Galactic Supernova Remnant Candidates Discovered by THOR

TL;DR

This study uses high-resolution radio and infrared data to identify 76 new Galactic supernova remnant candidates, potentially doubling the known SNRs in the surveyed area and addressing the discrepancy between observed and expected SNR populations.

Contribution

The paper introduces a new method combining radio and infrared data to efficiently identify SNR candidates and reports the discovery of 76 new candidates in the Galactic plane.

Findings

76 new SNR candidates identified in the survey area.

Candidates are smaller and less luminous than known SNRs.

Potential to more than double the number of known Galactic SNRs.

Abstract

There is a considerable deficiency in the number of known supernova remnants (SNRs) in the Galaxy compared to that expected. Searches for extended low-surface brightness radio sources may find new Galactic SNRs, but confusion with the much larger population of HII regions makes identifying such features challenging. SNRs can, however, be separated from HII regions using their significantly lower mid-infrared (MIR) to radio continuum intensity ratios. We use the combination of high-resolution 1-2 GHz continuum data from The HI, OH, Recombination line survey of the Milky Way (THOR) and lower-resolution VLA 1.4 GHz Galactic Plane Survey (VGPS) continuum data, together with MIR data from the Spitzer GLIMPSE, Spitzer MIPSGAL, and WISE surveys to identify SNR candidates. To ensure that the candidates are not being confused with HII regions, we exclude radio continuum sources from the WISE Catalog of Galactic HII Regions, which contains all known and candidate H II regions in the Galaxy. We locate 76 new Galactic SNR candidates in the THOR and VGPS combined survey area of 67.4deg>l>17.5deg, |b|<1.25deg and measure the radio flux density for 52 previously-known SNRs. The candidate SNRs have a similar spatial distribution to the known SNRs, although we note a large number of new candidates near l=30deg, the tangent point of the Scutum spiral arm. The candidates are on average smaller in angle compared to the known regions, 6.4'+/-4.7' versus 11.0'+/-7.8', and have lower integrated flux densities. If the 76 candidates are confirmed as true SNRs, for example using radio polarization measurements or by deriving radio spectral indices, this would more than double the number of known Galactic SNRs in the survey area. This large increase would still, however, leave a discrepancy between the known and expected SNR populations of about a factor of two.

