This paper was converted on www.awesomepapers.org from LaTeX by an anonymous user.
Want to know more? Visit the Converter page.

Tiny-scale Structure Discovered toward PSR B1557-50

Mengting Liu National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100101, People’s Republic of China University of Chinese Academy of Sciences, Beijing 100049, People’s Republic of China Marko Krčo National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100101, People’s Republic of China Di Li National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100101, People’s Republic of China University of Chinese Academy of Sciences, Beijing 100049, People’s Republic of China NAOC-UKZN Computational Astrophysics Centre, University of KwaZulu-Natal, Durban 4000, South Africa George Hobbs CSIRO Astronomy &\& Space Science, Australia Telescope National Facility, P.O. Box 76, Epping, NSW 1710, Australia J. R. Dawson CSIRO Astronomy &\& Space Science, Australia Telescope National Facility, P.O. Box 76, Epping, NSW 1710, Australia Department of Physics and Astronomy and MQ Research Centre in Astronomy, Astrophysics and Astrophotonics, Macquarie University, Sydney, NSW 2109, Australia Carl Heiles Department of Astronomy, University of California, Berkeley, CA 94720-3411, USA Andrew Jameson Centre for Astrophysics and Supercomputing, Swinburne University of Technology, P.O. Box 218, Hawthorn, Victoria 3122, Australia Australia Research Council Centre for Excellence for Gravitational Wave Discovery (OzGrav) Snežana Stanimirović Department of Astronomy, University of Wisconsin–Madison, Madison, WI 53706, USA Simon Johnston CSIRO Astronomy &\& Space Science, Australia Telescope National Facility, P.O. Box 76, Epping, NSW 1710, Australia John M. Dickey School of Maths and Physics, University of Tasmania, Hobart, TAS 7001, Australia
Abstract

Optical depth variations in the Galactic neutral interstellar medium (ISM) with spatial scales from hundreds to thousands of astronomical units have been observed through Hi absorption against pulsars and continuum sources, while extremely small structures with spatial scales of tens of astronomical units remain largely unexplored. The nature and formation of such tiny-scale atomic structures (TSAS) need to be better understood. We report a tentative detection of TSAS with a signal-to-noise ratio of 3.2 toward PSR B1557-50 in the second epoch of two Parkes sessions just 0.36 yr apart, which are the closest-spaced spectral observations toward this pulsar. One absorption component showing marginal variations has been identified. Based on the pulsar’s proper motion of 14 mas yr1\rm yr^{-1} and the component’s kinematic distance of 3.3 kpc, the corresponding characteristic spatial scale is 17 au, which is among the smallest sizes of known TSAS. Assuming a similar line-of-sight (LOS) depth, the tentative TSAS cloud detected here is overdense and overpressured relative to the cold neutral medium (CNM), and can radiatively cool fast enough to be in thermal equilibrium with the ambient environment. We find that turbulence is not sufficient to confine the overpressured TSAS. We explore the LOS elongation that would be required for the tentative TSAS to be at the canonical CNM pressure, and find that it is 5000\sim 5000—much larger than filaments observed in the ISM. We see some evidence of line width and temperature variations in the CNM components observed at the two epochs, as predicted by models of TSAS-like cloud formation colliding warm neutral medium flows.

ISM: clouds — ISM: structure — ISM: pulsar — line: profiles

1 Introduction

Tiny-scale atomic structures (TSAS) with spatial scales spanning from a few to hundreds of astronomical units have been studied for over four decades in the interstellar medium (ISM). Compared to the traditional definitions of the atomic medium in thermal equilibrium states—the cold neutral medium (CNM) and the warm neutral medium (WNM)—TSAS are overdense and overpressured, with densities and pressures several orders of magnitude higher than the theoretical values based on the heating and cooling balance (Stanimirović & Zweibel, 2018). TSAS has been probed mainly through three methods: (1) spatial and temporal Hi\rm H\textsc{i} absorption variability from high-resolution maps against extragalactic compact and resolved radio continuum sources (e.g. Diamond et al., 1989; Deshpande et al., 2000; Lazio et al., 2009; Roy et al., 2012; Rybarczyk et al., 2020); (2) temporal variability of Hi\rm H\textsc{i} absorption against pulsars (e.g. Frail et al., 1994; Johnston et al., 2003; Minter et al., 2005; Stanimirović et al., 2010); (3) spatial and temporal absorption variability of optical and ultraviolet lines (e.g. Na i, Ca i) toward stars in binary, multiple stellar systems, and globular clusters over a period of years (e.g. Meyer, 1990; Meyer & Blades, 1996; Lauroesch & Meyer, 2003).

