X-Ray Detected AGN in SDSS Dwarf Galaxies

MNRAS 000,1–19 (2020) Preprint 29 January 2020 Compiled using MNRAS LATEX style ﬁle v3.0

X-ray detected AGN in SDSS Dwarf Galaxies

Keir L. Birchall,? M. G. Watson, and J. Aird Department of Physics & Astronomy, University of Leicester, University Road, Leicester LE1 7RH, UK

Accepted XXX. Received YYY; in original form ZZZ

ABSTRACT In this work we present a robust quantification of X-ray selected AGN in local (z ≤ 9 0.25) dwarf galaxies (M∗ ≤ 3 × 10 M ). We define a parent sample of 4,331 dwarf galaxies found within the footprint of both the MPA-JHU galaxy catalogue (based on SDSS DR8) and 3XMM DR7, performed a careful review of the data to remove misidentifications and produced a sample of 61 dwarf galaxies that exhibit nuclear X-ray activity indicative of an AGN. We examine the optical emission line ratios of our X-ray selected sample and find that optical AGN diagnostics fail to identify 85% of the sources. We then calculated the growth rates of the black holes powering our AGN in terms of their specific accretion rates (∝ LX/M∗, an approximate tracer of the Eddington ratio). Within our observed sample, we found a wide range of specific accretion rates. After correcting the observed sample for the varying sensitivity of 3XMM, we found further evidence for a wide range of X-ray luminosities and specific accretion rates, described by a power law. Using this corrected AGN sample we also define an AGN fraction describing their relative incidence within the parent sample. We found the AGN fraction increases with host galaxy mass (up to ≈ 6%) for galaxies with X-ray luminosities between 1039erg/s and 1042erg/s, and by extrapolating the power law to higher luminosities, we found evidence to suggest the fraction of luminous AGN 42.4 (LX ≥ 10 erg/s) is constant out to z ≈ 0.7. Key words: galaxies: active – galaxies: dwarf – X-rays: galaxies

1 INTRODUCTION III stars. After collapsing into stellar mass black hole seeds (Madau & Rees 2001) they could have undergone a series Supermassive black holes (SMBHs) have been known to of intermittent super-Eddington accretion episodes (Madau exist at the centre of massive galaxies with a bulge for some et al. 2014) or merged with other seeds to form more time (Kormendy & Ho 2013) however as one goes down massive black holes (Miller & Davies 2012). Ineﬃcient gas the mass scale, the ubiquity of such objects becomes more cooling can also prevent star formation from occurring, thus uncertain. allowing black holes to form from the the direct collapse of a proto-galactic dust cloud (Begelman et al. 2006). Crucially, Quantifying the fraction of dwarf galaxies with an however the low angular momentum gas required for this active galactic nucleus (AGN) allows a lower limit to be method of formation only existed in suﬃcient quantities established on the fraction that contain black holes. Some in the early Universe. As disk-like structures began to dwarf galaxies have much lower metallicity relative to their

arXiv:2001.03135v3 [astro-ph.GA] 28 Jan 2020 form, the gas within would gain angular momentum and high mass counterparts and have not tidally interacted become less likely to collapse and form a black hole. Thus with their neighbours meaning they can be considered if direct collapse were the dominant seeding mechanism an analogue for galaxies in the high redshift Universe then the fraction of dwarf galaxies hosting black holes is (Bellovary et al. 2011). Thus we can use information about expected to be much lower than if the Population III stellar their AGN content to provide insights into the possible seeding mechanism was dominant (Greene 2012). For a mechanisms that form the seeds of supermassive black more in-depth review of black hole formation mechanisms holes in the the very early universe. Black hole formation see the review by Latif & Ferrara(2016). from stellar collapse is possible at all redshifts but the early universe contained low metallicity gas in abundance which facilitated the growth of more massive Population Black holes can be identiﬁed within massive galaxies using dynamical methods, however current technological limits mean these methods are diﬃcult to apply at lower galactic ? E-mail: [email protected] masses. Consequently, more indirect approaches have to be

© 2020 The Authors 2 K. L. Birchall et al. adopted. Identifying the presence of AGN in dwarf galaxies position matched to serendipitous sources found in 3XMM has become an area of growing interest to assess the black (Rosen et al. 2016) and followed up with a visual assessment. hole population in dwarf galaxies. There is increasing evidence, across a range of wavelengths to show that at least a small number of AGN do exist within this mass range 2.1 MPA-JHU (see review by Reines & Comastri(2016)). Some of the first large-scale studies into this area focused on optical emis- Optical photometry and spectroscopy covering 9274 deg2 sion (Reines et al. 2013) identifying black holes through a of the sky can be found in the Sloan Digital Sky Survey combination of identifying broad line emission and measur- Data Release 8 (SDSS DR8). The MPA-JHU catalogue pro- ing line strength. However, the effectiveness of this method vides derived estimates of galaxy properties such as stellar can be limited by obscuration from dust or star formation mass, star formation rate (SFR) and emission line fluxes, for signatures. 1,472,583 objects in this release. AGN emission in the X-ray band generally dominates Stellar masses are provided by the MPA-JHU catalogue over other sources and is less easily obscured. A number whose method is based on that described in Kauffmann of studies have had success in using this emission to et al.(2003a).The MPA-JHU catalogue analysis uses identify the presence of AGN in dwarf galaxies (e.g. Reines template spectra made from a linear combination of single et al.(2011); Lemons et al.(2015); Pardo et al.(2016); stellar population models generated using the Bruzual & Paggi et al.(2016); Mezcua et al.(2016); Mezcua et al. Charlot(2003) code. These models consider 10 possible (2018)). However, the techniques employed are not free ages - from 0.005 to 10 Gyr - and 4 possible metallicites from problems. Lower mass galaxies, like the ones being - from 0.25 to 2.4 Z . They model galaxies as a single studied, tend to host lower mass central black holes hence metallicity population with the chosen model being the less luminous emission is expected. This difficulty can also one that yields the minimum χ2. They then subtract this be compounded if the AGN being studied are very weakly from the observed spectrum and the remaining emission accreting. The biggest challenge for studies like these is to lines are modelled as Gaussians. Rather than simply taking rule out emission from other potential X-ray sources. the model with the best χ2, it employs a Monte-Carlo fitting technique and produces a probability distribution This paper presents one of the first large-scale and ro- for each observed and subsequently calculated property; bust quantification of AGN in local dwarf galaxies. Our work the most likely value being the median of the distribution. uses an unbiased sample, being based on the XMM-Newton Given the size of the SDSS fibre aperture, the spectral Serendipitous X-ray Survey and the SDSS which covers a flux measurements required by Kauffmann et al.(2003a) large sky area. First we describe the construction of such would be dominated by light from the galactic centre if a sample of local (z ≤ 0.25) dwarf galaxies that possibly based on SDSS spectroscopy. Instead, the ugriz photometry host AGN (section2). Next we analyse the properties of from the full extent of the galaxy is used, and the total that emission to refine our sample to the most likely hosts stellar mass is calculated by fitting to model magnitudes. (section3). To better understand the AGN identified this A Kroupa(2001) initial mass function is assumed. For way and the environments they inhabit we perform a se- this study we define a dwarf galaxy as having a stellar 9 ries of follow-up measurements. We compare the effective- mass M∗ ≤ 3 × 10 M . Throughout, the median of the ness of optical identification methods to see if they cor- mass probability distribution from the MPA-JHU catalogue rectly identify our X-ray selected AGN hosts (section4). is used as our mass value. Applying our dwarf galaxy Next, to gain more of an insight into the environment of threshold to this mass entry returns 65,461 galaxies. our AGN, we then calculate the accretion rates of their central SMBHs (section5). Finally, we characterise the variable MPA-JHU only consists of objects spectroscopically sensitivity limits of 3XMM, from which we can better under- classified as galaxies by the SDSS - those with absorption −1 stand the underlying distribution of AGN in dwarf galaxies lines or emission lines with widths < 200 km s−1Mpc . To (section6). This allows us to investigate the population’s check if objects identified as broad-line AGN are being AGN fraction as a function of host galaxy mass and redshift excluded from our dwarf galaxy sample we also searched the (section7). Throughout, we assume Friedman-Robertson- Shen et al.(2011) SDSS DR7 quasar catalogue. It provides Walker cosmology in this paper with Ω = 0.3, Λ = 0.7 and estimates of the central black hole masses using virial −1 −1 H0 = 70 km s Mpc . techniques. Here, we adopt scaling relations to estimate the total stellar masses of the host galaxies of these quasars and thus determine if any would enter our dwarf galaxy sample. We adopt a fiducial scaling between BH mass 2 CONSTRUCTING THE DWARF GALAXY and bulge mass of MBH = 0.002Mbulge, and, conservatively, SAMPLE assume that the bulge mass corresponds to the total stellar mass of the host galaxy. Using our dwarf galaxy What follows is a description of the construction of a sample 6.8 threshold, we find an upper black hole mass of ≤ 10 M . of dwarf galaxies potentially hosting an AGN. First, a par- Reines & Volonteri(2015) relate a galaxy’s stellar mass ent galaxy sample is isolated from the MPA-JHU catalogue, 1 to the mass of central black hole in the following way, an SDSS value-added catalogue . Next, these objects are 11 log(MBH/M ) = 7.45 + 1.05 log(M∗/10 M ). This relationship was derived using a sample of AGN; their masses were calculated using either broad line widths, reverberation 1 Available at http://www.mpa-garching.mpg.de/SDSS/DR7/ mapping or dynamical methods. The Reines & Volonteri

MNRAS 000,1–19 (2020) AGN in Dwarf Galaxies 3

(2015) relationship predicts a upper black hole mass limit 5.9 of ≤ 10 M . Whichever relationship is used, the outcome is the same: none of the Shen et al.(2011) objects have black hole masses close to these values. Thus, MPA-JHU is the sole catalogue used in this paper.

In addition to the dwarf galaxy sample, we also deﬁne a sample of comparison galaxies whose mass limit 10 is M∗ > 1 × 10 M given the high incidence of AGN in higher mass galaxies. This high mass sample is only used as a comparison point to our dwarf galaxy sample from MPA-JHU and thus we do not add any broad-line objects from the Shen et al. catalogue.