Tables

Table 1. Table 1: THOR SNR Candidates

Name	GLong	GLat	Radiusa	$S_{1.4}$	${\sigma }_{S1.4}$	Type	H06 Name
	deg.	deg.	arcmin.	Jy	Jy
G17.80$-$0.02	17.800	$-$0.020	4.4	0.29	0.19	S
G18.45$-$0.42	18.450	$-$0.420	7.6	2.16	1.77	S
G18.53$-$0.86	18.530	$-$0.860	8.6	0.43	0.15	S
G18.76$-$0.07	18.760	$-$0.073	0.8	0.26	0.04	?	18.7583$-$0.0736
G19.75$-$0.69	19.750	$-$0.690	13.2	8.06	4.96	F
G19.96$-$0.33	19.960	$-$0.330	5.9	0.45	0.34	C
G20.26$-$0.86	20.260	$-$0.860	7.5	1.97	0.74	F
G20.30$-$0.06	20.300	$-$0.060	3.1	0.19	0.14	S
G21.66$-$0.21	21.660	$-$0.210	5.1	0.59	0.34	F
G22.32+0.11	22.320	0.110	5.5	0.86	0.77	S	22.3833+0.1000
G23.11+0.19	23.110	0.190	12.1			S
G23.85$-$0.18	23.855	$-$0.180	2.7	0.34	0.08	S
G25.49+0.01	25.490	0.010	7.4	2.19	1.27	S
G26.04$-$0.42	26.040	$-$0.420	13.5			C
G26.13+0.13	26.130	0.130	11.3	3.76	7.60	S
G26.53+0.07	26.530	0.070	11.2	5.67	2.75	S
G26.75+0.73	26.750	0.730	5.3	0.53	0.50	F
G27.06+0.04	27.060	0.040	7.5	4.31	0.67	S	27.1333+0.0333
G27.18+0.30	27.180	0.305	0.9	0.05	0.03	?
G27.24$-$0.14	27.240	$-$0.140	6.1	5.36	1.31	F?
G27.39+0.24	27.390	0.240	2.4	0.16	0.22	F?
G27.47+0.25	27.467	0.246	1.7	0.20	0.11	F?
G27.78$-$0.33	27.780	$-$0.330	3.7	0.19	0.06	S
G28.21+0.02	28.210	0.020	2.5	0.23	0.13	F
G28.22$-$0.09	28.216	$-$0.087	1.7	0.06	0.09	F?
G28.33+0.06	28.330	0.060	3.2	0.42	0.29	F
G28.36+0.21	28.360	0.210	6.4	2.25	1.93	S	28.3750+0.2028
G28.56+0.00	28.564	0.000	1.5	0.89	0.10	S	28.5583$-$0.0083
G28.64+0.20	28.640	0.200	11.4	5.90	4.79	S	28.5167+0.1333
G28.78$-$0.44	28.780	$-$0.436	6.6	1.63	1.69	S	28.7667$-$0.4250
G28.88+0.41	28.880	0.410	8.9	1.97	2.26	S
G28.92+0.26	28.920	0.260	3.2	0.34	0.24	S?
G29.38+0.10	29.380	0.100	5.1	1.52	0.49	C	29.3667+0.1000
G29.41$-$0.18	29.410	$-$0.180	7.5	1.08	1.13	S
G29.92+0.21	29.920	0.210	2.1	0.26	0.11	F
G31.22$-$0.02	31.220	$-$0.020	3.1	0.55	0.37	S
G31.44+0.36	31.440	0.360	3.9	0.68	0.37	F?
G31.93+0.16	31.936	0.172	2.4	0.23	0.08	F?
G32.22$-$0.21	32.220	$-$0.210	3.1	0.63	0.16	F
G32.37$-$0.51	32.370	$-$0.510	12.0			S
G32.73+0.15	32.730	0.150	2.6	0.17	0.08	F?
G33.62$-$0.23	33.620	$-$0.230	2.7	0.26	0.05	F?
G33.85+0.06	33.848	0.061	0.6	0.02	0.01	?
G34.93$-$0.24	34.933	$-$0.244	8.1	0.77	2.37	S
G36.66$-$0.50	36.660	$-$0.500	8.2	1.29	1.20	S
G36.68$-$0.14	36.680	$-$0.140	10.0	2.16	0.58	S
G36.90+0.49	36.902	0.488	3.8	0.50	0.08	F?
G37.62$-$0.22	37.616	$-$0.223	1.9	0.41	0.12	F
G37.88+0.32	37.880	0.320	11.4	3.05	4.74	S
G38.17+0.09	38.170	0.090	14.7			S?
G38.62$-$0.24	38.620	$-$0.240	2.5	0.10	0.03	F?
G38.68$-$0.43	38.680	$-$0.430	4.3	0.44	0.12	F
G38.72$-$0.87	38.720	$-$0.870	8.5	0.70	0.80	F
G38.83$-$0.01	38.833	$-$0.014	0.6	0.01	0.00	?
G39.19+0.52	39.190	0.520	5.5	0.17	0.21	S?
G39.56$-$0.32	39.560	$-$0.320	8.5	1.19	1.64	S
G41.95$-$0.18	41.950	$-$0.180	7.0	1.19	0.50	S
G42.62+0.14	42.620	0.140	2.2	0.50	0.05	F
G45.35$-$0.37	45.350	$-$0.370	6.3	0.91	0.43	F?
G45.51$-$0.03	45.510	$-$0.030	4.1	1.63	0.42	F?
G46.18$-$0.02	46.180	$-$0.020	5.5	0.47	0.44	C?
G46.54$-$0.03	46.540	$-$0.026	6.2	0.85	0.51	S
G47.15+0.73	47.150	0.730	0.8	0.01	0.00	?
G47.36$-$0.09	47.360	$-$0.090	24.6	3.58	2.83	S
G51.21+0.11	51.209	0.113	14.9	24.35	2.10	?
G52.37$-$0.70	52.370	$-$0.700	17.7	5.24	1.75	S
G53.07+0.49	53.070	0.490	1.0	0.06	0.00	?
G53.41+0.03	53.412	0.035	4.6	1.21	0.21	S?
G53.84$-$0.75	53.840	$-$0.750	18.7	1.31	3.43	S?
G54.11+0.25	54.110	0.250	7.2	1.46	0.28	C
G56.56$-$0.75	56.560	$-$0.750	11.6	0.94	0.61	F
G57.12+0.35	57.120	0.350	14.1	0.60	0.22	C?
G58.70$-$0.31	58.700	$-$0.310	4.4	0.16	0.11	F
G59.46+0.83	59.460	0.830	4.5	0.16	0.03	F
G59.68+1.25	59.680	1.250	5.7	0.25	0.09	F?
G67.25$-$0.36	67.250	$-$0.360	2.7	0.03	0.01	F?