Several mechanisms have been proposed to form TSAS, although there is no consensus yet. These mechanisms include TSAS being the tail-end of the turbulent spectrum(Deshpande et al., 2000), discrete elongated disk or cylinder clouds passing across the line of sight (Heiles, 1997), isolated events, such as fragmentation of the Local Bubble wall through hydrodynamic instabilities (Stanimirović et al., 2010), and structures related to planet-building gas materials (Ray & Loeb, 2017; Stanimirović & Zweibel, 2018). If TSAS represents the tail-end of turbulent dissipation processes, then a power-law relation between the spatial scales of TSAS and the optical depth variation is expected (Deshpande, 2000). Alternatively, Koyama & Inutsuka (2002) and Hennebelle & Audit (2007) modeled TSAS creation by shocks and colliding WNM flows in shocked regions.

Pulsars are particularly useful background sources because: (1) on- and off-source measurements can be obtained without position switching; (2) the relative high-velocity motion of the Earth and a pulsar over time allows us to sample a slightly different line-of-sight(LOS) through the ISM, therefore probing small structures in the ISM; (3) Hi absorption spectra of low-latitude pulsars can be used to estimate the pulsar kinematic distance (Weisberg et al., 2008);

TSAS may contain ionized gas. For example, assuming shielding is not too high, a high fraction of the carbon in TSAS clouds may be ionized by the interstellar radiation field (Heiles 1997; Stanimirović & Zweibel 2018). Ionization of hydrogen may also be instigated by local stellar objects, to produce fully ionized tiny-scale structures. McCullough & Benjamin (2001) detected an extremely narrow ionized filament, which can be attributed to the effect of photoionization from a star or compact object. Tiny-scale inhomogeneities in the ionized ISM can also be discerned through scintillation and/or extreme scattering events (Stanimirović & Zweibel, 2018). For example, Basu et al. (2016), Coles et al. (2015), and Hill et al. (2005) detected tiny-scale ionized structure with spatial scales of a few to hundreds of astronomical units through extreme scattering events and substructure in scintillation arcs toward pulsars.

PSR B1557-50 (J1600-5044) was the first pulsar against which Hi absorption variations were detected in the southern sky (Deshpande et al., 1992). In observations spaced around five years apart, these authors found an overpressured TSAS with a spatial scale of 1000 au, at a velocity of -40 km s-1 with Δτ\Delta\tau of 1.1. Johnston et al. (2003) confirmed similar Hi absorption variation at -40 km s-1 as well as another two components at -110 and -80 km s-1 toward this pulsar using Parkes, in two observational epochs spaced 14 yr apart. They concluded that the component at -110 km s-1 is related to a TSAS of 1000 au, with a density of 2.6×\times104 cm-3, and Δτ\Delta\tau of 0.15. The sight line toward PSR B1557-50 traverses substantial distances, and has previously been monitored at such long cadences that multiple TSAS components could have passed across the source between observations. We focus here on monitoring PSR B1557-50 over a much shorter time baseline: \sim0.36 yr.

This Letter is organized as follows: Section 2 describes the observing and data processing strategies. In Section 3, we decompose the optical depth profile and study the properties of the CNM. Meanwhile, we analyze the variations of Hi optical depth profiles and the properties of the changes in the observed Hi gas at different epochs. We discuss our results and their implications in Section 4. We conclude in Section 5.

2 OBSERVATIONS AND DATA PROCESSING

We used the Parkes ultra-wide-bandwidth, low-frequency receiver (‘UWL’) and the MEDUSA signal processing system (Hobbs et al., 2020), to observe the 21cm Hi line towards PSR B1557-50 at two epochs, referred to below as E1(2019.42) and E2(2019.78). The UTC dates at the beginning of the observations are 2019 May 26 and 2019 September 11, respectively. The integration time for each epoch was 379 and 382 minutes, respectively.

We recorded voltage data streams covering frequencies from 1344 to 1472 MHz. We injected a calibration signal (CAL) every 1 hr for a duration of 3 minutes in order to calibrate the antenna temperature. We used the DSPSR package (van Straten & Bailes, 2011) to process the baseband data to get spectra with a velocity resolution of \sim0.1 km s-1. The data were folded synchronously with the pulsar period for an integration time of 10 s with 64 phase bins.

The pulse profile for each polarization is generated by collapsing (after de-dispersion) the data along the frequency dimension, which gives the location of pulsar-on and pulsar-off phase bins. The pulsar-on and pulsar-off spectra were obtained based on the strategies described by Weisberg et al. (2008). The absorption spectrum is the difference of the pulsar-on and pulsar-off spectra on an antenna temperature scale, I(v)=Ion(v)Ioff(v)I(v)=I^{\rm on}(v)-I^{\rm off}(v). The final normalized absorption spectrum (I(v)/I0I(v)/I_{0}) was obtained by dividing the final absorption spectrum by the mean value excluding the absorption channels (I0I_{0}). According to radiative transfer, the pulsar-on and pulsar-off spectra can be written as