As with the mass, SFR is measured using ﬁts to the spectral energy distribution based on photometry from the full extent of the galaxy taken from the SDSS and GALEX (UV; Martin et al.(2005)). MPA-JHU SFRs are calculated using the method outlined in Salim et al. (2007), who construct stellar population based on the Figure 1. Separation in arcseconds between the X-ray and op- Bruzual & Charlot(2003) population synthesis models and tical signals for the 86 dwarf galaxies that meet the matching assume a Chabrier(2003) IMF. Star formation histories criteria outlined in section 2.3. 84% of the signals in this sample were not single stellar populations, but the combination are matched within 5”. of an exponentially declining continuous star formation ∝ e−γt, with 0 ≤ γ ≤ 1 Gyr−1 uniformly distributed across this range, and with random starbursts superimposed. These bursts were constructed such that the occurrence of a single event over the past 2 Gyr is 50% and had a duration uniformly distributed in the 30-300 Myr range. Once constructed, each model is subjected to the Charlot & Fall(2000) dust attenuation model. The model SEDs at the redshift closest to the galaxy in question are, in turn, compared and their χ2 values evaluated. From this a probability distribution corresponding to a range of possible SFRs is produced. Throughout, the median of that SFR probability distribution is used as our SFR value.

Naturally, not all the observations are uniform in their quality so the sample had to be refined: 835,861 objects with a ”good” photometric reliability flag were selected. This in- dicated valid results from the photometric fits applied by the MPA-JHU team thus producing a usable mass measurement. However, before we can identify their AGN content the X-ray data must first be considered. Figure 2. Position-error-normalised separation distribution (dark blue) for the 86 dwarf galaxies that met the matching criteria and other match checks described in section 2.3. Poisson errors 2.2 XMM-Newton are assumed in the histogram bins. The Rayleigh distribution fit The X-ray data used in this study comes from the 3XMM (red) has a σ = 1.4 implying an underestimation in the XMM DR7 catalogue released in 2017 (Rosen et al. 2016). It is positional errors, a result that has been noted previously (Wat- son et al. 2009). A sample of high mass galaxies (light blue) has based on 9,710 pointed observations with the XMM-Newton undergone the same matching process and had its normalised sep- EPIC cameras which have a field of view ≈ 30’ and cover the aration scaled using the normalisation found from the Rayleigh energy range ∼ 0.2 − 12 keV. DR7 contains ∼ 400, 000 unique fit. X-ray sources based on 727,790 individual detections. Typi- cal position errors for DR7 are ≈ 1.5 arcsec. (1σ) and extend down to a flux limit of ≈ 10−15erg cm−2 s−1. 3XMM’s angular resolution depends on the instrument in use: the PN to the optical positions of the entire MPA-JHU catalogue detector has a FWHM of ∼ 6” and HEW of ∼ 16”; the MOS using a search radius of 10” around every X-ray object; this detectors have a FWHM of ∼ 5” and HEW of ∼ 15”. For yielded 3,440 matches. From this sample we summed fluxes our study we use the unique source list rather than the indi- in the 2 - 4.5 keV and 4.5 - 12 keV bands and converted vidual detections. Our results are thus averaged over several them to luminosities in the 2 - 12 keV energy range using individual observations for a significant number of sources. the MPA-JHU redshifts. Since these objects are at such a Using the data from this release, we performed a sky match low redshift, no rest-frame correction was applied.

MNRAS 000,1–19 (2020) 4 K. L. Birchall et al.

2.3 Identifying Candidate AGN-hosting Dwarf sources with the same criterion used to create the dwarf Galaxies galaxy sample. We then calculate the sky area covered by these sources assuming we perform radial searches centred Using these X-ray active objects, a sample of possible AGN- on the remaining 122,834 X-ray sources out to a radius of hosting dwarf galaxies could be found. First, a set of more 3.5 × ∆ . Multiplying the total search area, 0.91 deg2, by robust matching criteria were established to improve the X the uniform dwarf galaxy density, tells us to expect about conﬁdence of association between the optical and X-ray ob- 6 false matches. Given a lot of these X-ray sources will jects. For each dwarf galaxy/X-ray pair we calculated the already have well-deﬁned counterparts, this value represents position-error-normalised separation, x, as follows, a conservative upper limit on the false match number.

dO,X x = (1) As a final check of our position-error-normalised sepa- ∆X ration criterion, we plotted a distribution of this quantity where dO,X is the separation between the X-ray and for both samples of galaxies in figure2. The dark blue his- optical signals, and ∆X is the error in the X-ray position. togram shows the absolute values for the sample of 86 dwarf A dwarf galaxy was only considered a robust match if galaxies just identified, the light blue curve is the similarly- x < 3.5, giving a sample completeness of 99.8%. We also scaled high mass distribution. We perform a 1-D KS-test limited the extent of the X-ray source to less than 10” to compare this distribution to the expected distribution of to ensure point-like emission consistent with an AGN XMM errors, given by the Rayleigh distribution. The KS- −4 (Rosen et al. 2016). Applying this in conjunction with the test returned a p-value of 10 , indicating the observed dis- 9 M∗ ≤ 3 × 10 M mass threshold yields 101 possible hosts. tribution is not consistent with the expected Rayleigh distri- Applying the same matching criteria to the high mass bution, given the fiducial positional uncertainties provide in sample yields 2,237 matches. the 3XMM catalogue. To investigate whether the observed distribution was better described by a skewed Rayleigh func- By imposing the dwarf galaxy mass limit we could have tion, we fit it to this equation, removed higher mass objects with a smaller separation to 2 x −x the X-ray signal, resulting in a poorer dwarf galaxy match f (x) = N e (2σ2) (2) 2 being included. To check if any better matches to excluded σ objects exist, we uploaded the 101 X-ray co-ordinates to the where x is the position-error-normalised separation. The SDSS SciServer and compared them to the full SDSS DR8 to normalisation constant, N and σ value were both free and find their nearest neighbour. 11 X-ray signals were found to we assumed Poisson errors for the number of objects per have smaller separations to a higher mass galaxy removed by bin. Figure2 shows the results of this fitting: it was found imposing the dwarf galaxy mass limit during the matching that the skewed Rayleigh distribution (red) had a σ = 1.4, process. with a reduced χ2 of 1.90, implying an underestimate of A visual assessment of the dwarf galaxy sample was per- the X-ray errors. It is also clear that both the dwarf galaxy formed. The optical images from the SDSS Finding Charts and high mass distributions are consistent with this error identified 3 sources that were extremely off-nuclear. If the underestimation. The underestimation of errors is a known photometry was constructed correctly for these objects then issue in 3XMM, as noted in (Watson et al. 2009). Given the refinement process outlined should have removed them, these positional uncertainties, our position-error-normalised however this was not the case. We believed that in certain separation criterion functions correctly, thus we expect our instances a galaxy that should not have passed our mass cri- sources to be nuclear. terion was being broken into smaller sections and only having the MPA-JHU mass calculations applied to this small section. This was then matched to a nearby X-ray source, 3 ANALYSING DWARF GALAXY EMISSION fooling our identification criteria into thinking it was a valid target. These 3 sources were removed from our sample leav- Having identified 86 unique X-ray active dwarf galaxies by ing 87 dwarf galaxies with nuclear X-ray activity. This pho- the matching process detailed in section2, it is necessary tometric fragmentation process has been observed in other, to break down their emission properties to determine the similar studies such as Sartori et al.(2015). nature of the X-ray source. In this section, we discuss the A final assessment of the data found an object with analysis carried out on the X-ray emission produced by the extremely high redshift error. This was indicative of a poor dwarf galaxies in this sample. This analysis has also been ap- 10 fitting process leading us to doubt the accuracy of this plied to a sample of 2,237 galaxies with M∗ > 1 × 10 M as object’s data. It was removed leaving us with a final sample a comparison since these objects are more likely to harbour of 86 dwarf galaxies. Their distribution of separations AGN. between the X-ray and optical signals is shown in figure1: 84% of these dwarf galaxies match an X-ray object within 3.1 Sample Properties 5”, and the median matching radius is 2.2”. A sample of dwarf galaxy images, with their X-ray signals and position Figure3 shows the distribution of both samples of objects error overlaid, can be found in appendixA. Assuming a in mass and redshift. The dwarf galaxies, in dark blue, and uniform distribution of the 65,461 dwarf galaxies in the high mass sample, in light blue, are plotted over the full SDSS area (9274 deg2), we calculate a dwarf galaxy density sample of 3XMM X-ray detections within the SDSS area, of 7.05 deg−2. We then highlighted 131,736 X-ray sources in grey. Dwarf galaxies are found out to z = 0.25 and span a 6 9 within the SDSS area, from which we removed extended mass range of 3.08 × 10 M to 2.92 × 10 M . Higher mass

MNRAS 000,1–19 (2020) AGN in Dwarf Galaxies 5

Figure 3. Mass against redshift for X-ray active galaxies (grey) from the MPA-JHU catalogue. Highlighted are the points that make up the dwarf galaxy (dark blue) and high mass (light blue) Figure 4. X-ray luminosity distribution for the dwarf galaxy samples. Section3 breaks down the X-ray emission properties of (dark blue) and high mass (light blue) samples. The high mass these samples. distribution has been arbitrarily scaled to allow for comparison.

10 12 objects span a mass range 1.01 × 10 M to 2.84 × 10 M (HMXB) contribution to the SFR and the redshift depen- and are likely to host more luminous objects so can be dence accounts for changes in metallicity and evolution of detected at redshifts up to z = 0.33. the XRB population. Using this relationship, we calculated the expected emission from every dwarf galaxy’s binary Figure4 shows the distribution of X-ray luminosities population, LXRB. A total of 76 galaxies were found to for both samples. The dwarf galaxies, in dark blue, have ob- have an observed luminosity that is greater than LXRB. served X-ray luminosities of between 2.43 × 1036 erg s−1 and Thus, there is a significant sample of X-ray emitting dwarf 5.35×1042 erg s−1; the modal group is between 5×1039 erg s−1 galaxies whose emission cannot be accounted for solely by and 1 × 1040 erg s−1. Our dwarf galaxies are significantly their XRB population. less luminous than their high mass counterparts, shown in the arbitrarily-scaled light blue distribution, as expected. Hot gas in the interstellar medium can also produce X- When identifying an AGN using only the X-ray luminosity, rays, which could also potentially account for some of the a threshold of 3 × 1042 erg s−1 is typically used; (Brandt & emission. Its contribution can be estimated using the Mineo Alexander 2015) this would classify only 3 dwarf galaxies as et al.(2012b) relation, containing an AGN. This criterion is clearly biased against ( ± ) × 38 ( −1) (4) the low luminosity AGN we expect in the dwarf galaxy sam- LGas = 8.3 0.1 10 SFR M yr ple so we must analyse the emission properties further. We calculated the expected emission from the hot gas using Mineo et al.(2012b) and added this to their LXRB. It was still useful to calculate this quantity despite the 3.2 X-ray Contamination relatively low magnitude of this relation as all significant alternative X-ray sources needed to be considered. However, Given most of the dwarf galaxy X-ray detections do not as expected all 76 objects which already have observed meet the suggested 3 × 1042 erg s−1 threshold, we need to emission exceeding L also exceed the sum of L and assess other aspects of the emission. One possible source of XRB XRB L . X-ray contamination is the combined emission of unresolved Gas X-ray binary (XRBs) populations within the galaxy. To esti- Before we accepted this sample of 76 objects as AGN mate the contribution these objects make to a galaxy’s X-ray hosts, a level of significance needed to be applied to allow emission, we used the relationship provided in Lehmer et al. for potential uncertainties in the observed values of the re- (2016) (see also Aird et al.(2017)). It takes into account a lationships used. For this reason, dwarf galaxies which met galaxy’s stellar mass (M ), SFR and redshift (z) and takes ∗ or exceeded the following X-ray excess criterion, the following form, LX,Obs γ δ ≥ L = α (1 + z) M∗ + β (1 + z) SFR (3) 3 (5) XRB 0 0 LXRB + LGas where log10(α0) = 29.37 ± 0.15, γ = 2.03 ± 0.60, where LX,Obs is the observed X-ray luminosity, were con- log10(β0) = 39.28 ± 0.03 and δ = 1.31 ± 0.13 for 2 - 10 sidered to have sufficiently excessive X-ray emission to po- keV. tentially host an AGN. Figure5 shows the results of these calculations. A total of 61 objects highlighted in dark blue The low-mass X-ray binary (LMXB) contribution is and red both have sufficiently excess emission to pass this correlated to the stellar mass, the high mass X-ray binary criterion, however, they have their SFR measured in differ-

MNRAS 000,1–19 (2020) 6 K. L. Birchall et al.