Equations

$$I=\frac{1}{4}\sum _{i=1}^4{I}_i=\frac{1}{4}\sum _{i=1}^4\left(I_0-\frac{B_i}{N_{B,i}}\times N_S\right),$$

$${\sigma }_I=\sqrt{\frac{1}{4}\sum _{i=1}^4{\left(I_i-I\right)}^2},$$

Full text

1 Introduction

There is a severe discrepancy in the number of detected supernova remnants (SNRs) in the Galaxy compared to that expected. The most authoritative recent compilation contains just 294 SNRs (Green, 2014, hereafter G14), but based on OB star counts, pulsar birth rates, Fe abundances, and the SN rate in other Local Group galaxies, there should be $\ga 1000$ (Li et al., 1991; Tammann et al., 1994). These estimates derive in part from studies of similar external galaxies, scaled to the Milky Way based on its luminosity. The discrepancy may not be due to a true deficiency of Galactic SNRs, but rather may hint at observational problems related to lack of sensitivity and confusion in the Galactic plane (e.g., Brogan et al., 2006, hereafter B06).

The Galactic supernova (SN) rate is an important parameter for understanding the properties and dynamics of our Galaxy. Most SN arise from the core collapse of massive stars (cf. Tammann et al., 1994), and therefore the number of SNRs in the Galaxy is tied to recent massive star formation activity. SN inject energy into the interstellar medium (ISM), driving molecular cloud turbulence and galactic fountains out of the disk (de Avillez & Breitschwerdt, 2005; Joung et al., 2009; Padoan et al., 2016; Girichidis et al., 2016). This feedback can determine the disk scale height and star formation properties of a galaxy (Ostriker et al., 2010; Ostriker & Shetty, 2011; Faucher-Giguère et al., 2013). The search for new Galactic SNRs is therefore important for understanding the global properties of the Milky Way.

SNRs are frequently identified at radio wavelengths. According to the G14 catalog, $\sim\,90$% of known SNRs are detected and well-defined in the radio regime, $\sim\,40\%$ detected in X-rays, and $\sim\,30\%$ in the optical. The radio emission is due to synchrotron radiation, which dominates the Galaxy’s low-frequency radio emission. The most common radio morphology in the G14 catalog is that of a shell, or a partial shell.

Since many types of objects emit radio emission similar to that of known SNRs, additional criteria are used to determine if a radio continuum source is a true SNR. These criteria are: 1) the radio spectrum of candidate has a negative spectral index (typically $\sim\,-0.5$), 2) the radio emission from the candidate is polarized, 3) the candidate has associated X-ray or cosmic ray emission, and/or 4) the candidate has a mid-infrared (MIR) to radio continuum flux ratio much lower than that commonly found for thermally-emitting plasmas. The first two criteria can distinguish between thermal (flat spectrum, unpolarized) and non-thermal (negative spectral index, polarized) radio emission. The third criterion is sensitive to high-temperature ($\sim\,10^{7}\,\,{\rm K}$) plasma within SNRs that is rarely detected in H II regions. The fourth criterion has characteristics of the other three, in that it can also distinguish between thermal and non-thermal emitters. The MIR emission from dust arises from the interaction of the SN shock wave with the ISM during the initial expansion phases (e.g., Douvion et al., 2001). Other non-thermal radio continuum sources such as active galactic nuclei can be excluded from SNR searches due to their small angular sizes.

Many researchers have shown that SNRs are deficient in MIR emission compared to H II regions (e.g., Cohen & Green, 2001; Pinheiro Gonçalves et al., 2011). For SNRs to produce MIR emission, they must be sufficiently dense to produce collisional heating (Williams et al., 2006). Pinheiro Gonçalves et al. (2011) found that a typical 24 $\mu$m to 1.4 $\,{\rm GHz}$ flux density ratio for SNRs is $\sim\,5$, although they found flux density ratios ranging from 0.5 to 10. This low MIR to radio flux ratio holds even for young regions like Cas A, despite their strong MIR emission (see Rho et al., 2008). Due to its powerful discriminatory power and relative ease of use, the MIR to radio flux ratio is of most interest here.