Ton(v)=(Tbg+Tpsr)eτv+Ts(1eτv),Toff(v)=Tbgeτv+Ts(1eτv),\begin{split}T^{\rm on}(v)&=(T_{\rm bg}+T_{\rm psr})e^{-\tau_{v}}+T_{\rm s}(1-e^{-\tau_{v}}),\\ T^{\rm off}(v)&=T_{\rm bg}e^{-\tau_{v}}+T_{\rm s}(1-e^{-\tau_{v}}),\end{split} (1)

where TbgT_{\rm bg} is the background brightness temperature, τv\tau_{v} is the Hi  optical depth, TsT_{\rm s} is the Hi spin temperature, and TpsrT_{\rm psr} is the pulsar continuum brightness temperature. Therefore,

eτv=(Ton(v)Toff(v))/Tpsr=I(v)/I0.e^{-\tau_{v}}=(T^{\rm on}(v)-T^{\rm off}(v))/T_{\rm psr}=I(v)/I_{0}. (2)

The final calibrated antenna temperature of the Hi\rm H\textsc{i} emission spectrum, THI(v)T_{\rm HI}(v), is required to compute the frequency-dependent noise spectrum (see §3.2). THI(v)T_{\rm HI}(v) was obtained by removing the baseline of Ioff(v)I^{\rm off}(v) using a third-order polynomial. To obtain the brightness temperature-calibrated Toff(v)T^{\rm off}(v), which is required to derive TsT_{\rm s} (see Appendix B), we scaled THI(v)T_{\rm HI}(v) to match the Parkes Galactic All-Sky Survey (GASS) intensity scale (McClure-Griffiths et al., 2009).

The absorption spectra are affected by on-source ripples, especially for E2. We identified the Fourier components of the ripples. Among these there are two baseline ripples with lag times of 0.175 and 0.35 μ\mus (the second harmonic ripple) corresponding to the periods of standing waves formed by the reflections between the vertex and the focal plane. We did not attempt to diagnose formation mechanisms for any other ripples. All ripples were reduced in I(v)I(v) by fitting for and removing sinusoidal functions with the same Fourier components.

The Hi emission and absorption spectra from E1 and E2 are shown in the first and second panels of Figure 1, respectively. There are clear differences in the absorption features between the two epochs, which we discuss further below.

Below the spectra, we display the distance versus radial velocity curve, which was derived for the Galactic coordinates of PSR B1557-50 (l=l=330.690, b=b=1.631) using a linear rotation curve (Mróz et al., 2019). A detailed description of the derivation is given in Appendix A. The lower distance limit DlD_{l}=5.6±0.35.6\pm 0.3 kpc for PSR B1557-50 is set by the velocity center of the most distant absorption feature. The upper distance limit Du=16.6±0.8D_{u}=16.6\pm 0.8 kpc is given by the distance of the nearest significant emission component (brightness temperature above 35 K; Weisberg et al., 1979) not seen in absorption. We adopted the method described in Verbiest et al. (2012) to translate these lower and upper limits into actual distance using a likelihood analysis. The updated pulsar distance DupdatedD_{\rm updated} is 6.00.61.86.0^{1.8}_{0.6} kpc. This is lower than the distance from the Australia Telescope National Facility (ATNF) pulsar catalog Dpsrcat=6.90.91.9D_{\rm psrcat}=6.9^{1.9}_{0.9} kpc, which was estimated by Verbiest et al. (2012) based on Hi distance limits from Johnston et al. (2001) with a flat rotation curve (Fich et al., 1989), while higher than the distance determined from recent electron density models, which give DDMD_{\rm DM}=5.1 kpc (Yao et al., 2017).

Refer to caption
Figure 1: Spectra for PSR B1557-50. First panel: Hi emission scaled to match GASS in the direction of the pulsar with a velocity resolution of 0.1 km s-1. Second panel: Velocity-binned HI absorption spectra taken at E1 (black) and E2 (red) with a channel width of \sim1.7 km s-1. I(v)/I0I(v)/I_{0} is as defined in Equation 2. Third panel: distance versus radial velocity curve, calculated from Mróz et al. (2019) using the best-fit linear model with R0R_{0}=8.09 kpc, Θ0\Theta_{0}= 233.6 km s-1. Fourth panel: velocity-binned difference Hi absorption profiles (ΔI(v)/I0=[I(v)/I0]E2[I(v)/I0]E1\Delta I(v)/I_{0}=[I(v)/I_{0}]_{\rm E2}-[I(v)/I_{0}]_{\rm E1}) with a channel width of 1.7 km s-1, before (black) and after (red) mean-filtering. Red and black dashed lines represent ±\pm 2 and 3σ\sigma noise envelopes where the contribution from Hi emission has been taken into account. The blue channel represents the TSAS we have identified with an S/N of 3.2. The blue horizontal line represents 0 difference. Fifth panel: spatial scale as a function of velocity. The dashed-dotted line represents the maximum spatial scale corresponding to the transverse distance traveled by the pulsar between the two epochs.