44 from an AGN, rather than stellar processes. Exhibits Excess X-ray Emission (Photometric SFR) Exhibits Excess X-ray Emission (H SFR) )

s 43 Figure6 shows the error-weighted distribution of / No Excess X-ray Emission g

r hardness ratios for both our X-ray detected dwarf galaxy e (

42 sample and high mass comparison sample before (dotted ) y

t line) and after (solid line) applying the X-ray excess i

s 41

o criterion in equation (5). By overlaying the distributions in n i this way we can see equation (5) is working as intended: m 40 u the emission in both distributions becomes, on average, L

y harder. The contour lines can be seen to shift towards more

a 39 r

- positive values as objects with softer emission are removed. X (

38 This eﬀect is particularly pronounced in the high mass 0 1

g distribution, where a distinct group of harder emission o l 37 objects had been isolated from the full high mass sample. Less strict attention was given to matching this sample 36 5 4 3 2 1 0 1 2 so the high mass distribution, in the right-hand panel of log10 (Star-formation Rate) (M /yr) figure6, is more diffuse than that of the dwarf galaxies. However, higher mass galaxies generally have a larger Figure 5. X-ray luminosity as a function of photometric star number of confirmed AGN, thus we know this distribution formation rate for the X-ray active dwarf galaxies. A total of 56 will share some of their spectral properties. To determine objects (blue) have observed X-ray emission which exceeds the whether the thresholded dwarf galaxy distribution shared criterion of equation (5): possessing X-ray emission three times any similarities with AGN spectra we performed two KS greater than the sum of LXRB, predicted X-ray emission from X- tests. Firstly, the thresholded dwarf galaxy distribution was ray binary stars, and LGas, predicted X-ray emission from hot gas. compared with the full high mass distribution but they 5 objects (red) are those which meet or exceed the excess X-ray were found to be inconsistent, with a 2D 2 sample KS test criterion but have their SFR calculated using the Kennicutt & −3 Evans(2012) formalism and Hα emission line. The grey points producing a p-value of 10 . When both the thresholded are those that did not meet the X-ray excess criterion. dwarf galaxy and thresholded high mass galaxies were compared, however, a 2D 2 sample KS test showed they are consistent at a 3σ confidence. Thus the emission from our ent ways. The 56 dark blue galaxies have SFRs calculated thresholded dwarf galaxy sample shows characteristics of using the MPA-JHU method outlined in section2. The 5 coming from AGN. red points, however, were flagged as having bad SFR fits by the MPA-JHU catalogue so instead we used the Kennicutt & Spectra alone are not definitive in determining the Evans(2012) formalism and the Hα emission line flux to give source of emission. XRBs can also produce hard spectra, an SFR. Given the Hα emission line can be contaminated by similar to an AGN. Plotting these spectral distributions AGN light, this SFR is a conservative upper limit. X-ray de- next to each other and performing the KS tests, however, tections that do not meet the criterion given by equation (5) highlights similarities between the dwarf galaxy and high are plotted in grey. A breakdown of the observed and calcu- mass distributions which suggest the dwarf galaxy emission lated properties for this sample of 61 galaxies can be found is dominated by AGN. in appendixB. We also calculated the same quantities and applied the same criterion to the high mass sample which reduced it to 1,316 objects. 4 BPT CLASSIFICATION AGN can also produce signatures detectable in the visible 3.3 Hardness Ratio part of the spectrum. The central accreting black hole The nature of the emission will also affect the shape of a ionises the surrounding gas causing various emission lines galaxy’s X-ray spectrum; this feature can be probed by mea- to come to prominence in the galaxy’s spectrum. Other suring the hardness ratio (HR). HRs are defined between -1 processes such as star formation can also ionise a galaxy’s and 1, with more positive values indicating harder X-ray gas and dust but the associated radiation is much softer emission, likely from an AGN, and more negative values in- than from an AGN. The BPT diagram plots a pair of optical emission line ratios - [OIII] λ5007 and [NII] λ6583 - against dicating softer emission, likely from stellar processes. It is Hβ Hα calculated using the counts from two different energy bands, each other to try and distinguish the source of this ionising A and B, radiation (Baldwin et al. 1981). Given the diagnostic’s popularity in the field, we investigate whether the 61 AGN Band A − Band B HR = (6) hosts we identified using X-ray selection techniques would Band A + Band B also be found by the BPT diagram. Of the 61 AGN hosts in Line Flux We compare two HR bands: the soft band is the mean HR our sample, 53 had significant detections ( Line Flux Error > 3) of the 0.5 - 1.0 keV and 1.0 to 2.0 keV bands; the hard in each of the required emission lines so these were used in band is the mean HR of the 1.0 to 2.0 keV and 2.0 - 4.5 keV our analysis. bands. Analysing this quantity can help indicate whether the X-ray emission from our dwarf galaxy sample is coming The results of the BPT analysis are shown in figure

MNRAS 000,1–19 (2020) AGN in Dwarf Galaxies 7

0.8 0.35 1.5 1.5 Dwarf Galaxy Sample 0.7 High Mass Sample 0.30 1.0 1.0 0.6 0.25 0.5 0.5 0.5 0.20 0.0 0.4 0.0 0.15 0.5 0.3 0.5 Hard Band HR Hard Band HR 0.10 1.0 0.2 1.0

0.1 0.05 1.5 1.5 1 0 1 1 0 1 Soft Band HR 0.0 Soft Band HR

Figure 6. Error-weighted hardness ratios (HR) distribution for dwarf galaxy and high mass samples before (dotted line) and after (solid line) the application of the X-ray excess criterion in equation (5). The soft band is the mean hardness ratio of the 0.5 - 1.0 keV and 1.0 to 2.0 keV bands; the hard band is the mean hardness ratio of the 1.0 to 2.0 keV band and 2.0 - 4.5 keV bands. Applying the X-ray excess criterion shows a positive shift in HRs, indicative of possible AGN activity. Encouragingly, both samples display similarities in the shape of their spectral distribution. The plots have been smoothed using a Gaussian kernel, whose width corresponds to the sample size and dimensions of the data: ∼ 0.5 for the dwarf galaxies and ∼ 0.3 for the high mass galaxies. In addition, the points are weighted by their errors. The contours encompass 80%, 60%, 40% and 20% of the samples in each plot.

7 with our AGN hosts plotted as large, coloured points in sion to calculate the AGN’s contribution to observed optical the foreground. Black lines separate the AGN hosts into light. Lusso & Risaliti(2016) published the αOX relation different classifications: objects with ionisation signatures which has the form, predominately from AGN lie in the top-right, those domi- 1 nated by star formation in the bottom-left, and those that log10 (Lν, 2500A) = (log10 (Lν, 2 keV) − 7) (7) have a composite spectra are in the central region (Kewley 0.6 et al. 2001; Kauffmann et al. 2003b). Underneath these It relates the luminosity density at 2 keV, Lν, 2 keV to that at Û points, the BPT classification for the 62,703 galaxies in 2500A, Lν, 2500A, from a sample of SDSS quasars. Assuming 9 MPA-JHU with M∗ ≤ 3 × 10 M and z ≤ 0.25 are plotted our dwarf galaxies follow the same relation it can provide a in light grey. They are dominated by star-forming objects. useful starting point in our efforts to estimate the effects of The dark grey points show local galaxies in MPA-JHU with the AGN on optical observations. an X-ray counterpart within 10”. These are much more To find the luminosity density at 2 keV, we calculated spread out but will represent a range of objects including the geometric means of 3XMM bands 2 & 3 and bands 4 high mass AGN-hosting galaxies. & 5, giving the luminosity densities at 1 keV and 5 keV respectively. Using linear interpolation between these values Figure7 clearly shows that only 8 AGN hosts in we calculated Lν, 2 keV and used it in equation (7) to find our sample have been classified as an AGN by the BPT Lν, 2500A. Translating Lν, 2500A to the required emission at diagnostic, 1 has been classified as a composite and 44 3450AÛ required the composite UV-optical quasar spectrum have been classified as star-forming. Like other methods from Vanden Berk et al.(2001). They model this region as −α for identifying AGN, the BPT diagnostic has difficulty a power law spectrum, fν = cν , where α = 0.44. This finding AGN in certain environments. Moran et al.(2002) required Lν, 2500A to be converted to flux density, fν, 2500A notes that blue, star-forming galaxies present a challenging which was used, first, to scale the power for each AGN host. environment for the BPT diagnostic. The large amounts of From this an average spectrum was constructed and the flux star formation in these galaxies dominates the spectrum density at 3450 AÛ extracted. and hides emission from the lower luminosity AGN we The galaxy emission was more straightforward to find, expect to find. we simply converted the SDSS U-band magnitude to a flux density at 3450AÛ . We then divided the predicted AGN To investigate whether star formation dominates op- emission at 3450AÛ by that from the galaxy and split each tical emission in our AGN hosts we attempted to isolate object into groups based on this value. These dictate the emission from stellar and AGN processes at a single wave- colours used for the AGN host points in figure7. length, 3450AÛ - the central wavelength of the SDSS u-band wavelength - to compare their magnitudes. A total of 50 AGN hosts have predicted optical AGN First, we consider the AGN emission. To ensure little contributions less than that from galaxy emission - 44 have contamination from stellar processes we used the X-ray emis- AGN contributions > 25% of the galaxy - the majority of

MNRAS 000,1–19 (2020) 8 K. L. Birchall et al.

