Surface Properties of Near-Sun Asteroids

Many comets and asteroids spend part of their dynamical lifetimes with small perihelion distances (or "low-q") as a result of dynamical interactions with Jupiter (e.g., Bailey et al. 1992; Farinella et al. 1994; Gladman et al. 1997; Bottke et al. 2002; Marchi et al. 2009; Greenstreet et al. 2012). However, even while assuming a loss due to collisions with the Sun or planets or an escape from the inner solar system, Granvik et al. (2016) reported a deficit of asteroids with small perihelion distances (q ≲ 0.5 au), which they attributed to "supercatastrophic disruption" on timescales of less than 250 yr when an asteroid reaches a perihelion distance of less than q ∼ 0.076 au, with some dependence on asteroid size. Based on the evolution of debris streams, Ye & Granvik (2019) suggested that disruption is a more gradual process with timescales of 1–20 kyr. Granvik et al. (2016) determined that the disruption of near-Sun asteroids cannot be explained by tidal effects or thermal vaporization. Potential near-Sun processes that might lead to disruption over time (e.g., thermal cracking, spin-up, and subsurface volatile release) likely cause surface alteration, which might be observable from the ground using optical telescopes.

Low-q comets 322P/SOHO 1, 96P/Machholz 1, and 323P/SOHO have been observed to have atypical blue colors (Knight et al. 2016; Eisner et al. 2019; Hui et al. 2022). Comet 322P/SOHO 1 has a cometary orbit and shows evidence of being active at perihelion (Lamy et al. 2013), but it has an asteroid-like albedo, density, and fast rotation. Comet 96P/Machholz 1 had the smallest perihelion distance of any unambiguously active short-period comet until recent observations of 323P/SOHO, which was observed postperihelion with a long, narrow tail, likely caused by immense thermal stress or rotational instability; 323P has the shortest rotational period of the known comets in our solar system at 0.522 hr. Comet 323P/SOHO 1 was recently observed to have bluer colors than most solar system small bodies, which changed unprecedentedly over a few weeks to a color unlike any other small bodies in our solar system (Hui et al. 2022). Further characterization of objects with small perihelion distances is necessary to assess whether these comets are unique or if their bluer colors are typical of objects that closely approach the Sun. Very few periodic comets are observed from the ground with a perihelion distance less than 0.15 au, so additional observations must focus on asteroids.

Spectral trends with perihelion distance have been observed for S- and Q-type near-Earth asteroids (NEAs) with q ≳ 0.2 au. Marchi et al. (2006a) observed that S- and Q-types become spectrally bluer with decreasing perihelion distance using data from Binzel et al. (2004) and Lazzarin et al. (2004, 2005), which they attributed to resurfacing caused by close encounters with planets. Because of their closely related spectra, only different in features impacted by space weathering (spectral slope and 1 μm band depth), Q-types are best explained as resurfaced S-types that have not yet had enough time for space weathering to alter their surface (e.g., Chapman 2004; Brunetto et al. 2015). In agreement with Marchi et al. (2006a), DeMeo et al. (2014) and Devogèle et al. (2019) found a higher percentage of Q-type asteroids (which are spectrally bluer than S-types) with smaller perihelion distances. Graves et al. (2019) argued that resurfacing due to thermal degradation explains the trend better than planetary encounters after modeling NEA orbits and tracking spectral slope with planetary encounters or physical distance from the Sun for each object. More observations of near-Sun asteroids are needed to determine if this trend holds at smaller perihelion distances.

This work presents an observational study of known near-Sun asteroids and searches for any common properties that might be related to the near-Sun processes experienced by these objects. We focus our efforts on objects with perihelion distance q ≤ 0.15 au. We chose this limit because 0.15 au is the outer limit of the Solar and Heliospheric Observatory's (SOHO) outermost coronagraph's (C3) field of view (FOV), meaning we can potentially view the activity or disruption of these objects using SOHO. There were 53 known asteroids that reach perihelion at q ≤ 0.15 au (subsolar temperatures ≳1000 K) as of 2021 November 1, including the ones with poorly constrained orbits. All of the objects reach perihelion within the SOHO FOV each orbit, yet none have been observed to date, meaning that mass loss near perihelion has been very low over the last quarter century (to be explored further in Section 4.3).

Campins et al. (2009) and Jewitt et al. (2013) investigated a similar set of asteroids, but their perihelion distance limits were significantly larger (q ≤ 0.35 and 0.25 au, respectively). Only seven of their objects are in our sample. Most low-q asteroids are small and therefore faint and difficult to study. In-depth studies have not been possible for low-q objects other than 3200 Phaethon. While there have been a few serendipitous observations as part of general near-Earth object (NEO) studies, the available data are still limited, as detailed below and summarized in Table 1.

Table 1. Published Measurements of Near-Sun Asteroids ^a

Object	B − V	V − R	B − R	Ref.	Rotation Rate (hr)	Ref.	Albedo	Size [km]	Ref.
1995 CR	⋯	⋯	⋯	⋯	2.66 ± 0.04	1	$0.167{\pm }_{0.079}^{0.101}$	$0.129{\pm }_{0.024}^{0.045}$	2
2000 BD19	⋯	⋯	⋯	⋯	10.570 ± 0.005	3	0.247 ± 0.046	0.97 ± 0.04	4
							$0.123{\pm }_{0.066}^{0.082}$	$1.149{\pm }_{0.241}^{0.513}$	5
2000 LK	⋯	⋯	⋯	⋯	⋯	⋯	$0.137{\pm }_{0.074}^{0.092}$	$0.610{\pm }_{0.132}^{0.278}$	5
2002 AJ129	0.687 ± 0.050	0.405 ± 0.027	1.092 ± 0.057	6	3.9222 ± 0.0008	6	$0.226{\pm }_{0.116}^{0.141}$	$0.438{\pm }_{0.091}^{0.181}$	5
	⋯	⋯	1.23 ± 0.10	7	⋯	⋯	⋯	⋯	⋯
2002 PD43	0.66 ± 0.05	0.42 ± 0.05	1.08 ± 0.03	7	⋯	⋯	⋯	⋯	⋯
2004 UL	0.82 ± 0.10	0.54 ± 0.10	1.37 ± 0.10	7	38 ± 2	8	$0.604{\pm }_{0.264}^{0.258}$	$0.268{\pm }_{0.042}^{0.083}$	5
2004 XY60	⋯	⋯	⋯	⋯	⋯	⋯	$0.217{\pm }_{0.110}^{0.137}$	$0.389{\pm }_{0.080}^{0.161}$	5
2006 HY51	⋯	⋯	⋯	⋯	3.350 ± 0.008	9	0.157 ± 0.071	1.218 ± 0.23	4
2006 TC	0.60 ± 0.08	0.33 ± 0.03	0.93 ± 0.08	7	⋯	⋯	⋯	⋯	⋯
2007 EP88	⋯	⋯	⋯	⋯	⋯	⋯	0.174 ± 0.038	0.636 ± 0.04	4
							$0.531{\pm }_{0.230}^{0.236}$	$0.312{\pm }_{0.051}^{0.098}$	5
2008 HE	⋯	⋯	⋯	⋯	⋯	⋯	$0.120{\pm }_{0.066}^{0.091}$	$0.779{\pm }_{0.184}^{0.375}$	10
2008 MG1	⋯	⋯	⋯	⋯	⋯	⋯	$0.431{\pm }_{0.223}^{0.252}$	$0.194{\pm }_{0.038}^{0.084}$	10
2008 XM	⋯	⋯	⋯	⋯	⋯	⋯	0.128 ± 0.032	0.367 ± 0.01	4
2010 JG87	⋯	⋯	⋯	⋯	⋯	⋯	0.202 ± 0.040	0.408 ± 0.02	4
2011 KE	⋯	⋯	⋯	⋯	8.0 ± 0.1	11	⋯	⋯	⋯
2011 XA3	⋯	0.473 ± 0.051 b	⋯	12	0.730 ± 0.007	12	$0.347{\pm }_{0.163}^{0.196}$	$0.163{\pm }_{0.029}^{0.056}$	10
2017 AF5	⋯	⋯	⋯	⋯	49.68 ± 0.06	13	⋯	⋯	⋯
3200 Phaethon	0.67 ± 0.02	0.32 ± 0.02	0.99 ± 0.02	7	3.6039 ± 0.0002	14	0.16 ± 0.02	4.6 ± 0.3	15