3 Spectral Analysis, Line Profile Variations, and TSAS Properties

3.1 Spectral Analysis

We largely followed the method of Murray et al. (2018) to estimate the physical properties of each Hi component, assuming that CNM can be seen in both the optical depth spectrum and the Hi emission profile, while the WNM can only be seen in the emission profile. All CNM and WNM components were obtained through Gaussian decomposition (Heiles & Troland, 2003a). A detailed description of the spectral decomposition and spin temperature calculation is given in Appendix B.

The derived spin temperatures for each CNM component in both epochs are listed in Table 1. Most of the CNM clouds from the two observations have spin temperatures in the range 10–260 K, which is consistent with previous observed CNM clouds (Heiles & Troland, 2003b).

Table 1: Fitted Parameters of CNM Components from Gaussian Decomposition
Epoch τ0\tau_{0} v0v_{0} Δv0\Delta v_{0} TsT_{s} N(HI)absN({\rm HI})_{\rm abs}
(name) (kms1\rm km\,s^{-1}) (kms1\rm km\,s^{-1}) (K\rm K) (1020cm210^{20}\rm\,cm^{-2})
E1 (2019.42) 0.9 ±\pm 0.1 -106.18 ±\pm 0.09 1.6 ±\pm 0.2 11.5 ±\pm 3.1 0.3 ±\pm 0.1
3.3 ±\pm 0.1 -88.25 ±\pm 0.02 1.49 ±\pm 0.06 45.7 ±\pm 3.2 4.4 ±\pm 0.4
1.0 ±\pm 0.1 -81.45 ±\pm 0.08 0.9 ±\pm 0.2 8.1 ±\pm 4.9 0.1 ±\pm 0.1
0.35 ±\pm 0.06 -79.6 ±\pm 0.8 8.4 ±\pm 2.2 203.7 ±\pm 24.5 12 ±\pm 4.1
0.5 ±\pm 0.1 -72.8 ±\pm 0.3 2.6 ±\pm 0.7 97.0 ±\pm 7.3 2.2 ±\pm 0.8
0.67 ±\pm 0.05 -35.7 ±\pm 0.2 5.9 ±\pm 0.6 105.6 ±\pm 4.7 8.3 ±\pm 1.1
0.79 ±\pm 0.07 -22.7 ±\pm 0.2 3.5 ±\pm 0.4 103.7 ±\pm 3.8 5.7 ±\pm 0.8
0.83 ±\pm 0.07 -15.9 ±\pm 0.2 3.3 ±\pm 0.4 96.2 ±\pm 4.0 5.3 ±\pm 0.8
0.5 ±\pm 0.05 -6.6 ±\pm 0.3 6.2 ±\pm 0.8 98.7 ±\pm 5.7 6.2 ±\pm 1.1
0.79 ±\pm 0.08 2.0 ±\pm 0.1 2.8 ±\pm 0.3 137.7 ±\pm 9.1 6.0 ±\pm 1.0
E2 (2019.78) 0.5 ±\pm 0.07 -105.3 ±\pm 0.2 2.4 ±\pm 0.4 10.3 ±\pm 2.5 0.25 ±\pm 0.08
1.1 ±\pm 0.1 -88.61 ±\pm 0.06 1.4 ±\pm 0.1 16.7 ±\pm 11.5 0.6 ±\pm 0.4
0.25 ±\pm 0.04 -78.1 ±\pm 0.7 7.4 ±\pm 1.8 201.8 ±\pm 37.7 7.3 ±\pm 2.6
0.4 ±\pm 0.1 -72.5 ±\pm 0.1 1.2 ±\pm 0.4 17.6 ±\pm 7.7 0.2 ±\pm 0.1
0.37 ±\pm 0.06 -53.99 ±\pm 0.3 3.7 ±\pm 0.7 75.5 ±\pm 13.6 2.0 ±\pm 0.7
0.32 ±\pm 0.05 -46.6 ±\pm 0.4 4.7 ±\pm 1.1 151.6 ±\pm 10.5 4.6 ±\pm 1.3
0.5 ±\pm 0.1 -37.3 ±\pm 0.2 2.1 ±\pm 0.6 14 ±\pm 7.9 0.3 ±\pm 0.2
0.38 ±\pm 0.06 -34.8 ±\pm 0.7 8.3 ±\pm 1.3 157.4 ±\pm 10.2 9.8 ±\pm 2.3
0.58 ±\pm 0.06 -22.5 ±\pm 0.2 4.3 ±\pm 0.5 86.9 ±\pm 14.5 4.3 ±\pm 1.0
0.43 ±\pm 0.06 -15.0 ±\pm 0.2 3.2 ±\pm 0.6 131.7 ±\pm 7.0 3.6 ±\pm 0.9
0.35 ±\pm 0.04 -5.8 ±\pm 0.5 7.1 ±\pm 1.2 99.5 ±\pm 19.8 5.0 ±\pm 1.4
0.68 ±\pm 0.07 2.3 ±\pm 0.2 3.0 ±\pm 0.4 102.1 ±\pm 20.7 4.1 ±\pm 1.1

Note. — Column (1): observing epoch. Columns (2-4): gaussian parameters fit to the opacity profile (Equation B1). Column (5): Hi spin temperature. Column (6): Hi column density of absorption component calculated by N(Hi)abs=C0τTs𝑑v=1.064C0τ0Δv0TsN({\rm H\textsc{i}})_{\rm abs}=C_{0}\int\tau\,T_{s}\,dv=1.064\cdot C_{0}\cdot\tau_{0}\cdot\Delta v_{0}\cdot T_{s}, where C0=1.823×1018cm2/(kms1K)C_{0}=1.823\times 10^{18}\rm\,cm^{-2}/(km\,s^{-1}\,K) (Murray et al., 2018).