5 SPECIFIC BLACK HOLE ACCRETION 1 10 RATE To investigate the activity of the central black holes powering our AGN we calculate their growth rates in terms of the speciﬁc black hole accretion rate (sBHAR), λ . This 0 sBHAR 10 compares the bolometric AGN luminosity of the galaxy with H /

] an estimate of the black hole’s Eddington luminosity to give I I I an indication of how eﬃciently the black hole is accreting. O

[ L , 3540A(AGN/Galaxy) > 10 It is found using, 10 1 < 10 - 1 < 1 - 0.75 25L2−10keV Lbol < 0.75 - 0.5 λsBHAR = ≈ (8) < 0.5 - 0.25 1.26 × 1038 × 0.002M∗ LEdd < 0.25 - 0.125 < 0.125 - 0.0625 < 0.0625 - 0.03125 and is taken from Aird et al.(2012). We assume that the < 0.03125 black hole and stellar masses scale in the same way as their 10 2 10 2 10 1 100 101 higher mass counterparts to get a sense of the black hole [NII]/H growth relative to the total galaxy mass. In order to clarify the validity of this assumption, we would need to compare this scaling relation to black hole masses but these are very Figure 7. BPT diagram for various subsets of the MPA-JHU. difficult to accurately ascertain. Instead we looked at the The large, coloured points highlight the 53 dwarf galaxies iden- morphologies of our AGN hosts to see if they are bulge dom- tified as likely to contain an AGN with significant emission line detections. Their colour indicates the size of predicted optical inated. Of the 36 AGN hosts found in the Galaxy Zoo DR1 AGN luminosity compared to that coming from the galaxy, both (Lintott et al. 2011), 33 have uncertain morphology and the at 3450AÛ - see section4 for details on how this was calculated. remaining 3 are likely spirals. Despite this, we chose to cau- The smaller, background points are defined as follows: the light tiously continue using the relationship and present the re- 9 grey points show all the low mass objects (M∗ ≤ 3×10 M ) found sults in figure8. It plots the mass of a particular host against within the MPA-JHU highlighting a large distribution of points its observed X-ray luminosity, the colour gives an indication along the star-forming track; the dark grey points are the ob- of the sBHAR. We have also plotted lines of constant sB- jects in the MPA-JHU that have an X-ray counterpart, largely HAR to give an idea of the typical mass and luminosity dominated by the high mass AGN-like objects. expected from these galaxies. The most common environment in our sample is a host 9−9.5 of mass ≈ 10 M with an SMBH accreting at about 0.1% of its Eddington luminosity. None of the SMBHs are accreting very efficiently; most of this sample have an sB- HAR of less than 1% of their Eddington luminosity with the most frequent accretion rate being around 0.1%, with only one rising above 10%. In the high mass region we see a which lie in the star-forming region. This helps confirm the wide range of sBHARs which is restricted as we move down idea that the AGN emission in this part of the spectrum the mass scale. This effect is likely due to the fact that we is being hidden by star formation processes, causing the are missing objects with lower sBHAR at low masses as less BPT diagnostic to mis-classify them. Of the 2 remaining efficiently accreting hosts will be more difficult to observe AGN hosts with an optical AGN excess, only one lies in the (due to their extremely low X-ray luminosities), and likely AGN region. This host has the largest optical AGN excess won’t appear on the plot because of the threshold applied in the sample, over 10 times that of the galaxy emission. in equation (5). The other lies within the star-forming region because it has a much smaller excess, only 1.08 times that of the galaxy. This is likely insufficient to produce emission lines of the 6 COMPLETENESS-CORRECTED appropriate proportions to move it into the AGN region. LUMINOSITY AND ACCRETION RATE This further suggests that the BPT diagnostic is biased DISTRIBUTIONS against identifying the low-luminosity AGN we expect to see in dwarf galaxies. Currently our sample is subject to significant observational biases as we preferentially identify the most luminous Some studies have shown that the Lusso & Risaliti and actively accreting AGN, this gives us a skewed (2016) relation has an increased dispersion in the regime picture of the distribution of AGN in the wider galaxy of dwarf galaxies. In addition, these galaxies may be X-ray population. In this section we attempt to correct these weak relative to their UV emission, so these results could biases to try and understand how the probability of a under-predict the AGN contribution (Plotkin et al. 2016; galaxy hosting an AGN as a function of luminosity and Baldassare et al. 2017). Despite this, we have still identified accretion rate varies across the full dwarf galaxy population. a significant number of X-ray selected AGN mis-identified as star-forming galaxies. This finding is consistent with a in- Firstly, we made sure that our sample of AGN were creasing body of work suggesting that optical spectroscopic consistent with the bulk of the underlying galaxy popula- measurements are insufficient to identify AGN in these en- tion. Figure3 shows how the shape of the underlying galaxy vironments (Agostino & Salim 2018; Cann et al. 2019). distribution changes with increasing mass and that some of

MNRAS 000,1–19 (2020) AGN in Dwarf Galaxies 9

ordinates of 4,331 dwarf galaxies - our parent sample. Using 43 sBHAR > 0.1 the dwarf galaxy’s redshift, this ﬂux limit could be turned

) < 0.1 - 0.05 s

/ < 0.05 - 0.01 into a luminosity. Once all the luminosity upper limits were

g 42 < 0.01 - 0.005 r recorded, they were converted into a cumulative histogram

e < 0.005 - 0.001 (

< 0.001 - 0.0005 ) as a function of X-ray luminosity and normalised by the size

y 41 < 0.0005 t

i of the parent sample. This gave us a distribution of the frac- s o 0.1 tion of galaxies where an AGN could have been detected n = 40 R

i A sBH

m above a given X-ray luminosity, a distribution which will be

u 1 0.0

L = 39 R referred to as the luminosity sensitivity function. We can use A sBH y

a this information to correct the observed distributions of lu- 01 R 0.0 - = 38 AR minosities and account for the varying sensitivity of 3XMM, BH X s (

0 allowing us to recover estimates of the true underlying distri- 001 1 .0 37 = 0 g AR sBH bution of luminosities within our samples of dwarf galaxies. o l To produce the probability distribution, the observed 36 AGN in each mass interval were binned as a function of 6.5 7.0 7.5 8.0 8.5 9.0 9.5 10.0 the observed X-ray luminosity. For each luminosity bin, log10 (Stellar Mass) (M ) we use the sensitivity function to determine the number of galaxies within our parent sample where the 3XMM data is sufficiently sensitive to detect an AGN of this luminosity. Figure 8. Figure showing the X-ray luminosity against galaxy We then divided the total number of X-ray detections by mass for the 61 AGN hosts identified in section3. The colour gives an indication of the accretion rate for the central black hole the expected number of galaxies to provide an estimate the described in equation (8) true probability of finding an AGN with such luminosities. This process produced the probability distributions seen in . the left-hand column of figure9. They show the probability of finding an AGN within nearby dwarf galaxies, in each our observed AGN lie at a comparatively high redshift. To mass and redshift interval, as a function of the observed correct this we took galaxies of all masses in narrow bands X-ray luminosity. of redshifts from MPA-JHU and worked out the mass which contained ∼ 90% of the galaxies. Through this process we Correcting the statistical sample of AGN with its corre- defined a mass completeness limit as a function of redshift. sponding luminosity sensitivity function has removed some This completeness limit was applied to the observed AGN of the observational bias described earlier. In contrast to when we created 3 mass intervals: 9 − log10 (M∗/M ) < 9.5; the observed distribution shown in figure4, the probability 8 − log10 (M∗/M ) < 9; 6.8 − log10 (M∗/M ) < 8. As a result, of finding an AGN generally increases as we go to lower ob- our sample is also limited to z ≤ 0.06. served X-ray luminosities. Within each mass bin we can also In addition, our sample needs to be constrained so it see a similarly large spread of observed X-ray luminosities. only contains AGN with a statistically significant detection To produce the errors in the probability of hosting an in the energy bands used in the X-ray completeness analysis. AGN, we used the confidence limits equations presented in This analysis needs to be done to account for AGN that Gehrels(1986). With this we estimated the error on the may have been missed due to the varying sensitivity of number of AGN in a given luminosity bin and then propa- 3XMM. To do this we used Flix2 (Carrera et al. 2007), gated them as fractional errors to our probability estimates. 3XMM’s upper limits service. It provides an upper limit, Up until this point we have assumed all these objects flux estimate and associated error broken down by band are AGN, based on the low-mass & high mass X-ray binary and instrument for the whole of 3XMM. We use the upper checks applied to our sample in section3. Whilst the mod- limits and observed fluxes from band 8 in the PN camera; els used do consider the integrated X-ray emission from the not only does this band cover the entire energy range of galaxy up to around 1039erg s−1, an ultra luminous X-ray 3XMM but it also has the greatest number of AGN hosts object (ULX) located in the galactic centre and emitting that meet or exceed the 3XMM’s detection threshold. Thus significantly more than the rest of the galaxy could poten- we restricted our observed AGN sample to only those with tially have been included in this sample. To check whether or a detection likelihood > 6 in this band. Taking this and our not these objects had been included in our sample, we used mass and redshift corrections into account leaves us with the Mineo et al.(2012a) X-ray luminosity function (XLF). 28 AGN hosts in our statistical sample (these galaxies are It models the XRB populations, including ULXs, as a two- marked with an asterisk in appendixB). part power law normalised by the host galaxy’s SFR. In each mass interval, the galaxies’ XLFs were calculated and aver- To account for the varying sensitivity, we need to char- aged to show how the average number of ULXs compared acterise the distribution of X-ray detection upper limits to the average number of AGN as a function of observed X- in the region of MPA-JHU with coverage from 3XMM. ray luminosity. As can be seen on the plots in the left-hand There are 6,447 dwarf galaxies with matches to found within column of figure9, the vast majority of the data points do MPA-JHU and 3XMM, whose co-ordinates were uploaded not overlap with the ULX XLFs. to FLIX. It was able to produce X-ray flux limits at the co- Whilst this large gap does exist, it is important to quantify how many ULXs we might expect in this sample. Stochastic star formation could produce a single, very luminous ULX 2 Found at https://www.ledas.ac.uk/flix/flix dr7.html which may account for some of the overlap we see in the