Notes.

^a All uncertainty values are presumed 1σ errors. ^b Converted from $g^{\prime} -r^{\prime}$ according to Jordi et al. (2006).

References—(1) Warner (2014); (2) Trilling et al. (2016); (3) Warner (2015a); (4) Mainzer et al. (2011, 2016); (5) Trilling et al. (2010); (6) Devyatkin et al. (2022); (7) Jewitt (2013); (8) Warner (2015b); (9) this work; (10) Trilling et al. (2016); (11) B. A. Skiff, 2011 (posting on CALL website; https://minorplanet.info/php/call.php); (12) Urakawa et al. (2014); (13) Warner (2017); (14) Warner (2015b); (15) Masiero et al. (2019).

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1.3.1. 3200 Phaethon

1.3.2. Albedos

1.3.3. Rotation Periods

1.3.4. Spectral Properties

Near-Sun asteroids have not been thoroughly examined as a population. Color observations can provide us with a better understanding of the processes occurring and the effects they have on near-Sun asteroids. Broadband optical colors (e.g., $g^{\prime} -r^{\prime}$, $r^{\prime} -i^{\prime}$) can be obtained quickly and for fainter objects, which makes such observations optimal for a population study. Over more than 3 yr of observations from 2017 January to 2020 March, we attempted to observe as many near-Sun asteroids as possible. Of the 53 known asteroids with q ≤ 0.15 au (summary of orbital elements in Table 2), we attempted to observe 35 and successfully observed 22. Nine of the objects we attempted to observe were predicted to be within our FOVs and above our detection limit but were not recovered, most likely due to the very large uncertainty in their orbits. We also observed NEA 2004 LG, which previously spent 2500 yr with a perihelion distance less than 0.076 au, the disruption limit of Granvik et al. (2016), experiencing extreme temperatures of ∼2500 K at the surface (Vokrouhlický & Nesvorný 2012; Wiegert et al. 2020).

Data were collected primarily using Lowell Observatory's 4.3 m Lowell Discovery Telescope (LDT; formerly known as the Discovery Channel Telescope) and the 4.1 m Southern Astrophysical Research (SOAR) telescope, supplemented by data from the Isaac Newton Telescope (INT) and Lowell Observatory's 42- and 31-inch telescopes. Broadband Sloan Digital Sky Survey (SDSS) g', r', i', and z' filters were used at all telescopes except for Lowell Observatory's 31- and 42-inch, where Johnson–Cousins B, V, R, and I filters were used. A summary of the instruments used can be found in Table 3. All images were acquired at the asteroid's ephemeris rate, which frequently resulted in trailed stars. Whenever possible, we kept the star trails to less than twice the seeing in order to accurately register the image (see Section 2.4), but for some objects, long trails were unavoidable. As will be discussed later, this occasionally hampered absolute calibrations. Exposure times varied with the asteroid's brightness and telescope size but were typically 180–300 s. The number of images acquired for each target varied with the time available, though at least two cycles of filters were achieved for all observations, ordered in a way so that the mid-time of the observations for each filter is approximately the same to mitigate rotational variability.

Table 3. Instruments

Telescope	Instrument	Aper. Size (m)	FOV	Pixel Scale a	Binning	Filters
LDT	LMI	4.3	$12\buildrel{\,\prime}\over{.} 3\times 12\buildrel{\,\prime}\over{.} 3$	$0\buildrel{\prime\prime}\over{.} 36$	3 × 3	$g^{\prime} ,r^{\prime} ,i^{\prime} ,z^{\prime}$
SOAR	Goodman Spectrograph	4.1	$7\buildrel{\,\prime}\over{.} 2$ diameter	$0\buildrel{\prime\prime}\over{.} 30$	2 × 2	$g^{\prime} ,r^{\prime} ,i^{\prime} ,z^{\prime}$
SOAR	SOI	4.1	$5\buildrel{\,\prime}\over{.} 26\times 5\buildrel{\,\prime}\over{.} 26$	$0\buildrel{\prime\prime}\over{.} 077$	2 × 2	$g^{\prime} ,r^{\prime} ,i^{\prime} ,z^{\prime}$
INT	WFC	2.5	$11\buildrel{\,\prime}\over{.} 5\times 23\buildrel{\,\prime}\over{.} 0$	$0\buildrel{\prime\prime}\over{.} 33$	None	$g^{\prime} ,r^{\prime} ,i^{\prime} ,z^{\prime}$
Lowell Observatory 31-inch	NASAcam	0.8	$15\buildrel{\,\prime}\over{.} 7\times 15\buildrel{\,\prime}\over{.} 7$	$0\buildrel{\prime\prime}\over{.} 46$	None	B, V, R, I
Lowell Observatory 42-inch	NASA42 Camera	1.1	$25\buildrel{\,\prime}\over{.} 3\times 25\buildrel{\,\prime}\over{.} 3$	$1\buildrel{\prime\prime}\over{.} 48$	3 × 3	B, V, R, I

Note.

^a Effective pixel scale after binning.