3.2 Line Profile Variations and TSAS properties

In order to evaluate Hi absorption profile variations between the two epochs, we first define a difference Hi absorption profile ΔI(v)/I0=[I(v)/I0]E2[I(v)/I0]E1\Delta I(v)/I_{0}=[I(v)/I_{0}]_{\rm E2}-[I(v)/I_{0}]_{\rm E1}. Here it is crucial to account accurately for the noise profile, where the Hi emission tends to dominate over system temperature in this band (Weisberg et al., 2008). We thus added the Hi antenna temperature, in the velocity range where it can be measured, to the system temperature. The resultant noise profile is given by σΔI/I0(v)=σΔI/I0,offline×[THI(v)+Tsys,offline]/Tsys,offline\sigma_{\Delta I/I_{0}}(v)=\sigma_{\Delta I/I_{0},\rm off-line}\times[T_{\rm HI}(v)+T_{\rm sys,off-line}]/T_{\rm sys,off-line}, where Tsys,offlineT_{\rm sys,off-line} is the system temperature for off-line channels, THI(v)T_{\rm HI}(v) is the antenna temperature of Hi at velocity vv, and σΔI/I0,offline\sigma_{\Delta I/I_{0},\rm off-line} is the absorption spectrum standard deviation of off-line channels (Weisberg et al., 2008). In order to mitigate the effect of residual baseline ripples on ΔI(v)/I0\Delta I(v)/I_{0}, we constructed a boxcar mean-filter with a width of 40 km s-1 , based on the width of the residual ripples, and subtracted it from the difference spectrum. We propagated the uncertainties associated with the mean-filter to determine the final noise envelope of the difference spectrum after mean-filtering.

We adopted a two-stage approach to Gaussian fitting of the mean-filtered difference spectrum. First, we used a Markov Chain Monte Carlo (MCMC) method to obtain an initial list of TSAS candidates. The MCMC sampling was performed using the emcee package (Foreman-Mackey et al., 2013), with the range of Gaussian parameters for the prior obtained from the properties of the absorption components at the two epochs. We then treated all components with local minima as constrained from MCMC as TSAS candidates. We next performed a traditional χ2\chi^{2} fitting on those candidates using the LMFIT package (Newville et al., 2019), with initial guesses for the Gaussian parameters drawn from the MCMC output. We define the signal-to-noise ratio (S/N) of the component as the ratio of the fitted amplitude to the 1σ\sigma amplitude uncertainty output by LMFIT (where the appropriate noise spectrum has been used as the uncertainty input). This 1σ\sigma amplitude uncertainty is estimated from the square root of the diagonal elements of the covariance matrix. By this definition, there is one tentative TSAS showing a marginal detection with an S/N of 3.2 at a velocity of \sim-54 km s-1, shown in the fourth panel of Figure 1. Note that while the spectrum is binned in velocity to a channel width of 1.7 km s-1 to illustrate the TSAS clearly, the fitting was performed on the unbinned data.

Compared to previous TSAS detections toward this pulsar (Deshpande et al. 1992; Johnston et al. 2003), we have a much higher velocity resolution of \sim0.1 km s-1, reduced artifacts on the difference spectrum, and an estimated distance for each channel, enabling us to obtain TSAS detection results with definitive spatial scales. The spatial scale L(v)L_{\perp}(v) was calculated using L(v)=πPMTOT/(3600180)×Dist×ΔtL_{\perp}(v)=\pi\rm PM_{TOT}/\left(3600*180\right)\times Dist\times\Delta t, where PMTOT\rm PM_{TOT} is pulsar total proper motion of 14 mas yr-1 from the ATNF pulsar catalog (Manchester et al., 2005), and Δt\Delta t is the time baseline. The characteristic scale of the tentative TSAS that we probe is 17 au, which is much smaller than the TSAS previously detected toward PSR B1557-50 (\sim1000 au at the velocity of -110 km s-1 assuming the pulsar velocity of 400 km s-1 and the component distance of 6.4 kpc) with a time baseline of 20 yr. This is due to the short time interval between the two observations.