SNR candidates can be identified efficiently in radio continuum surveys using their low MIR to radio continuum flux ratios. While there is some faint associated MIR emission detected for some SNRs (Reach et al., 2006; Pinheiro Gonçalves et al., 2011), this emission is quite weak. At radio frequencies high enough that H II regions are optically thin, $\gtrsim\,1\,\,{\rm GHz}$, the MIR to radio flux ratio for SNRs is about 100 times lower than that of H II regions. Helfand et al. (2006), hereafter H06, used the lack of MIR emission as one criterion to identify 49 new SNR candidates in The Multi-Array Galactic Plane Imaging Survey (MAGPIS) 20 cm data. B06 also used this criterion to identify 35 SNR candidates in their VLA data. Recently, Green et al. (2014) used the anti-correlation between radio and 8 $\mu$m emission to identify 23 new SNR candidates from Molonglo Galactic Plane Survey (MGPS) data.

These previous studies have first identified promising radio continuum candidates, and then examined their 8.0 $\mu$m emission to determine their classifications. This method, however, has an inherent bias toward objects that look like SNRs, i.e. shell-type structures, at the expense of other possible SNR morphologies. A better method is to first identify all H II regions from their MIR morphologies and high MIR to radio continuum flux density ratios, and to then locate radio continuum sources not associated with the H II regions. This removes the confusion from H II regions in the Galactic plane, which is a major difficulty in new SNR identifications given their potentially similar radio morphologies and the much higher spatial density of H II regions. By excluding H II regions, one can search for non-thermal emission features without imposing any source morphology bias.

Here, we identify extended sources of emission in radio continuum data from The H I, OH, Recombination line survey of the Milky Way (THOR; Beuther et al., 2016) combined with the 1.4 $\,{\rm GHz}$ radio continuum data from the VLA Galactic Plane Survey (VGPS Stil et al., 2006). In the identification process, we first use the WISE Catalog of Galactic H II Regions (Anderson et al., 2014) to separate thermal and non-thermal extended emission. Compact sources of radio continuum emission detected by THOR are analyzed in Bihr et al. (2016) and Wang et al. (2017, in prep.). We focus instead on diffuse, resolved sources that are “discrete,” i.e. distinct from the diffuse background emission that pervades the Galactic disk.

2 Data

2.1 THOR

THOR is a $\sim\,20$ cm VLA survey of H I, OH, radio recombination line, and radio continuum emission in the Galactic plane from $67.4^{\circ}>\ell>14.5^{\circ}$, $|\,{\it b}\,|\leq 1.25^{\circ}$. It was conducted in VLA C-configuration, with a resolution of $\sim\,20\arcsec$. More survey details are given in Beuther et al. (2016). When the THOR continuum data are combined with 20 cm VGPS continuum data, taken with the VLA in D-configuration at a resolution of $60\arcsec$ and data taken with the 100 m Effelsberg telescope at a resolution of $9\arcmin$, the resulting data product is the most sensitive radio continuum Galactic plane survey in existence covering both large and small spatial scales. We call this combined data set “THOR+VGPS.” The THOR+VGPS data have an angular resolution of $25\arcsec$ because of smoothing we apply to the THOR data (see Beuther et al., 2016). Due to the coverage of the VGPS, the THOR+VGPS data set is restricted to $\ell>17.5^{\circ}$, so our final longitude range is $67.4^{\circ}>\ell>17.5^{\circ}$.

To detect low surface brightness SNRs, the radio observations must be sensitive to large, extended structures. To reduce confusion in the Galactic plane, the data should also have high angular resolution. The sensitivity of the THOR+VGPS data changes slightly over the extent of the survey, but a typical $1\sigma$ rms value is $\sim\,1{\rm\,mJy\,beam^{-1}}$ (Bihr et al., 2016), or $\sim 1\times 10^{-22}$ W m*-2* Hz*-1* sr*-1*. Over scales greater than that of the VGPS VLA D-configuration data ($\sim\,15\arcmin$), the surface brightness sensitivity should approach that of the Effelsberg single-dish data used in the VGPS, $\sim\,1\times 10^{-23}$ W m*-2* Hz*-1* sr*-1* (Reich & Reich, 1986; Reich et al., 1990), although the VLA data do add some noise on large spatial scales. The low surface brightness noise threshold, together with the sensitivity to small-scale structures, makes the THOR survey the ideal data set to identify new SNRs.