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Northern hemisphere targets were primarily observed with Lowell Observatory's 4.3 m LDT located near Flagstaff, Arizona. All observations were made using the Large Monolithic Imager (LMI; Massey et al. 2013). The LMI has an FOV of 123 × 123 and a pixel scale of 036 after an on-chip 3 × 3 binning. SDSS broadband $g^{\prime}$, $r^{\prime}$, $i^{\prime}$, and $z^{\prime}$ filters were used.

Observations of southern hemisphere targets were made using the 4.1 m SOAR telescope on Cerro Pachón in Chile. The Goodman Spectrograph Red Camera (Clemens et al. 2004) and the SOAR Optical Imager (SOI) were used. The Goodman Spectrograph camera has a circular FOV of 72 diameter. The observations were done in 2 × 2 binning mode with an effective pixel scale of 030. The SOI uses two adjacent CCD chips read out through two amplifiers per chip that cover a 526 × 526 FOV and have a scale of 0077 pixel^–1 after 2 × 2 binning. Broadband $g^{\prime}$, $r^{\prime}$, $i^{\prime}$, and $z^{\prime}$ SDSS filters were used on all images.

We conducted an observing run in 2017 January using the 2.5 m INT at the Rogue de Los Muchachos Observatory in La Palma, Spain. Observations were made using the Wide Field Camera (WFC), which has an effective FOV of 115 × 230 and a pixel scale of 033. All observations used broadband $g^{\prime}$, $r^{\prime}$, $i^{\prime}$, and $z^{\prime}$ SDSS filters.

Supplemental observations were made using Lowell Observatory's Hall 31-inch (0.8 m) and 42 inch (1.1 m) telescopes. The 31-inch telescope has a square FOV of 157 on a side and an unbinned pixel scale of 046. The 42 inch telescope has a square FOV of 253 on a side and a 3 × 3 binned pixel scale of 098. Observations using the 31-inch were robotically acquired. All other telescopes used the classic observing mode. Johnson–Cousins B, V, R, and I filters were used.

Each observation followed the same reduction routine. A master bias frame for each night was created by averaging 10–20 individual bias frames. The master bias frame was then subtracted from each frame. At least five sky or dome flats taken during the same observing period were normalized and median combined for each filter used. Images were flat-field corrected by dividing each frame by the median-combined flat field.

2.4.1. Absolute Calibration

We estimated the absolute magnitude (H(1, 1, 0) or H), which is the apparent V-band magnitude at 1 au from the Sun and Earth, observed at a phase angle of 0°. For observations made using SDSS filters, the $g^{\prime}$ magnitude value was converted to V magnitude (m_V ) according to Jordi et al. (2006). To determine the absolute magnitude, we used the formula

$$\begin{eqnarray}&&H(1,1,0)={m}_{V}-5{\mathrm{log}}_{10}({\rm{\Delta }}{r}_{{\rm{h}}})+2.5{\mathrm{log}}_{10}(\phi (\alpha )),\end{eqnarray} \tag{ 1 }$$

where Δ equals the geocentric distance in au, r_h equals the heliocentric distance in au, α is the phase angle (the Sun–target–observer angle), and ϕ(α) is the phase integral, which is the ratio of the brightness at phase angle α to that at phase angle 0°. For the majority of the observed low-q asteroids, we used HG formalism (Bowell et al. 1989), with G = 0.15 to determine the phase integral. Asteroid 2002 AJ129 was the only object with enough phase coverage to determine the phase integral using the three-parameter magnitude phase function (HG₁ G₂; Muinonen et al. 2010), which we then used to determine a more accurate H (Section 3.5).

The SDSS color filters provide sufficient wavelength coverage to study spectral slope trends (e.g., Thomas et al. 2013; Graves et al. 2018; Thomas et al. 2021). We followed techniques used by DeMeo & Carry (2013) and Thomas et al. (2013) to calculate the spectral slope over the $g^{\prime}$, $r^{\prime}$, and $i^{\prime}$ reflectance values (hereafter the gri -slope) to represent the slope of the continuum and $z^{\prime} -i^{\prime}$ color representing the depth of the possible 1 μm band. First, known Sloan filter solar colors ⁹ ($g^{\prime} -r^{\prime} =0.44$, $g^{\prime} -i^{\prime} =0.55$, and $g^{\prime} -z^{\prime} =0.58$ mag) were removed from the color indices. Then, the Sun-subtracted color indices were converted into reflectance values, normalized to the $g^{\prime}$ band:

$$\begin{eqnarray}&&\displaystyle \frac{{R}_{x}}{{R}_{g}}={10}^{0.4[({m}_{g}-{m}_{x})-({m}_{g,\odot }-{m}_{x,\odot })]}.\end{eqnarray} \tag{ 2 }$$

The error for each reflectance point is calculated using standard error propagation. The R_g value does not have an error, as the other values are always in relation to R_g = 1.

A linear regression was fit to the three photometric points using the central wavelength of the filters to calculate the slope. We computed the slope errors for each object via a Monte Carlo calculation, a technique used by Thomas et al. (2013, 2021). For each object, the individual reflectance values were modified by applying an offset of a random number pulled from a Gaussian distribution where the standard deviation is the 1σ error of the reflectance value. This calculation was done 20,000 times for each object, and a slope was determined for each altered spectrum. The uncertainty of the slope was the standard deviation of the altered slopes generated by this process.

We find our derived absolute magnitude values to be mostly consistent with those reported by the Jet Propulsion Laboratory's (JPL) Small-Body Database ¹⁰ with a standard deviation of the difference equal to ∼0.3 (Figure 1). An offset in absolute magnitude larger than the uncertainty due to rotational variability (${A}_{\max }\lesssim$ 1 mag for small asteroids; Statler et al. 2013) could indicate unresolved activity during one epoch. However, we do not observe any such offset and will explore the upper limits of the activity in Section 4.3.

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Our measured colors are summarized in Table 4 and plotted in Figure 2. For near-Sun asteroids, we see a wide variety of colors with large overlap between the near-Sun distribution and the colors reported by Dandy et al. (2003) and SDSS. Some objects have error bars spanning a broad spectrum, which we attribute to faint objects observed under less than ideal conditions after inspecting each frame individually to rule out uncertainty due to the occasional background star or poor photometric solutions. In Figure 2, we also separate objects by Tisserand parameter, T_J. Most main-belt asteroids have T_J > 3, most Jupiter family comets (JFCs) have 2 < T_J < 3, and long-period comets have T_J < 2. Therefore, T_J, which is related to an object's encounter velocity with Jupiter, can generally be used to distinguish between types of orbits (Tisserand 1889; Kresák 1972; Carusi et al. 1987; Levison 1996). The red circles have a cometary orbit (T_J < 3), and the blue squares have an asteroidal orbit (T_J > 3). We do not see a clear trend with color versus T_J. The two objects with the smallest spectral slope in the lower left of the figure are 2011 CP4 and 2019 UJ12. There are several objects with $r^{\prime} -i^{\prime}$ colors most similar to V-types, but some of their $g^{\prime} -r^{\prime}$ colors are more red.