The observed properties of the single tentatively detected TSAS component are summarized in Table 2. We assume that the TSAS has the same spin temperature as its host CNM cloud (75.5±13.675.5\pm 13.6 K). The derived value of the Hi column density of the tentative TSAS is (7±37\pm 3)×1019cm2\times 10^{19}\rm\,cm^{-2}, consistent with the column densities of previous TSAS studies, which range from 1019 to 10cm221{}^{21}\rm\,cm^{-2} (Stanimirović & Zweibel, 2018). In total, the tentative TSAS feature contributes 34%\sim 34\% to the total Hi column density of its host CNM cloud.

4 Discussion

If the tentative TSAS is a spherical cloud with diameter of LL_{\perp}, the derived Hi volume density and thermal pressure are larger than 104\sim 10^{4} cm-3 and 106\sim 10^{6} K cm-3, respectively. These values are consistent with previous TSAS studies (assuming similar geometry), with TSAS showing densities and thermal pressures two to three orders of magnitude larger than typical ISM values (Heiles, 1997; Stanimirović et al., 2010; Stanimirović & Zweibel, 2018).

Table 2: Tentative TSAS properties
Center Velocity LL_{\perp} Dist Δv0\Delta v_{0} Δτ\Delta\tau TsT_{s} N(HI)TSASN(\rm HI)_{\rm TSAS} n(HI)TSASn(\rm HI)_{\rm TSAS} P/kP/k Nmin,cN_{\rm min,c} σtur\sigma_{\rm tur}/σturTs=10K\sigma_{\rm tur}^{T_{s}=10\,K} σth\sigma_{\rm th}/σthTs=10K\sigma_{\rm th}^{T_{s}=10\,K}
(km s-1) (au) (kpc) (km s-1) (K\rm\,K) (1020cm210^{20}\rm\,cm^{-2}) (104cm310^{4}\rm\,cm^{-3}) (106cm3K10^{6}\rm\,cm^{-3}\,K) (1020cm210^{20}\rm\,cm^{-2}) (km s-1) (km s-1)
53.5±0.2-53.5\pm 0.2 17 3.3±\pm0.3 1.3 ±\pm0.5 0.35±\pm0.11 75.5±\pm13.6 0.7±\pm0.3 26.6 ±\pm13.7 20.7±\pm12.5 0.03 0.2±\pm0.1/0.5 0.8±\pm0.1/0.3

Note. — Column (1): TSAS central velocity. Column (2): TSAS spatial scale. Column (3): TSAS distance. Column (4): FWHM of TSAS. Column (5) maximum absolute value of optical depth variations. Column (6): spin temperature. Column (7): TSAS Hi column density. It is calculated by N(HI)TSAS=1.064C0|Δτ|Δv0TsN{(\rm HI)}_{\rm TSAS}=1.064\cdot C_{0}\cdot|\Delta\tau|\cdot\Delta v_{0}\cdot T_{s}. Column (8): TSAS Hi volume density. Column (9): thermal pressure. Column (10): the minimum column density calculated when the dynamical time is larger than the cooling time. Column (11): the one-dimensional turbulent velocity/ the one-dimensional turbulent velocity when the TSAS spin temperature is 10 K. Column (12): the one-dimensional thermal velocity/ the one-dimensional thermal velocity when the TSAS spin temperature is 10 K.

When the dynamical time tdynt_{dyn} is larger than the cooling time tcoolt_{cool}, clouds may reach a thermal equilibrium, even if they are overpressured. The column density Nmin,cN_{\rm min,c} for a cloud at the balance of cooling and expanding can be calculated. As estimated by Stanimirović & Zweibel (2018),

tdyn>tcool,RkT/m>3kT2nΛ,nR>Nmin,c=1.2×1015T3/2e92/T,\begin{split}t_{dyn}&>t_{cool},\\ \dfrac{R}{\sqrt{kT/m}}&>\dfrac{3kT}{2n\Lambda},\\ nR&>N_{\rm min,c}=1.2\times 10^{15}T^{3/2}e^{92/T},\end{split} (3)

where RR is the size of the cloud, TT is the kinetic temperature, and Λ\Lambda is the radiative loss function assuming the Cii fine structure line is the main source of cooling, with a carbon depletion factor of 0.35. Clouds with a column density larger than Nmin,cN_{\rm min,c} would cool radiatively faster than they expand and reach thermal equilibrium. We use the Hi spin temperature as an approximation of the kinetic temperature (Lauroesch & Meyer, 2003) to estimate Nmin,cN_{\rm min,c}. We found that our tentative TSAS component has N(HI)TSASN{(\rm HI)}_{\rm TSAS} significantly larger than Nmin,cN_{\rm min,c}, suggesting that it may be overpressured and in thermal equilibrium with the ambient ISM.