2.2 The WISE Catalog of Galactic HII Regions

The WISE Catalog of Galactic H II Regions (Anderson et al., 2014) is to date the largest, most complete catalog of H II regions spanning the entire Galaxy. It was created by searching WISE (Wright et al., 2010) data by-eye for the characteristic mid-infrared (MIR) signature of H II regions: $\sim\,20$ $\mu$m emission surrounded by $\sim\,10$ $\mu$m emission (Anderson et al., 2011). The $\sim\,20$ $\mu$m emission is caused by small stochastically heated dust grains that are mixed with the H II region plasma, while the $\sim\,10$ $\mu$m intensity is dominated by emission from polycyclic aromatic hydrocarbons (PAHs). All known Galactic HII regions have this characteristic morphology. Planetary nebulae can appear similar, but they are distinguished by their small sizes and weak far-infrared fluxes (Anderson et al., 2012). The H II region MIR emission detected by WISE and Spitzer is about two orders of magnitude more intense than the $\sim 20$ cm radio continuum emission, and these observatories have sensitivities far lower than that necessary to detect H II regions across the entire Galactic disk (Anderson et al., 2011, 2014). This single MIR morphological criterion can therefore be used to identify all Galactic H II regions.

The WISE catalog contains $\sim\,8000$ objects with the MIR morphology of H II regions, of which $\sim\,2000$ are H II regions with measured ionized gas velocities (H$\alpha$ or radio recombination line, RRL). This includes all known H II regions, indicating that the MIR morphological criterion can be used to identify all known Galactic H II regions. The remaining $\sim\,6000$ sources that lack ionized gas spectroscopic detections are H II region candidates, and there are two sub-classes: $\sim\,2000$ “radio-loud” candidates that have spatially coincident radio continuum emission and $\sim\,4000$ “radio-quiet” candidates that do not. Radio continuum emission, caused by the free-free emission of the ionized gas, makes the identification of H II regions more secure (e.g., Haslam & Osborne, 1987). The distribution of known regions in the catalog is statistically complete for all H II regions with ionizing fluxes consistent with single O-stars of all spectral sub-types (Armentrout et al., 2017, in prep., Mascoop et al., 2017, in prep.).

2.3 Green Catalog

G14 is the most up-to-date and authoritative catalog of Galactic SNRs. It currently contains 294 regions compiled from the literature, and tabulates their spatial coordinates, their 1 $\,{\rm GHz}$ flux densities, spectral indices, and angular sizes. The catalog sources cover the entire sky, but since it is not derived from a homogeneous survey, the catalog sensitivity varies with Galactic location. Green (2004) suggest that an earlier version of the catalog than that used here was complete to a radio surface density limit of $10^{-20}$ W m*-2* Hz*-1* sr*-1*. In addition to the surface brightness limit, the catalog appears to be lacking the small angular size SNRs that are expected (Green, 2015).

3 Methodology

Our method relies on identifying discrete regions of radio continuum emission that a) are not associated with H II regions from the WISE catalog and b) lack Spitzer or WISE MIR emission. These criteria are somewhat redundant, as nearly all discrete sources of coincident MIR and radio continuum emission in the Galactic plane are H II regions and are included in the WISE catalog. We do not have a preferred morphology for the regions we identify aside for avoiding long filamentary radio continuum features that, based on the morphologies of known SNRs, are not likely to be SNRs.

To locate new SNR candidates, we search the THOR+VGPS data by-eye. We first identify all discrete, extended radio continuum sources that are not associated with an H II region in the WISE catalog. This initial search allows us to separate SNR candidates from the much more numerous population of H II regions. We then search Spitzer GLIMPSE 8.0 $\mu$m (Benjamin et al., 2003; Churchwell et al., 2009) and MIPSGAL 24 $\mu$m (Carey et al., 2009) data at the location of each identified source to ensure that there is no detectable MIR emission. These MIR surveys have sensitivities sufficient to detect all H II regions across the entire Galaxy. For the few sources with latitudes outside the range of the Spitzer surveys, we use WISE 12 and 22 $\mu$m data (Wright et al., 2010). Our process should remove planetary nebulae and any remaining H II regions not included in the WISE catalog. The remaining radio continuum sources are either SNR candidates or known SNRs. By matching the positions and sizes with the G14 catalog, we determine which of these sources have been previously identified as SNRs. We illustrate the identification process in Fig. 1.