MNRAS 000,1–19 (2020) 10 K. L. Birchall et al. lowest mass probability distributions. To calculate the num- median value for each quantity in the full statistical sample. ber of ULXs we might expect, we folded the ULX luminos- The power laws are shown as a dashed red line allowing us to ity function through the correction fractions extracted from more clearly identify how the probability of finding an AGN the 3XMM sensitivity curve. This calculation suggests there in a dwarf galaxy, within a given mass and redshift, changes are 1.26 individual ULX detections within our dwarf galaxy as a function of observed X-ray luminosity and sBHAR. We sample. Thus, the probability that 1 or more of our X-ray then performed a χ2 fit with a power law function, adopting detections is in fact an ULX, rather than an AGN, is ∼72% the average of the Gehrels(1986) uncertainties as the error in whilst the probability of 2 or more contaminants is ∼36%, each point. Parameter errors were then estimated by taking assuming a Poisson distribution. the square-root of the covariance matrix’s diagonal. Using It has also been noted that galaxies with lower than this, the uncertainty in the power laws could be highlighted solar metallicities, like the dwarf galaxies being studied, as pink regions in figure9. have an enhanced HMXB population (Brorby et al. 2014). These power law fits reinforce the effect of the upper Thus the Mineo et al.(2012a) XLF could be underestimat- limits correction, giving us insight into the true extent of ing the probability of finding a HMXB in this population black hole activity across the dwarf galaxy population. We of galaxies. Lehmer et al.(2019) observes this enhancement can confidently say that, in the higher mass plots (top and in 4 dwarf galaxies of similar masses and metallicities middle rows), the average number of AGN in dwarf galaxies to those in our sample. We follow the same procedure as a function of both luminosity and sBHAR are well de- outlined earlier in the paragraph but use the normalisations scribed by a power law. The luminosity power law in both outlined in Lehmer et al.(2019) and observe some increase these mass bins is distinct from the Mineo et al.(2012a) in the probability of finding a HMXB at any given X-ray XLF in both normalisation and index, highlighting the fact luminosity. We have not shown this relationship on figure9 we have identified a distinct sample of AGN. Moreover, the because the small sample size means the relationship will luminosity power laws describing the high and middle mass have large and undefined uncertainties and believe plotting samples are identical within the error regions. The sBHAR the relationship would appear overly definitive. When power laws do not have the same degree of similarity. How- the Lehmer et al.(2019) relationship is plotted on the ever, they are very likely derived from a clean sample of AGN lowest galaxy mass panel we observe that the increase does and their error regions are of a similar size to the luminos- overlap with the lower luminosity half of the fit line which ity power laws which suggests that sBHAR, like luminosity, suggests that some of these detections may not be AGN. could be a fundamental property of an AGN. This has been However, when plotted in the higher host galaxy mass bins previously observed in Aird et al.(2012) for a higher redshift we see that the increase is insufficient to overlap with the and higher mass sample. probability distributions. Thus, we can be confident that A slight downward trend exists in the low mass bin but the observed sample residing in the higher mass dwarf it is much more uncertain given only 4 AGN were identified galaxies are AGN. in this region (compared to the 15 and 10 AGN found in the high and middle mass bins respectively). Thus each point After confirming our sample were AGN we could con- is based on a single observation and leads to comparatively fidently construct probability distributions for the sBHAR, large errors. as first considered in section5. Observed X-ray luminosity Overall, we see that AGN in dwarf galaxies are de- can be affected by a number of host galaxy properties so by tected emitting at a range of luminosities, driven by a cor- doing this we can reduce the observational bias whereby a respondingly large range of accretion rates. For each mass black hole growing at a given accretion rate in a low mass bin, the average number of AGN increases with decreasing galaxy produces a similar observable X-ray luminosity when X-ray luminosity and sBHAR. This is consistent with other compared to a black hole with a lower accretion rate in a AGN population studies (Georgakakis et al. 2017; Aird et al. higher mass host galaxy. We can also confirm whether the 2018), showing we expect AGN with lower luminosities and results shown in figure8 are consistent with the underlying sBHARs to be much more numerous, despite our current population of AGN in dwarf galaxies. We repeated the inability to detect them. upper limits correction process described earlier but instead binned the observed and upper limits data as a function of sBHAR. The results, plotted in the right-hand column of figure9, show a similar dynamic range to the observed X-ray luminosities that varies significantly depending on 7 AGN FRACTION AS A FUNCTION OF the stellar mass bin. In addition as we move down the mass HOST GALAXY MASS AND REDSHIFT scale, the average sBHAR increases. This is consistent with The fraction of galaxies that host an AGN within given the results in figure8 as lower mass galaxies require a larger mass and redshift regimes can be easily derived from the sBHAR to be observed. probability distributions shown in figure9. In this section we use the luminosity probability distributions, in the A power law of the following form was fit to each of left-hand column of figure9, to calculate robust AGN these plots, fractions above fixed luminosity limits to provide a direct 0 comparison with previous work. log10(p(X)) = A + k(log10(X) − x ) (9) where p(X) is the probability of observing an AGN with First, we consider how AGN fraction varies as a function corresponding X-axis quantity, X. Each equation is centred of host galaxy mass. For each plot in the left-hand column at x0 = 40.5 for luminosity and -2.5 for sBHAR, this is the of figure9 the probabilities were summed, converted into

MNRAS 000,1–19 (2020) AGN in Dwarf Galaxies 11 9

l o g 1 0 6 ( M 0 . * 0 /

M <

z )

9 . 5 8

l o g 4 1 0 0 ( . M 0

* < /

M z

)

9 6 . 8

l o 2 g 0 1 . 0

0 (

M <

* / z M

)

Figure 9. Distributions showing the probability of finding an AGN within the completeness corrected dwarf galaxy sample as a function of both observed X-ray luminosity (left-hand column) and specific black hole accretion rate (right-hand column). Power laws (dashed red line) were fit to each distribution, their uncertainty is also shown (pale red region). The redshift and mass applied to each row are shown. The blue line shows the expected average contribution from discrete, stellar-origin sources (HMXBs, ULXs) within our galaxy sample, based on the Mineo et al.(2012a) XLF scaled by the average SFR of the galaxies in our sample. It is plotted on each of the left-hand luminosity plots to rule out the possibility of a central ULX or group of ULXs being responsible for some of the low luminosity emission. See section6 for more details on how these plots were constructed. MNRAS 000,1–19 (2020) 12 K. L. Birchall et al. fractions, with the following equation, 8 log L > 39 42 10 X Õ 7 log10LX > 40.5 f (LX > Lmin) = p(log10(LX)) × ∆ log10(LX) (10) Lmin 6 where f (LX > Llim) is the AGN fraction with X-ray lumi- ) nosity > Lmin. The AGN fraction for the full range of X-ray % 5 (

39 luminosity (LX > 10 erg/s) are plotted in green on ﬁgure ) m i 10 at the median mass for that sample. The process was l 4 L

then repeated with only the higher luminosity half of the >

40.5 X observed AGN hosts (LX > 10 erg/s), and plotted as pur- L 3 ( ple squares on the same figure. Overall we see an increase f in AGN fraction with host galaxy mass. This is consistent 2 with the expectation that black holes in higher mass galaxies are near ubiquitous, thus as as the incidence of black holes 1 increases we would expect a corresponding increase in the in- 0 cidence of AGN. We also saw in section6 that AGN with low 7.0 7.5 8.0 8.5 9.0 9.5 accretion rates are more common, thus the likelihood that log10 (Stellar Mass) (M ) an AGN would produce detectable emission rises with host galaxy mass. This helps explain the large difference in AGN fraction for log10(LX) > 40.5. At a fixed host galaxy mass Figure 10. AGN fraction, f (LX > Llim), as a function of host this measurement is highlighting the higher accretion rate galaxy mass for the full (green circles) and high luminosity (pur- end of the distribution. A statistically significant increase ple squares) samples as a function of host galaxy mass. AGN along the higher luminosity track would suggest increasing fraction increases with stellar mass in both luminosity regimes, host galaxy mass is the dominant source of increasing AGN however, the increase is more statistically significant for AGN fraction in the higher luminosity regime. However, we can- with log10(LX) > 40.5. not confirm this result for the lowest stellar masses in this regime as there were no AGN observed with these properties. The upper limit presented as a triangle is based on the assumption that a single AGN does actually exist; a black hole in a host of this mass would have to be accreting at an exceptionally high rate to produce detectable luminosities. The errors on the AGN fraction as a function of host mass limits and luminosity limits shown in figure 11. This galaxy mass, f (L > L ), are found by summing the errors X lim figure shows our results plotted as circular points at the on each data point in quadrature. For the full luminosity median redshift of each luminosity and mass bin, alongside bin, log10(LX) > 39, the errors are consistent with a constant 9.5 the Mezcua et al.(2018) data (square points), and from fraction up to M∗ ∼ 10 M . Perhaps host galaxy mass is others studies as lines, regions and points. The errors in not the only property that affect the incidence of AGN for each of our AGN fractions is found by accounting for the the bulk of the population. 1σ uncertainty in the power law fit parameters. Thus, we also consider how the AGN fraction varies Due to the limited redshift range of MPA-JHU, our as a function of the host galaxy’s redshift. Mezcua et al. points cover only the low redshift end of the axis, however (2018) present some interesting work on this topic, looking in both panels they are consistent, within the errors, with 7 9 the results from prior studies shown in figure 11. at dwarf galaxies (10 < M∗ < 3 × 10 M ) in the Chan- dra COSMOS-Legacy survey out to z ∼ 2.4. Using similar For the lower luminosity AGN, in the upper panel, we techniques to those outlined in section3, they identified a find that our AGN fractions are consistent for both stel- sample of 40 AGN which they use to study the evolution of lar mass bins showing no mass dependence. This contrasts the AGN fraction as a function of stellar mass, X-ray lumi- with the results from Mezcua et al.(2018), taken at higher nosity and redshift. Their sample allows them to measure redshift. However, within the large uncertainties our data is consistent with Mezcua’s, so we don’t find any evidence for the AGN fraction out to z ∼ 0.7 and log10(LX) ∼ 42.4; we have only observed one AGN that reaches luminosities in a mass dependence in lower luminosity AGN. that range. Lower fractions in the bottom panel of figure 11 show By extrapolating the power law fit made in section6 we that higher luminosity AGN are less abundant than their can estimate the AGN fraction at higher luminosities that dimmer counterparts. This is consistent with earlier findings are not directly probed by our study. We used the power law suggesting that AGN are more numerous at lower observed X-ray luminosity. Mezcua et al.(2018) suggested that the from the high mass bin (9 < log10(M∗/M ) < 9.5) and re-ran AGN fraction may decline with increasing redshift, although the X-ray upper limits analysis for the 7−log10 (M∗/M ) < 9 used in Mezcua et al.(2018). We calculated the predicted given the large uncertainties, their measurements are consis- AGN fraction as follows, tent with a constant AGN fraction out to z = 0.7. Our result, in this mass and redshift bin, provides an estimate at low ¹ Lmax fextrap(LX) = A + k(log10(LX) − 40.5) d log10(LX) (11) redshift, consistent with the Mezcua results at higher red- Lmin shifts. Our results, therefore, indicate that the AGN fraction where fextrap(LX) is the predicted AGN fraction between the in dwarf galaxies is constant with increasing redshift.