To alleviate the problem of overpressurization, Heiles (1997) proposed a model in which TSAS represents discrete features in the shape of cylinders and disks that are homogeneously and isotropically distributed within CNM clouds. The volume density of TSAS depends both on the spatial scale across the LOS, LL_{\perp}, and the path length along the LOS LL_{\parallel}. Following Heiles (1997), we define the geometric elongation factor 𝒢\mathscr{G} = L/L_{\parallel}/LL_{\perp} and use the standard CNM thermal pressure of 4000 cm3K\rm\,cm^{-3}\,K. Based on our measured spin temperature and column density, the elongation factor 𝒢\mathscr{G} required for our tentative TSAS to match the CNM pressure is 5000\sim 5000. Such extremely filamentary structure is out of the range predicted by Heiles (1997), who found 𝒢\mathscr{G} larger than 1 and less than 10. This is a result of the large variation of optical depth and the small spatial scale of the tentative TSAS that we detected.

Hennebelle & Audit (2007) found that CNM clouds can be generated from thermally unstable regions in colliding WNM flows based on simulations with resolutions of 400 and 4000 au (larger than the typical spatial scales of observed TSAS). The supersonic collisions of CNM clouds form transient shocked regions with large temperature variations at the boundaries, showing similar properties to TSAS. Such temperature variations could induce line width and optical depth variations in the observed Hi absorption spectrum. From Figure 1 and Table 1 it can be seen that, in addition to apparent optical depth variations, the spin temperatures and line widths of the CNM components detected in this work do exhibit differences between the two epochs. While much of this variation lies below strict 3σ3\sigma limits, it may still be indicative of the variations predicted by colliding flows models, possibly suggesting that our tentative TSAS cloud could have formed from such colliding WNM flows. However, better evidence of variability in the data, together with detailed descriptions of the properties of TSAS from higher-resolution simulations (<100 au) are needed to make firm quantitative comparisons.

Finally, compressible turbulence can generate density fluctuations and may even be able to confine an overpressured structure. Stanimirović & Zweibel (2018) estimate that in order to confine TSAS-like structures that are overpressured by a factor of \sim100, the turbulent velocity must be at least 10 times the rms thermal velocity (see also McKee & Zweibel, 1992). Following Rybarczyk et al. (2020), we estimate the one-dimensional turbulent velocity σtur\sigma_{\rm tur} for the tentative TSAS components by assuming that the nonthermal component of the velocity dispersion is entirely accounted for by turbulent motions such that

σtur2=σv02σth2=(Δv0/2.355)2kbTs/mH0,\sigma_{\rm tur}^{2}=\sigma_{v_{0}}^{2}-\sigma_{\rm th}^{2}=(\Delta v_{0}/2.355)^{2}-k_{b}T_{s}/m_{\rm H_{0}}, (4)

where mH0m_{\rm H_{0}} is the mass of hydrogen atom, σth\sigma_{\rm th} is the one-dimensional rms thermal velocity, and the spin temperature TsT_{s} is assumed to be a good approximation of the kinetic temperature for the CNM. The turbulent velocity is far too small to confine the tentative TSAS, with σtur=0.2\sigma_{\rm tur}=0.2 km s-1and σth=0.8\sigma_{\rm th}=0.8 km s-1(see also Table 2). If we instead adopt a minimum possible value of 10 K for the spin temperature, we can calculate a lower limit for σth\sigma_{\rm th} and the corresponding upper limit for σtur\sigma_{\rm tur} (as denoted by σthTs=10K=0.3\sigma^{T_{s}=10\,K}_{\rm th}=0.3 km s-1and σturTs=10K=0.5\sigma^{T_{s}=10\,K}_{\rm tur}=0.5 km s-1 in Table 2). Even in this case, the turbulent velocity is still less than 10 times the rms thermal velocity. This suggests that the turbulent velocity is not sufficient to confine the tentative TSAS.

5 Conclusions

We have obtained Hi absorption spectra in two epochs 0.36 yr apart toward PSR B1557-50 with the Parkes telescope. We have detected one component with marginally significant absorption variations, which probes astronomical-unit-scale atomic structure in the Milky Way. Our main results are summarized as follows:

1. One TSAS component at \sim-54 km s-1 was marginally detected with an S/N of 3.2 after carefully reducing artifacts on the difference spectrum.

2. The characteristic plane-of-sky spatial scale of the TSAS component is 17 au, with an inferred volume density (assuming a spherical cloud) of 2.7×1052.7\times 10^{5} cm-3, and thermal pressure of 2.1×107cm32.1\times 10^{7}\rm\,cm^{-3} K, suggesting that the TSAS is overdense and overpressured by two to three orders of magnitude.

3. The inferred overpressured TSAS has sufficiently high column density to be in thermal equilibrium with the ambient ISM, while still being overpressured.

4. The elongation factor of the TSAS cloud would need to be \sim5000 in order for it to be in pressure equilibrium with a canonical CNM. This is much larger than expected from disks or cylinders (Heiles, 1997), and than the aspect ratio of observed ISM filaments.

5. In addition to optical depth variations, there is some evidence of line width and temperature variations in the CNM components observed at the two epochs. This may hint at a possible formation mechanism involving WNM collisions.