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Table 4. Summary of Observations and Measured Magnitudes

Object	UT Date	Tel. a	rh b	Δ c	αd	Filters	HV e	$g^{\prime}$	$r^{\prime}$	$i^{\prime}$	$z^{\prime}$	$g^{\prime} -r^{\prime}$	$r^{\prime} -i^{\prime}$	$i^{\prime} -z^{\prime}$	gri -slope f
1995 CR	2017 Jan 25	INT	1.415	0.520	27.700	$g^{\prime} ,r^{\prime} ,i^{\prime}$	21.87	22.826 ± 0.092	22.169 ± 0.084	21.928 ± 0.181	⋯	0.657 ± 0.124	0.242 ± 0.199	⋯	1.36 ± 1.02
2000 BD19	2017 Jan 26	INT	1.177	0.606	56.700	$g^{\prime} ,r^{\prime} ,i^{\prime} ,z^{\prime}$	17.53	19.260 ± 0.008	18.554 ± 0.003	18.551 ± 0.008	19.120 ± 0.034	0.706 ± 0.008	0.003 ± 0.008	−0.569 ± 0.035	0.59 ± 0.08
	2020 Jan 28	31 in	1.103	0.272	57.788	B, V, R, I	17.60	⋯	17.071 ± 0.007	16.654 ± 0.007	16.494 ± 0.009	0.547 ± 0.023	−0.074 ± 0.016	⋯	−0.23 ± 0.11
	2020 Jan 30	31 in	1.077	0.237	61.215	B, V, R, I	17.40	17.624 ± 0.057	16.621 ± 0.037	16.265 ± 0.035	16.317 ± 0.054	0.447 ± 0.092	−0.288 ± 0.070	⋯	−1.06 ± 0.26
	2020 Jan 31	31 in	1.064	0.220	63.355	B, V, R, I	17.24	17.378 ± 0.043	16.337 ± 0.038	15.888 ± 0.033	16.119 ± 0.034	0.600 ± 0.090	−0.468 ± 0.053	⋯	−1.10 ± 0.23
2000 LK	2019 Jun 25	SOAR	2.421	1.839	22.766	$g^{\prime} ,r^{\prime} ,i^{\prime}$	18.50	23.223 ± 0.051	22.555 ± 0.037	22.516 ± 0.065	⋯	0.668 ± 0.063	0.040 ± 0.075	⋯	0.58 ± 0.36
	2020 Mar 1	LDT	2.091	1.373	23.257	$g^{\prime} ,r^{\prime} ,i^{\prime} ,z^{\prime}$	18.81	22.548 ± 0.048	21.955 ± 0.052	22.073 ± 0.096	⋯	0.592 ± 0.071	−0.118 ± 0.109	⋯	−0.22 ± 0.40
2002 AJ129	2018 Feb 9	LDT	1.077	0.093	14.192	$g^{\prime} ,r^{\prime} ,i^{\prime} ,z^{\prime}$	18.73	14.785 ± 0.013	14.250 ± 0.009	14.203 ± 0.007	14.522 ± 0.009	0.535 ± 0.015	0.048 ± 0.011	−0.319 ± 0.012	0.12 ± 0.09
	2018 Feb 10	LDT	1.091	0.109	17.083	$g^{\prime} ,r^{\prime} ,i^{\prime} ,z^{\prime}$	18.73	15.245 ± 0.008	14.680 ± 0.007	14.658 ± 0.006	14.956 ± 0.008	0.565 ± 0.010	0.022 ± 0.009	−0.298 ± 0.010	0.14 ± 0.08
2002 PD43	2018 Jun 25	SOAR	1.239	0.836	54.575	$g^{\prime} ,r^{\prime} ,i^{\prime} ,z^{\prime}$	19.38	21.795 ± 0.029	21.211 ± 0.028	21.209 ± 0.040	⋯	0.583 ± 0.040	0.003 ± 0.049	⋯	0.14 ± 0.22
2004 UL	2019 Jun 25	SOAR	1.631	1.793	34.124	$g^{\prime} ,r^{\prime} ,i^{\prime} ,z^{\prime}$	18.73	22.727 ± 0.115	22.316 ± 0.077	22.277 ± 0.102	⋯	0.411 ± 0.139	0.039 ± 0.128	⋯	−0.31 ± 0.56
2006 HY51	2019 Mar 29	42 in	2.585	2.643	22.387	B, V, R, I	17.05	19.133 ± 0.029	18.309 ± 0.016	17.871 ± 0.004	17.562 ± 0.047	0.581 ± 0.035	0.076 ± 0.052	⋯	0.38 ± 0.23
	2019 Mar 31	42 in	1.470	0.660	34.530	$g^{\prime} ,r^{\prime} ,i^{\prime} ,z^{\prime}$	17.22	18.960 ± 0.004	18.326 ± 0.004	18.218 ± 0.005	18.578 ± 0.007	0.634 ± 0.006	0.109 ± 0.006	−0.360 ± 0.008	0.71 ± 0.08
2006 TC	2019 Oct 21	SOAR	1.446	0.878	42.540	$g^{\prime} ,r^{\prime} ,i^{\prime} ,z^{\prime}$	18.73	21.636 ± 0.143	20.902 ± 0.029	21.113 ± 0.056	⋯	0.523 ± 0.153	0.211 ± 0.064	⋯	0.66 ± 0.64
2007 EP88	2017 Apr 7	SOAR	1.571	0.664	23.890	$g^{\prime} ,r^{\prime} ,i^{\prime} ,z^{\prime}$	18.70	20.280 ± 0.017	19.654 ± 0.010	19.502 ± 0.009	20.115 ± 0.027	0.626 ± 0.019	0.152 ± 0.013	−0.613 ± 0.028	0.85 ± 0.12
	2020 Jan 2	SOAR	0.962	0.663	72.030	$g^{\prime} ,r^{\prime} ,i^{\prime} ,z^{\prime}$	18.52	20.442 ± 0.019	19.805 ± 0.016	19.643 ± 0.016	⋯	0.637 ± 0.025	0.162 ± 0.023	⋯	0.93 ± 0.16