For each identified SNR candidate, as well as all previously-known SNRs, we compute the 1.4 $\,{\rm GHz}$ THOR+VGPS flux density using aperture photometry, following the methodology of Anderson et al. (2012). We define a circular aperture for each source that completely contains its radio continuum emission. For SNR candidates that have partial-shell morphologies, the circular aperture follows the curvature of the visible portion of the shell. We define four background apertures for each source. The background apertures sample the local background and avoid discrete continuum sources not associated with the SNR. We attempt to make the background apertures as large as possible, and to space them evenly around the source. If there are large-scale gradients in the background level, however, we sample these gradients. In complicated fields, we must define smaller background apertures, but we still aim to space them evenly around the source. Five SNR candidates are low surface brightness and confused with nearby regions, and we do not compute their flux densities.

We then compute the source integrated intensity as

[TABLE]

and the source integrated intensity uncertainty as

[TABLE]

where the summations are carried out over the four background apertures, $I$ is the average integrated source intensity, $I_{i}$ is the integrated source intensity found using one background aperture, $I_{0}$ is integrated source intensity before background subtraction, $B_{i}$ is the integrated intensity from one background aperture, $N_{B,i}$ is the number of pixels within one background aperture, and $N_{S}$ is the number of pixels within the source aperture. This method subtracts the mean intensity of a background aperture from every pixel in the source aperture. The derived uncertainties ignore the approximately 20% uncertainty in the absolute intensity calibration of the THOR+VGPS data.

We convert $I$, which has units of ${\rm\,Jy\,beam^{-1}}$, to flux densities in Jy using the THOR+VGPS circular synthesized beam size of $25\arcsec$. If there are any unrelated continuum sources that fall within the source aperture (typically extragalactic point sources or H II regions), we manually remove their flux densities from the source flux density. We use only the flux density values, rather than intensities, in subsequent analyses.

There are a couple complications with our method. First, there are numerous filamentary features in the Galactic plane observed in radio continuum emission. These features are frequently located near large massive star formation complexes. We interpret them as being dense thermally emitting ionized gas interacting with atomic or molecular material in the ISM, and do not catalog such regions as possible SNRs. Another unrelated complication also arises around massive star formation complexes, where bright continuum emission produces interferometric artifacts that do not have MIR counterparts, and therefore can be mistaken for SNRs (see Beuther et al., 2016, their Figs. 7 and 8). To reduce the chance of identifying artifacts, we verify that all identified SNR candidates near large star formation complexes are also detected in the NVSS (Condon et al., 1998) or MAGPIS surveys. Due to the higher probability that a radio continuum feature is thermally emitting ionized gas or an interferometric artifact, we are conservative in our identifications around large star formation complexes.

We classify the radio continuum morphology of each SNR candidate as “shell,” for those with well-defined radio continuum shells, “filled,” for those lacking an outer shell but emission filling a roughly circular region, or “composite” for those that have a shell with a filled interior. For seven of the smallest regions, the THOR+VGPS resolution is insufficient to determine their morphological classification.

4 Results

We identify 76 new Galactic SNR candidates, and detect the radio continuum emission from 52 of 53 previously-known SNRs from the G14 catalog. In our aperture photometry measurements, we create a circular aperture that encloses the radio continuum emission of each source and therefore define the centroid and radius of each region. We give parameters of the new SNR candidates in Table 4, which lists the Galactic longitude, Galactic latitude, and radius, as defined in THOR+VGPS data, the 1.4 $\,{\rm GHz}$ THOR+VGPS flux density and its uncertainty, the radio continuum morphological type, and the name from H06 if the same source was identified there. Seven SNR candidates are so confused with nearby radio continuum sources that their flux densities are unreliable; we do not list flux densities for these seven regions. We give the parameters of the G14 regions in Table LABEL:tab:green, which has the same columns as Table 4 but additionally contains the $1\,\,{\rm GHz}$ flux density and spectral index ($\alpha$) from the G14 catalog. We show THOR+VGPS and MIR two-color images for example SNR candidates in Fig. 2, and for all candidates in the Appendix. We plot the Galactic locations of all known and candidate SNRs in Fig. 3.