MNRAS 000,1–19 (2020) AGN in Dwarf Galaxies 13

Next we investigated the activity of the central SMBHs 7 - log10(M*/M ) < 9 0 powering our AGN by calculating their accretion rates. 10 9 - log10(M*/M ) < 9.5 We found that the SMBHs span a wide range of accretion rates but that none are accreting very eﬃciently. The most ) common environment in our sample is a host of mass % (

9−9.5

) ∼ 10 M with an SMBH accreting at about 0.1% of X 10 1 L

( its Eddington luminosity. The most active SMBH has an

p 41.5 - log10(Lx) < 42.4 a r

t accretion rate of ∼ 10% of its Eddington luminosity. x e

f log10(Lx) 42.4 Finally, we attempted to correct our sample to account 10 1 for AGN that could have been missed due to the varying sensitivity of 3XMM. To do this, the observed AGN This work Pardo et al (2016) sample needed to be reduced so it matched the underlying 10 2 Mezcua et al (2018) Reines et al (2013) Aird et al (2018) Schramm et al (2013) galaxy distribution and had significant detections in the appropriate 3XMM band. Thus our observed sample was 10 2 10 1 100 z reduced from 61 to statistically robust sample of 29 AGN, which were then split into 3 mass and redshift bins. To correct the observed distribution, we determined the upper Figure 11. AGN fraction as a function of redshift for different X-ray flux limit at the positions of the 4,331 dwarf galaxies observed X-ray luminosity ranges, adapted from Mezcua et al. in MPA-JHU that lie within the 3XMM footprint. We use (2018). The circular points were derived from power law fits, these flux upper limits to determine a sensitivity function within highlighted mass and corresponding 90% complete red- that allows us to correct our observed distributions of shift regimes discussed in section6. Our results are consistent AGN luminosities for incompleteness and recover the true with most other work in the field. probability distribution functions of AGN luminosities and specific accretion rates within the dwarf galaxy population. As a final check, the luminosity probability distributions 8 SUMMARY AND CONCLUSIONS were compared to the Mineo et al.(2012a) XLF of HMXBs This study has rigorously matched a sample of dwarf and ULXs. They were found to lie above the XLF indicating galaxies from the MPA-JHU catalogue to their central that we are identifying a distinct population of AGN within X-ray counterpart from 3XMM. We then predicted the dwarf galaxies and are not significantly contaminated by contribution that XRBs and hot gas may make to this the detection of individual, bright ULXs within the galaxy. X-ray emission based on each galaxy’s mass and SFR. Any The probability distributions show that AGN in dwarf galaxies that had observed emission three times greater galaxies have a wide range of activity, with the probability than what was predicted were considered likely to be of identifying an AGN being well described by a power law. host an AGN. We showed that placing this threshold, AGN are more numerous at lower X-ray luminosities and likely isolated the hardest X-ray emission in this sample. sBHARs. Off-nuclear emission cannot be completely ruled out given the resolution of 3XMM, however recent simulations by We used our robust measurements of the probability Bellovary et al.(2019) and observations in Reines et al. of hosting an AGN as a function of X-ray luminosity to (2019) suggest that such a detection could still be an AGN. determine how the incidence of AGN varies as a function Thus, we can confidently say we have identified 61 dwarf of other galaxy properties. We find evidence that the frac- galaxy AGN hosts. Of these AGN, 21 have been previously tion of galaxies with an AGN above a luminosity limit of 39 identified (see appendixB for details). LX > 10 erg/s increases as a function of stellar mass, rising 7.4 9.5 from ∼ 2.7% at M∗ ∼ 10 M to ∼ 6% at M∗ ∼ 10 M . We then performed BPT analysis on the 53 AGN We extrapolate our measurements to higher luminosity hosts with significant detections in the required emission thresholds and compare to higher redshift measurements lines to see if their optical characteristics matched their from Mezcua et al.(2018), finding no evidence for any X-ray classification. We found that the vast majority of evolution in the AGN fraction in dwarf galaxies between these galaxies were classified as star-forming. Our result z ∼ 0.7 and z ∼ 0.03 probed by our study. adds to a growing body of evidence suggesting that optical selection methods may miss AGN, particularly those In conclusion, we have shown that AGN with a broad residing in dwarf galaxies. To investigate if star formation range of accretion rates are found across the dwarf galaxy dominated the optical component of AGN emission, we population. Our study shows that many of these AGN will took the X-ray emission - dominated by the AGN - and be missed by the standard optical selection tools but are translated it down to the SDSS U-band, to find out the revealed by the careful analysis of X-ray observations of suf- AGN’s contribution to the host galaxy’s optical emission ficient depth. Thus, accreting central massive black holes which is compared to the observed u-band flux. All but appear to be a common feature even in at the lowest galaxy one of the X-ray selected AGN classified as star-forming masses. Our measurements quantify the AGN fraction, pro- had their optical emission dominated by the galaxy. viding lower limits on the incidence of massive black holes This finding shows that star formation can confuse the re- in this low-mass regime and thus crucial constraints on the sults from BPT diagnostics and hide the signatures of AGN. physical mechanisms that determine their formation and subsequent growth.

MNRAS 000,1–19 (2020) 14 K. L. Birchall et al.

ACKNOWLEDGEMENTS Series Vol. 228, Dynamics of Star Clusters and the Milky Way. p. 187 (arXiv:astro-ph/0011328) We thank the referee for their helpful comments. KB ac- Latif M. A., Ferrara A., 2016, Publications of the Astronomical knowledges funding from a STFC PhD studentship. JA ac- Society of Australia, 33, e051 knowledges support from an STFC Ernest Rutherford Fel- Lehmer B. D., et al., 2016, ApJ, 825, 7 lowship, grant code: ST/P004172/1. Lehmer B. D., et al., 2019, ApJS, 243, 3 This research has made use of data obtained from the Lemons S. M., Reines A. E., Plotkin R. M., Gallo E., Greene 3XMM XMM-Newton serendipitous source catalogue com- J. E., 2015, ApJ, 805 piled by the 10 institutes of the XMM-Newton Survey Sci- Lintott C., et al., 2011, MNRAS, 410, 166 ence Centre selected by ESA. Lusso E., Risaliti G., 2016, ApJ, 819, 154 In addition, this research made use of Astropy,3 a Madau P., Rees M. J., 2001, ApJ, 551, L27 Madau P., Haardt F., Dotti M., 2014, ApJ, 784 community-developed core Python package for Astronomy Marleau F. R., Clancy D., Habas R., Bianconi M., 2017, A&A, (Astropy Collaboration et al. 2013, 2018). 602, A28 Funding for SDSS-III has been provided by the Al- Martin D. C., et al., 2005, ApJ, 619, L1 fred P. Sloan Foundation, the Participating Institutions, Mezcua M., Civano F., Fabbiano G., Miyaji T., Marchesi S., 2016, the National Science Foundation, and the U.S. Depart- ApJ, 817 ment of Energy Office of Science. The SDSS-III web site Mezcua M., Civano F., Marchesi S., Suh H., Fabbiano G., Volon- is http://www.sdss3.org/. teri M., 2018, Monthly Notices of the Royal Astronomical Society, 478, 2576 Miller M. C., Davies M. B., 2012, ApJ, 755, 81 Mineo S., Gilfanov M., Sunyaev R., 2012a, MNRAS, 419, 2095 REFERENCES Mineo S., Gilfanov M., Sunyaev R., 2012b, MNRAS, 426, 1870 Moran E. C., Filippenko A. V., Chornock R., 2002, ApJ, 579, L71 Agostino C. J., Salim S., 2018, arXiv e-prints, p. arXiv:1811.11755 Nucita A. A., Manni L., De Paolis F., Giordano M., Ingrosso G., Aird J., et al., 2012, ApJ, 746, 90 2017, ApJ, 837, 66 Aird J., Coil A. L., Georgakakis A., 2017, MNRAS, 465, 3390 Paggi A., Fabbiano G., Civano F., Pellegrini S., Elvis M., Kim Aird J., Coil A. L., Georgakakis A., 2018, MNRAS, 474, 1225 D.-W., 2016, ApJ, 823 Astropy Collaboration et al., 2013, A&A, 558, A33 Pardo K., et al., 2016, ApJ, 831 Astropy Collaboration et al., 2018, AJ, 156, 123 Plotkin R. M., Gallo E., Haardt F., Miller B. P., Wood C. J. L., Baldassare V. F., Reines A. E., Gallo E., Greene J. E., 2017, ApJ, Reines A. E., Wu J., Greene J. E., 2016, ApJ, 825, 139 836, 20 Reines A. E., Comastri A., 2016, Publications of the Astronomical Baldwin J. A., Phillips M. M., Terlevich R., 1981, PASP, 93, 5 Society of Australia, 33 Begelman M. C., Volonteri M., Rees M. J., 2006, Monthly Notices Reines A. E., Volonteri M., 2015, ApJ, 813, 82 of the Royal Astronomical Society, 370, 289 Reines A. E., Sivakoff G. R., Johnson K. E., Brogan C. L., 2011, Bellovary J., Volonteri M., Governato F., Shen S., Quinn T., Wad- Nature, 470, 66 sley J., 2011, ApJ, 742 Reines A. E., Greene J. E., Geha M., 2013, ApJ, 775 Bellovary J. M., Cleary C. E., Munshi F., Tremmel M., Chris- Reines A. E., Condon J., Darling J., Greene J., 2019, arXiv e- tensen C. R., Brooks A., Quinn T. R., 2019, MNRAS, 482, prints, p. arXiv:1909.04670 2913 Rosen S. R., et al., 2016, A&A, 590, A1 Brandt W. N., Alexander D. M., 2015, Astronomy and Astro- Salim S., et al., 2007, ApJS, 173, 267 physics Review, 23 Sartori L. F., Schawinski K., Treister E., Trakhtenbrot B., Koss Brorby M., Kaaret P., Prestwich A., 2014, MNRAS, 441, 2346 M., Shirazi M., Oh K., 2015, Monthly Notices of the Royal Bruzual G., Charlot S., 2003, MNRAS, 344, 1000 Astronomical Society, 454, 3722 Cann J. M., Satyapal S., Abel N. P., Blecha L., Mushotzky R. F., Shen Y., et al., 2011, The Astrophysical Journal Supplement Se- Reynolds C. S., Secrest N. J., 2019, ApJ, 870, L2 ries, 194, 45 Carrera F. J., et al., 2007, A&A, 469, 27 Sun J., Shen Y., 2015, ApJ, 804, L15 Chabrier G., 2003, PASP, 115, 763 Vanden Berk D. E., et al., 2001, AJ, 122, 549 Charlot S., Fall S. M., 2000, ApJ, 539, 718 Véron-Cetty, M.-P. Véron, P. 2010, A&A, 518, A10 Gehrels N., 1986, ApJ, 303, 336 Watson M. G., et al., 2009, A&A, 493, 339 Georgakakis A., Aird J., Schulze A., Dwelly T., Salvato M., Nan- dra K., Merloni A., Schneider D. P., 2017, MNRAS, 471, 1976 Greene J. E., 2012, Nature Communications, 3, 1304 Kauffmann G., et al., 2003a, MNRAS, 341, 33 Kauffmann G., et al., 2003b, MNRAS, 346, 1055 Kawasaki K., Nagao T., Toba Y., Terao K., Matsuoka K., 2017, APPENDIX A: IMAGES ILLUSTRATING ApJ, 842, 44 CROSS-MATCHING PROCEDURE Kennicutt R. C., Evans N. J., 2012, Annual Review of Astronomy Figure A1 provides examples from our dwarf galaxy AGN and Astrophysics, 50, 531 sample and illustrates results of the cross-matching process. Kewley L. J., Dopita M. A., Sutherland R. S., Heisler C. A., Trevena J., 2001, ApJ, 556, 121 The images are ordered by increasing sky separation between Kormendy J., Ho L. C., 2013, Annual Review of Astronomy and the optical centre of the galaxy (black cross) and the X- Astrophysics, 51, 511 ray signal (magenta cross). In the top left panel, the X-ray Kroupa P., 2001, in Deiters S., Fuchs B., Just A., Spurzem R., signal is 0.3” away from the optical centre of the galaxy, Wielen R., eds, Astronomical Society of the Pacific Conference and it sits well within the X-ray position error (solid circle). In contrast, the optical centre of the galaxy in the bottom right panel sits at the edge of the 3.5 × position error (dashed 3 http://www.astropy.org circle) as it lies 6.8” away from the X-ray signal.