2008 HE	2018 Mar 9	LDT	1.638	1.697	34.584	$g^{\prime} ,r^{\prime} ,i^{\prime} ,z^{\prime}$	17.49	21.465 ± 0.017	20.924 ± 0.017	20.928 ± 0.015	20.853 ± 0.026	0.541 ± 0.024	−0.004 ± 0.023	0.075 ± 0.030	−0.03 ± 0.13
2008 HW1	2017 Apr 7	SOAR	2.937	1.995	8.014	$g^{\prime} ,r^{\prime} ,i^{\prime} ,z^{\prime}$	17.97	22.705 ± 0.264	22.137 ± 0.105	21.878 ± 0.077	⋯	0.568 ± 0.284	0.259 ± 0.130	⋯	1.04 ± 1.31
	2017 Jan 24	INT	2.194	1.802	26.249	$g^{\prime} ,r^{\prime} ,i^{\prime} ,z^{\prime}$	17.29	21.845 ± 0.031	21.208 ± 0.014	21.142 ± 0.040	⋯	0.637 ± 0.034	0.066 ± 0.043	⋯	0.56 ± 0.22
2008 MG1	2017 Jul 1	SOAR	0.721	1.692	8.361	$g^{\prime} ,r^{\prime} ,i^{\prime} ,z^{\prime}$	18.87	20.218 ± 0.055	19.646 ± 0.035	19.576 ± 0.041	19.684 ± 0.094	0.572 ± 0.065	0.070 ± 0.054	−0.108 ± 0.103	0.33 ± 0.31
	2019 Jun 25	INT	1.371	0.373	15.476	$g^{\prime} ,r^{\prime} ,i^{\prime} ,z^{\prime}$	20.02	19.745 ± 0.009	19.197 ± 0.006	19.177 ± 0.014	19.260 ± 0.017	0.547 ± 0.010	0.020 ± 0.015	−0.083 ± 0.022	0.07 ± 0.09
2008 XM	2018 Jan 20	LDT	1.506	0.596	22.826	$g^{\prime} ,r^{\prime} ,i^{\prime} ,z^{\prime}$	20.29	21.476 ± 0.022	20.917 ± 0.002	20.629 ± 0.020	21.021 ± 0.065	0.558 ± 0.030	0.288 ± 0.029	−0.392 ± 0.068	1.11 ± 0.19
2010 JG87	2019 Aug 26	LDT	1.785	1.683	33.732	$g^{\prime} ,r^{\prime} ,i^{\prime} ,z^{\prime}$	19.35	23.520 ± 0.094	22.885 ± 0.062	22.890 ± 0.138	⋯	0.635 ± 0.113	−0.005 ± 0.152	⋯	0.30 ± 0.67
2011 CP4	2017 Jan 24	INT	1.701	0.727	7.423	$g^{\prime} ,r^{\prime} ,i^{\prime} ,z^{\prime}$	20.84	22.079 ± 0.042	21.684 ± 0.027	21.797 ± 0.078	⋯	0.395 ± 0.050	−0.113 ± 0.082	⋯	−0.77 ± 0.25
2011 KE	2018 Apr 14	LDT	1.755	0.800	14.953	$g^{\prime} ,r^{\prime} ,i^{\prime} ,z^{\prime}$	19.69	21.528 ± 0.020	21.085 ± 0.022	20.957 ± 0.028	21.444 ± 0.072	0.443 ± 0.029	0.128 ± 0.035	−0.487 ± 0.077	0.07 ± 0.16
	2018 Apr 15	LDT	1.768	0.820	15.624	$g^{\prime} ,r^{\prime} ,i^{\prime} ,z^{\prime}$	19.69	21.645 ± 0.021	21.157 ± 0.022	21.111 ± 0.030	21.512 ± 0.082	0.488 ± 0.031	0.046 ± 0.037	−0.400 ± 0.087	−0.04 ± 0.17
2013 YC	2017 Dec 17	LDT	1.431	0.447	0.856	$g^{\prime} ,r^{\prime} ,i^{\prime} ,z^{\prime}$	21.48	20.986 ± 0.051	20.416 ± 0.086	20.284 ± 0.072	20.544 ± 0.054	0.570 ± 0.100	0.131 ± 0.112	−0.260 ± 0.090	0.54 ± 0.56
2017 AF5	2017 Jan 24	INT	1.549	0.581	10.935	$g^{\prime} ,r^{\prime} ,i^{\prime} ,z^{\prime}$	17.90	18.685 ± 0.007	18.137 ± 0.012	18.102 ± 0.010	18.230 ± 0.029	0.547 ± 0.014	0.036 ± 0.016	−0.128 ± 0.030	0.12 ± 0.10
2018 GG5	2018 Apr 23	LDT	1.276	0.392	39.622	$g^{\prime} ,r^{\prime} ,i^{\prime} ,z^{\prime}$	19.86	20.232 ± 0.056	19.710 ± 0.055	19.578 ± 0.074	19.935 ± 0.135	0.523 ± 0.079	0.131 ± 0.092	−0.356 ± 0.154	0.36 ± 0.44
2019 AM13	2020 Mar 1	LDT	1.638	0.657	7.810	$g^{\prime} ,r^{\prime} ,i^{\prime} ,z^{\prime}$	22.00	23.037 ± 0.073	22.496 ± 0.054	22.334 ± 0.152	20.595 ± 0.079	0.540 ± 0.091	0.163 ± 0.161	⋯	0.54 ± 0.68
2019 UJ12	2019 Nov 4	LDT	1.145	0.192	34.023	$g^{\prime} ,r^{\prime} ,i^{\prime} ,z^{\prime}$	22.61	20.984 ± 0.012	20.572 ± 0.026	20.635 ± 0.123	⋯	0.412 ± 0.029	−0.063 ± 0.126	0.013 ± 0.166	−0.60 ± 0.35
Phaethon	2017 Jan 25	INT	2.154	2.307	25.206	$g^{\prime} ,r^{\prime} ,i^{\prime} ,z^{\prime}$	14.21	19.071 ± 0.011	18.703 ± 0.007	18.536 ± 0.010	18.617 ± 0.037	0.368 ± 0.013	0.167 ± 0.012	−0.081 ± 0.039	−0.06 ± 0.09
2004 LG g	2019 Mar 31	LDT	1.719	1.711	33.862	$g^{\prime} ,r^{\prime} ,i^{\prime}$	17.67	21.754 ± 0.041	21.178 ± 0.039	21.355 ± 0.078	⋯	0.568 ± 0.058	−0.012 ± 0.100	⋯	⋯

