest target with the smallest FOV (NGC 3351, D = 10 Mpc, FOV = 42.7 arcmin on a side), results in a maximum radius to which we are potentially complete in our satellite search of 62 kpc. Therefore, to compare the numbers of candidate satellites among galaxies fairly, we can either limit all fields to this radius or correct for the incomplete sampling for some targets at larger radii. Considering only the candidates found at R${}_{proj}<60$ kpc, we do find a correlation between the host galaxy magnitude and the number of satellite candidates in the expected sense, but the statistical significance is at roughly only the 1$\sigma$ level. The average number of satellite candidates within 60 kpc per host in our catalog is 0.84.

Alternatively, we can probe out to a larger R_proj and correct for those fields that do not reach these radii. In this approach, we select a radius that many of our fields reach, 100 kpc, and then simply weight the results at the larger radii by the inverse of the fraction of images that sample to that radius⁴⁴4This correction does not address the loss of physically smaller, lower luminosity, satellites of hosts at larger distance due to our angular size cut. Correcting for this effect requires knowledge of the satellite size and luminosity functions that we do not have.. We refer to this as the radial completeness correction to differentiate from the photometric completeness discussed in §3.1. When we apply both the radial and photometric corrections, we find that each host galaxy has 4 satellite candidates out to 100 kpc. Without applying the radial correction we find that each host has 3.6 candidates to this projected radius. The modest change in numbers after we apply the radial completeness correction reflects that situation that most of our fields do sample out to R${}_{proj}=100$ kpc.

Certain properties of the satellites themselves are provided in Figure 6. In the upper panel we present the radial distribution of satellites after applying both the photometric and radial completeness corrections. For intuition, note that if the 3-D density of satellites was proportional to $r^{-2}$ (as it would be for isothermal sphere distribution) then the number of satellites per radial bin would remain constant. We find no signs of a significant trend with radius (at R${}_{proj}>100$ kpc the results are highly uncertain because only a few of our images sample that radial range).

In the middle panel, we present the central surface brightness distribution, which slightly favors brighter central surface brightnesses among the detected candidates, but extends to $\mu_{0,g}\sim 28$ mag arcsec^-2. With the photometric completeness correction applied, we find a nearly flat distribution in central surface brightness, with, at most, an allowed increase in numbers of roughly a factor of two from our bright central surface brightness limit to our faint one. We conclude that there is no vast reservoir of such undiscovered galaxies down to at least $\mu_{0,g}\sim 28$ mag arcsec^-2.

Finally, the third panel in Figure 6 shows that the distribution in luminosity for these low surface brightness candidate satellites is nearly constant between $-13<$M${}_{r}/$mag$<-9$ for the detected sources. Again, the situation changes slightly after applying the photometric completeness corrections, suggesting a moderate rise in the numbers of smaller, fainter LSB satellites across the parameter range explored here.

In summary, after applying photometric and radial completeness corrections and removing the two systems that we suspect of being contaminated with group members, we find an average of 4 LSB satellites ($24<\mu_{0,g}/{\rm mag\ arcsec}^{-2}<28$ and $3.2<r_{e}/{\rm arcsec}<23$) per target galaxy out to a projected radius of 100 kpc and conclude that the number of satellites at the lower luminosities and surface brightness is likely to be larger still, but only by a moderate amount.

We present the current status of the LIGHTS (LBT Imaging of Galactic Halos and Tidal Structures) survey, an overview of the data processing and quality, and a catalog of low surface brightness ($\mu_{0,g}>24$ mag arcsec^-2) satellite galaxy candidates.

Regarding the status, we now have deep $g$- and $r$-band imaging of 25 galaxies. The target galaxies are mostly morphologically late type and in the median about a magnitude fainter than our Milky Way galaxy. They span a range of environments, although mostly low density ones, and were selected to be in low extinction regions of the sky and relatively uncontaminated by bright stars. The extinction and low stellar contamination are essential to reaching the lowest possible surface brightness. The sample will grow slightly with time, but is mostly complete. The depth of the imaging will rival the full 10-year depth of the LSST, and so provides excellent guidance on what can be expected to become available across a large fraction of the observable sky.

We describe the data reduction process that enables us to consistently reach a 3$\sigma$ $r$-band surface brightness in areas equivalent to 10 arcsec boxes $\sim$ 30.5 mag arcsec^-2 across the sample. In particular, we rely on an iterative masking and background modeling used to achieve the final sensitivity and state-of-the-art object extraction software in Gnuastro.

We make slight modifications to the low surface brightness object detection procedure outlined by Zaritsky et al. (2019, 2021, 2022, 2023) to generate a catalog of 54 low surface brightness satellite galaxy candidates. These are defined to have $\mu_{0,g}>24$ mag arcsec^-2 and $r_{e}>3.2$ arcsec. Detections extend nearly to $\mu_{0,g}=28$ mag arcsec^-2, which is $\sim$ 2 magnitudes fainter than the same procedure reached on Legacy Survey images (Dey et al., 2019). We use simulated sources to derive completeness estimates across the parameter range covered by our detected sources. These candidates are mostly confined to within 100 kpc projected radius from their parent galaxy due to the image size.

We expect, after applying the completeness corrections, that satellites of even lower total luminosity ($M_{r}>-10$ mag) and lower central surface brightness ($\mu_{0,r}>27.5$ mag arcsec^-2) to be more prevalent. Over the parameter range we explore, each host (excluding those that are in overdense regions, apparently groups) has nearly 4 LSB satellites to a projected radius of 100 kpc. These objects are mostly just at or beyond the reach of spectroscopic surveys of LSB galaxies (e.g., Kadowaki et al., 2021) unless they are H I rich or have ongoing star formation. However, they are sufficiently close to us, if truly associated with their host galaxies, that other distance estimators utilizing resolved stars or the globular cluster luminosity function could be exploited with space-based observatories. For the fainter, smaller satellites that might have fewer luminous stars or globular clusters even these methods may fail.

In our catalog, there are 3, possibly 4, ultra-diffuse galaxies (UDGs; $r_{e}>1.5$ kpc). This allows us to explore and possibly extend the relationship between the number of UDGs and the host halo mass found for more massive systems (van der Burg et al., 2016b; Román & Trujillo, 2017; Mancera Piña et al., 2019; Karunakaran & Zaritsky, 2023; Goto et al., 2023). Despite differences in sample selection, we find that our results are in agreement with the extrapolation of the mean relationship used by Karunakaran & Zaritsky (2023) to galaxies that are a magnitude fainter than the Milky Way.

We visually identify 12 galaxies in our catalog that host a nuclear star cluster (NSC). The occupation fraction for the sample (12/54) is in excellent agreement with that found among satellites of early type galaxies (Poulain et al., 2021) and so suggests that the morphological type of the parent galaxy plays at most a limited role in the NSC satellite occupation fraction.

The LIGHTS sample provides exquisite data for the exploration of the halo stellar populations, whether they be diffusely distributed, in tidal features, or, as described here, still part of a low surface brightness satellite galaxy. Comparison with state-of-the-art published studies shows that LIGHTS matches or supersedes the sensitivity of those and/or provides data for a different category of parent galaxy. It is a resource for current study, but also provides direct guidance on the potential of upcoming deep imaging surveys such as the LSST that will be carried out using the Rubin telescope.