MNRAS 000,1–19 (2020) AGN in Dwarf Galaxies 15 n o i t a r a p e S

g n i s a e r c n I

Figure A1. A sample of 12 showing the positions of the galaxy (black cross) and X-ray source (magenta cross) surrounded associated position error (solid circle) and 3.5 × position error (dashed circle). These images are ordered by increasing sky separation between these signals.MNRAS 000,1–19 (2020) 16 K. L. Birchall et al.

APPENDIX B: LIST OF AGN CANDIDATES Table B1 contains data for the 61 dwarf galaxies that we identify as hosting X-ray AGN. The table is split into two sections: first are the AGN with a valid photometric SFR, followed by the AGN whose SFR was calculated using the Hα luminosity and Kennicutt & Evans(2012) method. Each galaxy’s co-ordinates have also been uploaded to SIMBAD and NED to check if these AGN have been previously detected. The columns are defined as follows: • (1) Common name of galaxy taken from SIMBAD and NED. Galaxies marked with an asterisk are those which made up the statistical sample (see section6 for more details). • (2) A letter in this column indicates that the AGN has been identified previously. Here follows a list of the studies which have previously identified our AGN, the letters used to represent them and the wavelength range within which their study focuses: – a: Véron-Cetty, M.-P. & Véron, P.(2010) (Optical, X-ray & Radio) – b: Reines et al.(2013) (Optical) – c: Lemons et al.(2015) (X-ray) – d: Sartori et al.(2015) (Optical & IR) – e: Sun & Shen(2015) (Optical) – f: Baldassare et al.(2017) (X-ray & UV) – g: Kawasaki et al.(2017) (Optical) – h: Marleau et al.(2017) (IR) – i: Nucita et al.(2017) (X-ray) • (3) & (4) Galaxy co-ordinates taken from MPA-JHU (based on SDSS DR8). • (5) & (6) X-ray co-ordinates taken from 3XMM DR7. • (7) Separation between the optical and X-ray co- ordinates in arcseconds. • (8) Total stellar mass of galaxy and associated errors in units of M . The median error for the upper/lower error on the mass is 0.1/0.08 dex. −1 • (9) SFR and associated errors in units of M yr . The median error for the upper/lower error on the SFR is 0.27/0.21 dex. • (10) Observed 2-12keV X-ray luminosity and associated errors, taken from 3XMM, in units of erg/s. • (11) Predicted 2-12keV X-ray luminosity (LXRB + LGas; see section3 for more information) in units of erg/s.

MNRAS 000,1–19 (2020) 38 38 38 37 39 38 39 39 38 38 39 38 38 38 38 37 38 38 38 39 39 39 38 39 38 38 38 37 37 36 38 38 38 39 39 38 38 39 E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E 6 4 6 0 2 8 5 8 5 3 2 4 0 4 8 1 9 9 4 5 7 1 2 9 5 6 4 2 7 6 3 7 8 3 1 8 2 9 ...... 1 3 3 2 2 1 3 7 9 2 7 2 7 8 2 9 5 3 1 2 2 3 6 3 1 3 2 7 1 1 4 2 1 2 2 4 7 1 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± (erg/s) 38 39 39 37 39 38 39 39 38 38 39 38 39 39 38 38 38 38 38 39 39 39 38 39 38 38 39 38 37 36 38 38 38 39 39 38 38 39 (11) 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 Pred × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × L

47 21 10 62 28 86 24 32 42 44 33 44 45 03 49 44 10 73 74 76 12 79 65 02 53 31 05 AGN25 62 in23 Dwarf26 89 77 Galaxies10 78 60 04 67 17 ...... 5 1 1 7 2 3 3 4 9 3 6 3 1 1 9 1 5 4 1 1 3 2 7 4 5 7 1 1 5 6 6 9 4 3 5 5 9 3 40 40 39 40 40 39 39 40 40 39 40 39 40 40 40 39 38 40 38 39 40 39 39 40 38 40 39 39 39 38 39 39 39 40 40 40 39 41 continued on next page... E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E 1 4 7 9 1 8 9 8 4 8 9 7 3 7 4 0 4 3 2 5 4 7 2 1 0 3 5 4 3 0 4 3 0 3 3 5 0 2 ...... 5 6 7 1 4 4 4 1 1 9 2 3 1 1 2 3 2 2 4 5 3 6 7 2 5 1 8 1 1 7 4 9 6 2 3 1 6 1 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± (erg/s) 40 40 39 39 40 39 40 40 39 40 40 39 40 39 39 40 39 39 39 40 40 40 40 40 39 40 39 39 39 40 40 39 39 40 40 39 39 40 (10) 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 Obs × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × L 90 77 71 41 19 69 13 16 70 46 24 88 93 44 12 18 78 11 60 01 99 06 02 80 17 21 36 24 47 39 10 14 02 45 89 99 34 54 ...... 4 7 4 3 3 2 1 4 7 1 4 7 1 8 3 1 3 3 1 4 2 1 1 3 6 2 3 2 2 1 1 7 8 2 4 4 7 5 2 2 1 2 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 2 1 1 1 1 2 1 1 1 2 2 1 2 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 2 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 ) − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − 0 − − − 0 − − − 0 − − − − − − − − − − − − − − − − − − − − 1 E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E − E E E E E E E E E E E E E E E E E E 0 1 4 2 2 5 5 2 5 2 0 3 3 9 7 7 8 8 3 2 9 5 4 5 7 8 6 7 8 5 5 1 6 5 8 1 7 9 8 1 9 7 7 3 9 9 4 0 3 5 3 0 5 6 3 0 8 6 8 7 2 2 8 1 7 2 0 6 1 5 4 9 3 8 4 0 ...... 4 4 1 5 3 3 3 3 2 2 3 5 1 1 2 2 4 4 4 3 7 1 5 6 1 7 4 2 2 3 9 1 7 3 3 3 3 1 1 2 8 1 3 2 2 3 9 1 3 6 1 1 7 1 5 1 1 2 yr 2 5 3 3 3 3 2 1 2 2 4 2 1 1 2 1 7 1 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + − − − − − − − − − − − − − − − − − − − − − − − − − − − − − + + + + + + + + + − − − − − − − − − 1 1 1 2 1 2 1 2 1 1 1 1 2 1 1 2 1 2 2 1 1 2 3 4 1 1 2 1 1

0 0 0 0 0 0 0 0 0 − − − − − − − − − − − − − − − − − − − − − − − − − − − − − (9) M 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × 30 61 50 17 10 43 04 20 22 39 61 85 21 57 00 85 70 13 53 28 90 15 19 57 04 87 60 70 10 28 30 90 57 60 15 22 17 72 ...... SFR ( 1 1 2 1 1 1 1 2 1 1 3 2 3 6 5 2 6 1 3 3 3 6 2 1 5 5 8 1 1 3 5 1 1 2 4 2 2 2 8 8 8 6 8 8 8 8 8 8 8 7 8 8 7 6 7 7 7 8 8 8 8 8 8 8 8 6 7 6 7 7 8 8 8 7 8 9 8 9 8 5 8 8 8 8 8 8 8 7 8 8 7 5 6 7 7 8 8 8 8 8 8 8 8 6 7 6 7 7 8 8 8 7 8 9 E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E ) 9 8 7 5 4 2 3 8 0 7 5 0 3 8 6 0 6 8 1 7 8 5 8 5 5 4 6 8 2 4 5 2 6 1 2 5 0 1 2 1 9 1 0 4 2 0 5 5 8 0 1 8 2 4 6 0 0 7 0 2 2 5 8 8 5 1 1 6 2 6 1 9 1 9 4 0 ......