Notes.

^a Telescope used: LDT 4.3 m, 42 in = Hall-42 inch telescope (1.1 m), 31 in = 31-inch telescope (0.8 m), INT 2.5 m, SOAR 4.1 m ^b Heliocentric distance, au. ^c Geocentric distance, au. ^d Phase angle, degrees. ^e Absolute V magnitude measured by this work. ^f The slope of the reflectance values over the g, r, and i bandpasses [%/μm] ^g Object 2004 LG was formerly low-q (Vokrouhlický & Nesvorný, 2012).

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We compared the distribution of colors of low-q objects to the range for NEAs using the SDSS Moving Object Catalog (MOC; Ivezić et al. 2001, 2002), which observed 471,569 moving objects through 2007 March, using five filters, $u^{\prime}$, $g^{\prime}$, $r^{\prime}$, $i^{\prime}$, and $z^{\prime}$. We restricted the sample from the SDSS MOC database according to the criteria outlined in DeMeo & Carry (2013, Section 2.1), which include filtering faint data, data with large errors, and data with flags relevant to moving objects and good photometry. We further reduce the sample by only including objects that are categorized as NEAs (q ≤ 1.3 au). Applying the selection criteria, we are left with a sample of 42. We plot the range of colors as a gray crosses behind our low-q measurements and the average colors of NEA types observed by Dandy et al. (2003) to give context for our findings. We also included the colors of 322P/SOHO 1 (Knight et al. 2016), 323P/SOHO (Hui et al. 2022), and solar colors.

In the color–color plot (Figure 2), we observe that low-q asteroids have a bluer distribution compared to average NEA colors (more in the lower left than the upper right). To investigate this further, we used a kernel density estimation (KDE) plot of the colors (Figure 3). Rather than using discrete bins, a KDE plot smooths the measurements with a Gaussian kernel, producing a continuous probability density estimate. The data points are weighted by their 1σ uncertainty, and the kernel bandwidth is selected according to Scott's rule (Scott 1979). The result is shown in Figure 3. The low-q $g^{\prime} -r^{\prime}$ colors appear to have the same distribution as the colors from SDSS MOC but shifted bluer. The low-q $r^{\prime} -i^{\prime}$ colors show a wider distribution than the SDSS MOC, but they extend to bluer colors. The KDE plots confirm our interpretation of the color–color plot.

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The reflectance values for each observation can be seen in Figure 4. We use the reflectance values to determine the gri -slope and $z^{\prime} -i^{\prime}$ color, representing the slope of the continuum and the 1 μm band depth, respectively. Both of these parameters are affected by space weathering, which causes a steeper spectral slope and shallower 1 μm band depth. In Figure 5, we compare our sample to NEAs from SDSS MOC values and class boundaries reported by DeMeo & Carry (2013). We find that near-Sun asteroids have a shallower spectral slope compared to the NEA population, especially those with V-type colors, which agrees with our findings of bluer colors shown in Figure 2. The $z^{\prime} -i^{\prime}$ reflectance values are smaller as well, consistent with a steeper 1 μm band depth.

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We compare the low-q spectral slopes with the trend versus perihelion distance seen in S- and Q-type NEAs with q ≥ 0.2 au by Marchi et al. (2006b) and Graves et al. (2019) to determine if the same trend might extend to smaller perihelion distances (Figure 6). We can make this comparison because we anticipate our sample to be predominately S- or Q-type because together they are the most common NEA types (Binzel et al. 2019) and based on dynamical modeling of source regions, which we discuss in Section 3.6. For our comparison, we used the trend measured by Graves et al. (2019), who implemented a windowed moving average, instead of the similar trend determined by Marchi et al. (2006a), who implemented a point-based moving average. Our data are consistent with the extended trend at the 1σ level. However, given the large uncertainty in some of the slopes in our data set and the short range of q, the trend is virtually indistinguishable from a flat distribution, indicative of no spectral slope change with perihelion distance. Further investigation is warranted.

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We compiled high-cadence light curves of three bright objects (394130 (2006 HY51), 276033 (2002 AJ129), and 137924 (2000 BD19)) in our sample in order to measure their rotation periods and amplitudes. The methodology for determining the rotational period follows our approach in earlier papers (e.g., Knight et al. 2011, 2012; Eisner et al. 2017). Because the observing geometry changed rapidly throughout the night, we corrected our photometric results for the geometric circumstances image by image. The rotation period was estimated by superimposing the light curves from all nights with the data phased to a "trial" period and zero phase at perihelion. We then iterated the trial period, making direct "better or worse" comparisons between each iteration, until a rotation period estimation was constrained by eye. We estimate the uncertainty by determining how much the period can be adjusted before a phased light curve appears obviously incorrect.

3.4.1. 2006 HY51

Object 2006 HY51, whose rotation period was previously unconstrained, was observed over 3 nights (2019 March 27–29) using Lowell Observatory's 42-inch telescope and the Johnson R filter. We collected ∼6 hr of data each night with frames every ∼300 s. We measured a rotation period of 3.350 ± 0.008 hr and a maximum peak-to-trough amplitude of ∼0.2 mag (Figure 7). The light curve has an interesting shape, with one peak larger than the other, suggesting that the shape of the asteroid deviates substantially from a triaxial ellipsoid (Magnusson 1986).

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3.4.2. 2002 AJ129

Object 2002 AJ129 was observed over 2 nights (2018 February 6 and 7) using Lowell Observatory's 31-inch telescope in robotic mode and the Johnson R filter, collecting ∼9 hr of data each night with frames every ∼90 s. We found a rotational period of 3.918 ± 0.010 hr and a peak-to-trough amplitude of ∼0.15 mag (Figure 8). The prepublished period of 2002 AJ129 was reported by the Ondrejov Asteroid Photometry Project (Pravec et al. 2018) ¹¹ as 3.9226 ± 0.0007, and Devyatkin et al. (2022) reported a period of 3.9222 ± 0.0008. Both measurements are within our uncertainty.

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3.4.3. 2000 BD19

We observed 2000 BD19 over 2 half-nights (2021 January 30 and 31) using Lowell Observatory's 31-inch in robotic mode and the Johnson R filter, collecting ∼6 hr of data each night with frames every ∼20 minutes. Warner (2015a) observed 2000 BD19 over 6 nights and reported a rotational period of 10.570 ± 0.005 hr and a max peak-to-trough amplitude of 0.69 ± 0.04. Without full coverage, we are unable to constrain the rotational period further. However, when phased to the published period, our results are consistent with the reported period and amplitude (Figure 9).

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For most of the objects we observed, the range of phase angles was too small to determine the asteroid's phase function. The exception is asteroid 2002 AJ129, which we observed over 2 nights with Lowell Observatory's 31-inch over ∼0.8–17.8°. Using the online implementation of the model selection stated in Penttilä et al. (2016), we employ the H, G₁, G₂ system described in Muinonen et al. (2010). Here G₁ and G₂ are determined to be 0.157 ± 0.035 and 0.486 ± 0.017, respectively, which is consistent with measurements of main-belt asteroids (Muinonen et al. 2010; Vereš et al. 2015).

We assessed the origin of low-q asteroids using the Granvik & Brown (2018) escape region model, which has been progressively developed by Granvik et al. (2016, 2017, 2018). Given the orbital elements and H magnitude of an object, this model gives the probabilities that an object "escaped" from each of the seven source regions, where the sum of the probabilities is equal to 1. The source regions consist of the ν₆ inner main-belt region; Jupiter resonance complexes 3:1, 5:2, and 2:1; the Hungarias; the Phocaeas; and the JFCs. The source regions and orbital elements are tabulated for all 53 known low-q objects in Table 2, and the source region with the highest probability of being the escape region for each object is in bold. All known objects with q ≤ 0.15 au most likely escaped from the ν₆ or 3:1 regions, with the exception of 2010 JG87, which most likely escaped from the 5:2 region.

This is unsurprising, considering the ν₆ and 3:1 regions are the resonance escape regions for nearly 80% of NEAs (Bottke et al. 2002; Granvik et al. 2018; Binzel et al. 2019). The NEAs from these source regions are mostly S- or Q-types, with ∼80%–90% having a geometric albedo greater than 0.1 (Binzel et al. 2019; Morbidelli et al. 2020). These resonances call for large oscillations of the eccentricity, so even if asteroids from these regions are not currently low-q, they possibly were at one point in their dynamical history. Around 80% of all NEAs have experienced a perihelion distance smaller than 0.15 au at some point since they escaped from the main belt (Toliou et al. 2021). For our specific sample, we use the lookup table from Toliou et al. (2021), which utilizes the NEO model from Granvik et al. (2018) to determine the probability that an object with a given set of orbital elements and H magnitude spent time with a perihelion distance smaller than a certain q_s and the estimated dwell times with q ≤ q_s . Unlike previous models, the Granvik et al. (2018) model accounts for "supercatastrophic" disruption of NEAs within a certain q, dependent on H. We tabulate the estimated dwell times each of our objects spent with q ≤ 0.15 au in Table 2. Dwell times range from 1 kyr (2013 YC) to 15 Myr (2007 EP88), consistent with the findings from Marchi et al. (2009). The time spent with q ≤ 0.15 au can vary from several hundred years to a few megayears, averaging ∼20 kyr for NEAs from ν₆ (Toliou et al. 2021). Asteroids that escaped from the 3:1 resonance and outer main belt spend an average of ∼5 kyr and ∼300 yr with q ≤ 0.15 au (Marchi et al. 2009).

Space weathering is the alteration of asteroid surfaces due to the space environment, which is dominated by ion radiation from the solar wind (Marchi et al. 2006b; Vernazza et al. 2009; Brunetto et al. 2015), as opposed to micrometeorite impacts, which have longer timescales (Sasaki et al. 2001). How space weathering affects an object is dependent on the object's original composition (Lantz et al. 2017). Because characterized low-q objects typically have high albedos, where more than 10% of incident radiation is reflected (Mainzer et al. 2012; Granvik et al. 2016), we focus on the space weathering effects on high-albedo objects. The space weathering effects on silicates are well understood, where nanophase-reduced iron particles (npFe0) darken and spectrally redden the surface (e.g., Pieters et al. 2000; Hapke 2001; Taylor et al. 2001; Clark et al. 2002; Brunetto et al. 2015). Space weathering of silicates will also suppress the pyroxene–olivine absorption bands (e.g., Pieters et al. 2000; Hapke 2001; Gaffey 2010). Therefore, when a fresh unweathered surface is exposed by some resurfacing process, the spectral slope becomes bluer for asteroids with a higher albedo. This has been observed on NEA Eros by NEAR (Clark et al. 2001) and on regolith grains brought from NEA Itokawa during the Hayabusa mission (Noguchi et al. 2011), both of which are S-type asteroids.

There are several potential resurfacing mechanisms that affect asteroids when they closely approach the Sun. (a) Increasing temperatures can cause material from lower surface layers to sublimate and allow for progressively less volatile material to reach sublimation temperatures. Decomposition and sublimation of refractory organics begins around ∼450 K, metal sulfides at ∼700 K, and silicates at 1000–1500 K (see review by Jones et al. 2017). (b) Many studies have explored the resurfacing caused by the tidal forces experienced during planetary encounters (e.g., Nesvorný et al. 2005; Marchi et al. 2006b; Binzel et al. 2010; Nesvorný et al. 2010; DeMeo et al. 2014; Carry et al. 2016; Devogèle et al. 2019). (c) An asteroid with an irregular surface can experience an increase in spin rate due to asymmetrical radiative torques, known as the Yarkovsky–O'Keefe–Radzievskii–Paddack (YORP) effect (Bottke et al. 2006), which may result in centrifugal loss of material (Rubincam 2000; Vokrouhlický et al. 2015). (d) Thermal fatigue induced by the diurnal temperature variations can cause boulders and grains to fracture and break down into smaller regolith, which can then be lost due to outgassing and/or solar radiation pressure (Jewitt 2012; Delbo et al. 2014). Such thermal cracking was observed on asteroid Bennu during the OSIRIS-REx mission (Molaro et al. 2020). (e) Resurfacing through impacts by high-speed near-Sun meteoroids that could eventually lead to disruption has also been suggested (Wiegert et al. 2020). All of these processes can resurface weathered regolith on low-q asteroids, which would most likely cause an overall bluer spectrum.

We found evidence for bluer colors in the low-q population when compared to NEAs (Dandy et al. 2003), but it is not as obvious as we might expect given the bluer colors observed in comets 322P, 323P, and 96P (Knight et al. 2016; Eisner et al. 2019). Additionally, we find agreement with the extended bluening trend with decreasing perihelion seen in S- and Q-type NEAs at q > 0.2 au (Marchi et al. 2006a; Graves et al. 2019). However, uncertainties are such that over the short range of q, the trend is indistinguishable from no spectral slope change with q.

The color distribution of low-q asteroids may be more stochastic because of competing processes and the potential variety in asteroid properties, including dynamical and regolith properties, such as albedo and grain size, which play a role in the extent of heating and near-Sun processing effects. Phase reddening, where the spectral slope increases with increasing solar phase angle, α, is expected to contribute to the scatter in the distribution; the effect is common among S-group asteroids (Sanchez et al. 2012; Carvano & Davalos 2015; Perna et al. 2018), but the extent varies for each individual asteroid and therefore cannot be modeled out of our measurements (Carvano & Davalos 2015; Binzel et al. 2019; Popescu et al. 2019).

4.2.1. Dynamical Considerations

4.2.2. Varying Surface Properties

4.2.3. Competing Processes

This study focused on observing surface properties, since the goal was to sample the whole population in ∼3 yr. As a result, most were too faint to meaningfully search for comae. We visually inspected all data and did not detect any evidence of activity, e.g., tail, coma, or broadened point-spread function. Comparing absolute magnitudes at the time of observation to previous data, we can crudely constrain that the low-q asteroids had no activity in excess of typical NEA rotational amplitudes.