5 7 3 3 5 2 1 7 2 1 6 5 5 2 6 4 1 5 4 5 2 4 5 3 4 4 2 5 4 6 1 3 3 6 9 2 2 1 8 1 2 1 4 2 1 8 1 1 3 4 4 1 6 4 1 5 3 5 2 2 5 2 4 3 2 9 5 5 1 2 4 3 8 2 1 1 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − M 8 9 9 6 9 9 8 9 9 8 9 8 9 9 7 7 7 8 8 9 9 8 9 9 9 9 9 7 8 7 7 7 9 9 9 8 9 9 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 (8) × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × Mass ( 60 14 70 09 82 12 63 97 04 49 57 30 46 02 41 00 84 15 43 67 28 35 27 14 18 92 11 05 17 50 85 47 73 64 37 03 09 50 ...... 8 1 1 3 2 1 6 1 1 7 1 3 2 1 8 1 3 4 2 1 1 8 2 2 2 1 1 1 2 2 7 6 1 2 2 2 1 2 ) ” (7) 1.964 3.245 3.444 3.308 5.309 2.694 2.543 2.074 7.693 3.045 2.602 1.090 1.199 5.533 1.003 0.698 0.703 2.835 1.110 0.242 2.468 0.656 0.907 2.210 0.380 2.089 5.867 6.775 5.909 0.091 2.298 2.255 2.118 2.659 0.835 6.279 3.691 0.596 Separation ( (6) Dec ) 0.634991 0.176987 6.054252 3.764018 3.680705 5.851398 o -0.232070 -0.411804 -0.214698 51.883489 21.064209 19.201694 38.080536 15.195871 16.479499 36.402101 12.947564 12.760766 29.635034 58.063435 53.501374 38.056521 61.396035 58.561797 58.385212 19.763534 23.459134 43.126437 42.474715 14.220695 33.546854 39.519672 27.945354 27.637073 53.962394 11.515225 55.362280 26.513346 X-ray ( (5) RA 18.849929 26.371791 40.320863 44.190282 123.635037 124.912299 125.619971 128.002743 134.126541 135.897672 139.677069 141.835438 152.021634 156.359322 157.254775 158.542303 159.687208 166.424690 167.301427 172.128389 172.293456 175.055270 176.257412 178.993608 181.942229 182.253462 183.469156 184.283938 184.632586 186.453591 188.832976 188.926617 195.004703 195.140594 196.784717 197.087516 199.877721 205.282967 (4) Dec ) o 0.635465 0.177643 6.055122 3.764374 3.682264 5.849806 -0.231196 -0.411672 -0.215192 51.884872 21.064728 19.201637 38.082253 15.195035 16.479506 36.401897 12.947362 12.761314 29.635242 58.063630 53.501450 38.056496 61.396324 58.561844 58.385834 19.763748 23.458563 43.126354 42.475260 14.220132 33.546870 39.519196 27.945436 27.637766 53.962387 11.515293 55.362810 26.513401 Optical ( (3) RA 18.850199 26.372410 40.321250 44.189983 123.634210 124.911720 125.620580 128.002140 134.124920 135.897540 139.676320 141.835160 152.021360 156.360800 157.254560 158.542300 159.686900 166.425690 167.301650 172.128300 172.294000 175.055150 176.257550 178.993360 181.942140 182.253720 183.470730 184.282880 184.632190 186.453610 188.833530 188.927000 195.004040 195.140300 196.785110 197.089300 199.876170 205.283140 i i i i i i i i i i e c g a h (2) found? Previously b; c; f; h; i Data for all 61 dwarf galaxies believed to be strong candidates for hosting an AGN. Name (1) SDSS J011523.96+003808.7 * SDSS J014529.26+001036.0 SDSS J024117.09-001352.3 * 2MASX J02564580+0603173 LEDA 2402319 * 2MASX J08193880+2103521 * SDSS J082228.93+034551.7 2MASS J08320053+1912058 SDSS J085629.97+380456.1 SDSS J090335.40+151142.0 SDSSCGB 15.2 2XMMi J092720.4+362407 * PWC2011 J100805.1+125650 SDSS J102526.59+124540.3 LEDA 30866 Mrk 1434 * SDSS J103844.88+533005.2 * LEDA 2116718 UGC 6192 * SDSS J112830.77+583342.9 SDSS J112910.56+582309.0 * Mrk 1303 * 2XMM J114501.7+194549 * SDSS J115558.40+232730.7 NGC 4117 * ECO 11516 * SDSS J121352.97+141312.5 SDSS J121707.89+034056.3 * LEDA 39539 NGC 4395 * 2XMM J123519.9+393110 NVSS J123542-001252 LEDA 44693 * 7W 1258+27W06 * 2MASX J13070847+5357446 * SDSS J130821.42+113055.0 SDSS J131930.27+552146.0 2XMM J134107.9+263047 Table B1:

MNRAS 000,1–19 (2020) 40 39 39 38 38 39 38 38 37 39 38 37 39 38 38 38 38 38 38 38 37 38 38 E E E E E E E E E E E E E E E E E E E E E E E 5 4 7 9 2 9 1 8 4 4 8 7 6 9 8 6 7 2 1 6 8 7 4 ...... 1 1 4 5 4 1 1 3 5 1 3 7 1 6 3 9 4 9 9 1 2 6 8 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± (erg/s) 40 39 39 38 38 39 38 38 37 39 38 38 39 39 38 38 38 39 38 38 37 38 38 (11) 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 Pred × × × × × × × × × × × × × × × × × × × × × × × L

18 K.99 L.52 Birchall59 61 71 36 et74 al.17 82 85 73 53 88 11 69 97 63 05 63 15 81 64 09 ...... 3 1 3 6 9 2 3 4 5 1 7 1 1 1 8 7 7 1 6 2 3 8 7 40 39 40 39 39 39 39 39 38 40 40 38 40 40 40 39 40 39 41 40 40 40 42 E E E E E E E E E E E E E E E E E E E E E E E 2 5 9 6 3 1 9 8 8 4 2 4 0 4 8 9 2 8 2 3 6 6 2 ...... 8 5 5 1 9 8 8 4 1 1 3 9 2 1 1 8 2 9 6 1 2 8 5 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± (erg/s) 41 40 41 39 39 40 39 39 39 39 40 39 40 40 40 39 40 40 41 39 40 41 42 (10) 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 Obs × × × × × × × × × × × × × × × × × × × × × × × L 57 23 88 85 26 42 34 07 13 10 91 16 09 04 60 95 34 35 59 02 66 27 35 ...... 1 1 8 3 8 2 4 2 1 7 6 3 1 3 1 3 2 1 1 5 5 1 5 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 3 5 5 3 2 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 3 5 5 3 2 1 1 2 1 ) − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − 1 E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E − E E E E 2 1 7 9 7 9 2 7 5 8 8 3 8 6 6 6 6 4 8 2 1 5 6 0 5 1 8 3 0 3 4 4 3 2 8 2 6 6 4 8 2 1 0 7 1 8 ...... 3 3 1 2 9 2 2 2 2 1 2 2 1 3 2 2 3 9 3 1 2 2 2 1 2 5 2 2 2 2 1 2 1 1 3 2 2 3 9 3 1 2 yr 1 4 8 2 + + + + + + + + + + + + + + + + + + + + + − − − − − − − − − − − − − − − − − − − − − + + − − 1 1 1 1 1 1 2 1 1 2 1 1 1 1 1 1 2 3 4 2 2

1 0 − − − − − − − − − − − − − − − − − − − − − (9) M 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 × × × × × × × × × × × × × × × × × × × × × × × 60 26 08 17 14 20 42 33 46 82 53 94 25 90 53 56 87 65 71 14 85 11 23 ...... SFR ( 1 1 5 2 3 7 1 1 2 5 1 4 6 3 2 2 1 3 2 1 4 2 7 8 8 8 8 8 8 7 7 6 8 8 7 8 8 8 8 8 8 9 8 8 9 9 8 8 8 7 8 8 7 7 6 8 8 7 8 8 8 8 8 7 9 8 8 9 9 E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E ) 9 6 4 4 5 6 9 8 3 3 5 5 2 7 4 0 7 1 1 3 1 3 5 3 7 9 3 7 9 6 6 1 2 5 0 8 0 7 5 1 4 0 3 6 4 9 ......

7 2 6 1 2 5 6 7 1 3 3 2 2 1 2 2 2 1 3 6 1 2 2 5 1 3 9 1 3 8 5 1 2 3 2 1 1 1 1 2 8 4 9 1 3 2 + + + + + + + + + + + + + + + + + + + + + + + − − − − − − − − − − − − − − − − − − − − − − − M 9 9 9 8 8 9 8 8 6 9 9 8 9 8 9 8 9 8 9 8 8 9 9 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 (8) × × × × × × × × × × × × × × × × × × × × × × × Mass ( 40 26 28 87 17 66 44 27 24 66 64 64 57 65 08 87 19 26 92 53 52 92 27 ...... 1 1 2 6 9 2 1 4 7 1 1 1 1 7 1 6 1 8 1 8 1 2 1 ) ” (7) 1.410 0.876 0.278 0.299 2.258 0.657 4.501 4.487 3.186 1.259 0.245 1.218 6.507 2.203 2.733 3.874 1.206 5.240 6.258 3.088 6.501 3.809 4.697 Separation ( (6) Dec ) 1.726305 2.795508 3.527134 8.158277 0.466883 0.523861 0.228235 1.061061 o -0.848164 -0.488693 -1.204069 -0.513464 56.025227 58.243869 17.567890 28.273226 13.810569 55.263194 35.128482 17.807222 51.094039 39.145578 38.915903 X-ray ( (5) RA 18.590958 20.854883 46.192499 206.114470 206.829865 206.901721 215.863058 217.760817 220.052756 220.235329 228.549996 234.267702 237.079120 241.382749 241.712419 243.340961 246.676418 246.873416 324.386684 324.432454 353.106172 331.495163 356.996524 (4) Dec ) o 1.726289 2.795424 3.528175 8.157967 0.466721 0.523779 0.226537 1.062305 -0.847034 -0.489282 -1.202276 -0.513736 56.024930 58.243744 17.567960 28.273851 13.809550 55.264060 35.128174 17.807276 51.093000 39.145226 38.915330 Optical ( (3) RA 18.590588 20.855507 46.192287 206.114010 206.830260 206.901690 215.862980 217.760760 220.052920 220.236020 228.550740 234.267400 237.078920 241.382700 241.712550 243.338610 246.677060 246.874050 324.385620 324.432130 353.105260 331.494140 356.996920 i d d (2) b; f; i found? Previously b; c; d; f; h; i Data for all 61 dwarf galaxies believed to be strong candidates for hosting an AGN. Name (1) 2XMM J134427.6+560130 2XMM J134719.1+581437 * 2XMM J134736.4+173404 UGC 9215 SDSS J143102.57+281625.9 2MASX J14401271+0247441 * 2XMM J144056.3+033145 ECO 2050 * SDSS J153704.18+551550.5 * SDSS J154818.94+350741.2 2XMM J160531.8+174825 * AGC 262533 * SDSS J161321.26+510534.8 SDSS J162642.49+390842.8 * SDSS J162729.77+385455.1 SDSS J213732.54+002800.1 SDSS J213743.69+003125.5 6dFGS gJ233225.3-005049 * SDSS J011421.73+001335.6 SDSS J012325.32-002921.4 * SDSS J030446.14-011208.1 * SDSS J220558.60-003049.3 SDSS J234759.26+010344.2 Table B1:

MNRAS 000,1–19 (2020) AGN in Dwarf Galaxies 19

This paper has been typeset from a TEX/LATEX ﬁle prepared by the author.

MNRAS 000,1–19 (2020)