Since all objects in our sample reach perihelion within SOHO's FOV, they are observable in every orbit when they are most likely to be active. SOHO's limiting magnitude of ∼8 is not sensitive enough to detect any objects in our sample unless they are active. However, because forward scattering of comet dust grains is highly efficient (e.g., Marcus 2007; Hui 2013), for some orientations, only a small amount of coma is needed in order to be detected. We constructed a simple model to estimate the observability of the low-q asteroids when in the SOHO FOV (r_h ≤ 0.15 au). Using the limiting magnitude of SOHO, we can determine the dust cross section needed to be detectable,

$$\begin{eqnarray}&&C=\displaystyle \frac{(2.25\times {10}^{22})\pi {r}_{{\rm{h}}}^{2}{{\rm{\Delta }}}^{2}}{{p}_{r}\phi (\alpha )}{10}^{0.4({m}_{\odot }-{m}_{\mathrm{lim}})},\end{eqnarray} \tag{ 3 }$$

where p_r is the $r^{\prime}$-band geometric albedo, α is the phase angle (the Sun–target–observer angle), ϕ(α) is the phase function (we use the Schleicher-Marcus comet dust phase function; Schleicher & Bair 2011), m_⊙ is the apparent $r^{\prime}$-band magnitude of the Sun, and m_lim is the limiting magnitude of SOHO (∼8). We identified all instances when each asteroid in our sample was within the SOHO FOV. We then estimated the depth of global material lost in order for the object to be detectable by SOHO, assuming an average particle size of 1 μm, a density of 3000 kg m^{– 3}, an albedo of 0.15, and the diameter estimate for each object. The minimum depth was ∼0.2 μm, though most required more than 1 μm, the approximate depth of material needed to be excavated by Phaethon to produce the amount of dust observed while Phaethon was active near perihelion (Li & Jewitt 2013). The apparitions needing the least depth of material were either Phaethon, the largest low-q object in our sample, or objects in extreme forward-scattering geometries (α > 160°).

Phaethon has been repeatedly detected in STEREO images at a peak apparent magnitude of ∼11 (Hui & Li 2017). As this is about ∼3 mag fainter than SOHO's limiting magnitude, it is no wonder that it has never been detected in SOHO images. Thus, it seems likely that our simple model is overly optimistic. There are numerous uncertainties in our estimates, including assuming spherical grains, uniform grain size, and grain albedo and that asteroid grains scatter in a manner analogous to comet grains, which might affect the estimated depth by an order of magnitude. Furthermore, we assumed that such dust would be present throughout the time the asteroid was in SOHO's FOV, but Li & Jewitt (2013) showed that Phaethon's activity only lasts for a few days, with peak activity ∼0.5 days after perihelion, greatly reducing the window for such dust to be detected on a given apparition. Thus, we can only exclude activity in the rest of the low-q population at 1–3 orders of magnitude higher mass-loss rates per unit surface area than Phaethon, since they are significantly smaller. A deeper search of the SOHO data via shifting and stacking at the ephemeris rate (e.g., Hui & Knight 2019) could set upper limits for the activity near perihelion for each object in the sample but is beyond the scope of the current paper.

Only nine low-q objects have reported rotational periods, including 2006 HY51, first reported in this work (Table 1; Figure 10). While several low-q objects have rotation periods of ∼3–4 hr, only one object (2011 XA3) has a rotation period of ∼45 minutes, which is below the critical spin limit for a rubble-pile asteroid (Urakawa et al. 2014). Object 2011 XA3 has H = 20.4, corresponding to a diameter of ∼200–500 m, depending on albedo. Because fast-rotating asteroids must resist their own centrifugal force, they have structurally significant tensile strength.

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Both 322P and 96P are among the fastest-rotating known comets being at/near the spin period limit, with periods of ∼3 and ∼4 hr, respectively (Knight et al. 2016; Eisner et al. 2019). Object 323P is the fastest-rotating comet with a period of ∼0.5 hr, which is a suggested driver of the comet's activity (Hui et al. 2022). The fast spin rates could be indicative of an increased YORP effect near the Sun. A larger sample in the future could support this idea if more low-q objects were found to be fast-rotating.

Smaller asteroids are found to have bluer colors (Binzel et al. 2004; Thomas et al. 2012; Carry et al. 2016), likely due to resurfacing caused by YORP spin-up and failure (Graves et al. 2018). Therefore, we might assume that faster-spinning objects approaching or above the spin barrier have bluer colors. On the other hand, a faster-spinning asteroid has a more smoothed-out temperature distribution in longitude than a slower-rotating one, creating less diurnal variability and lessening the effects of resurfacing due to thermal fatigue. We search for a trend of colors versus rotational period. However, the sample is very limited (nine objects), and no definitive conclusion can be made.

The upcoming Vera Rubin Observatory's Legacy Survey of Space and Time (LSST) will increase the number of known NEOs by an order of magnitude. The NEOs will be observed a median of 90 times spread among the ugrizy bandpasses (Jones et al. 2016). A similar analysis to this work with a much larger sample size will likely be possible. However, if different filters are not obtained simultaneously, the colors might not be easily measured due to geometric and rotational variation between observations. Because there will be hours to days between observations of the same object, rotational periods are unlikely to be determined without dedicated prompt follow-up of LSST discoveries, unless the period is sufficiently long. The LSST will observe NEOs at several epochs with various phase angles, so a phase curve is likely to be obtained. Determining the phase curve for low-q objects will give us a better understanding of surface roughness, which can better constrain the effects of regolith production and resurfacing due to thermal fatigue. The LSST will be capable of monitoring and reporting any activity or disruption events nightly with potential pipelines and proposed surveys (Schwamb et al. 2018; Seaman et al. 2018), which will further the study of near-Sun asteroids that are more likely to be active or catastrophically disrupted (Jewitt 2012; Granvik et al. 2016; Ye & Granvik 2019).

The Near-Earth Object Surveillance Mission (NEOSM, previously NEOCam; Sonnett et al. 2020) aims to extend the Wide-field Infrared Survey Explorer catalog of diameters and visual albedos by an order of magnitude. NEOSM is expected to point closer to the Sun than NEOWISE or Spitzer, enabling the search for near-Sun activity, as well as the discovery and albedo measurements of more low-q objects. More albedo measurements of low-q asteroids will help us determine the surface temperature these objects reach, as well as how space weathering affects the surface, which varies with composition and can be distinguished with albedo (Lantz et al. 2017). Additionally, the combination of NEOSM with the increasing cadence of visible observations like LSST (and other surveys like Pan-STARRS and ZTF) should improve the size estimates.