6. We consider a scenario in which the TSAS represents the tail-end of a turbulent cascade, and find that the turbulent line width would be insufficient to confine such an overpressured cloud.

Further monitoring of this pulsar, as well as other pulsars with previous Hi absorption measurements using single-dish telescopes (e.g. Parkes, FAST), would enable us to detect a larger population of TSAS, and with sensitivity to a wide range of spatial scales, which may be critical in better probing the role of turbulence in TSAS formation. More generally, future high-resolution, interferometric observations (e.g. with the VLA, ALMA) of atomic and molecular lines (e.g. Hi, CO, Ci, Cii, SiO) toward TSAS associated with a larger range of spin temperature estimates are necessary to better constrain TSAS cooling and heating mechanisms, and shed crucial light onto TSAS formation.

This work is supported by National Natural Science Foundation of China (NSFC) program Nos. 11988101, 11725313, 11690024, by the CAS International Partnership Program No. 114-A11KYSB20160008, and the National Key R&D Program of China (No. 2017YFA0402600). Cultivation Project for FAST Scientific Payoff and Research Achievement of CAMS-CAS. J.R.D. is the recipient of an Australian Research Council (ARC) DECRA Fellowship (project number DE170101086). We are grateful to Lawrence Toomey for supporting the data transfer and processing. We thank CSIRO computing resources for data storage and processing. We express our thanks to Claire Murray for Gaussian decomposition and fitting discussions; Shi Dai, Weiwei Zhu, Lei Zhang, and Chenchen Miao for dispersion measurements (DM) discussions; Jumei Yao for DM-based distance discussions; James Green for his help in observation arrangement at Parkes; Zhichen Pan and Yi Feng for supporting the data transfer; Lei Qian for the turbulent velocity discussion; Zheng Zheng for baseline removal discussion; Chaowei Tsai and Guodong Li for the MCMC discussion.

Appendix A The distance – radial velocity curve Derivation

Mróz et al. (2019) used a sample of 773 Classical Cepheids with precise distances based on mid-infrared period–luminosity relations coupled with proper motions and radial velocities from Gaia to construct an accurate rotation curve of the Milky Way up to a distance of \sim20 kpc from the Galactic center. They found linear rotation curves describe that data much better than a simple constant rotation curve and are more consistent with previous observations than the universal rotation curve.

For an object in circular rotation about the galactic center at radius RR with circular velocity ω\omega, by adopting the best-fit linear model from Mróz et al. (2019)

ωω0=1.046(R0R)0.046,\frac{\omega}{\omega_{0}}=1.046\left(\frac{R_{0}}{R}\right)-0.046, (A1)

where ω0\omega_{0} is the angular velocity of the Sun’s rotation around the Galaxy, the radial velocity with respect to the LSR is given by

Vr=[1.046(R0R)0.046]×Θ0sinlcosb+VΠcoslcosb,V_{r}=\left[1.046\left(\frac{R_{0}}{R}\right)-0.046\right]\times\Theta_{0}\sin l\cos b+V_{\Pi}\cos l\cos b, (A2)

where R0R_{0} and VΠV_{\Pi} represent the galactocentric distance of the Sun and the net outward motion of the LSR with respect to the Galactic object that is 4.2 km s-1, respectively. Similar to Weisberg et al. (2008), we add and subtract velocities of 7 km s-1to VrV_{r} to estimate the uncertainties in distance limits due to streaming and random gas motions in the Galaxy.

With R=(R02+d22R0dcosl)1/2R=(R_{0}^{2}+d^{2}-2R_{0}d\cos l)^{1/2}, the distance–radial velocity relation can be derived

d=±(1.046R0Θ0sinlcosbVrVΠcoslcosb±7+1.046Θ0sinlcosb)2R02(1cosl2)+R0cosl.d=\pm\sqrt{\left(\frac{1.046R_{0}\Theta_{0}\sin l\cos b}{V_{r}-V_{\Pi}\cos l\cos b\pm 7+1.046\Theta_{0}\sin l\cos b}\right)^{2}-R_{0}^{2}\left(1-\cos l^{2}\right)}+R_{0}\cos l. (A3)

Appendix B Gaussian Decomposition and Spin Temperature Estimation

We largely followed the method described in Murray et al. (2018) to estimate the spin temperature. This method was developed based on the strategy first proposed by Heiles & Troland (2003a). They used a least-squares fitting method to fit Gaussian components to both Hi absorption and emission spectra in order to estimate the physical properties of the Hi clouds. Compared to the traditional fitting method described in Heiles & Troland (2003a), Murray et al. (2018) adopted an autonomous Gaussian decomposition algorithm (Gausspy), which implements a derivative spectroscopy technique to use supervised machine learning to estimate the number of Gaussian features and their properties. We used an upgraded, fully autonomous, Gaussian decomposition algorithm via its open-source Python implementation (GaussPy+; Riener et al., 2019) to make initial guesses for Gaussian components toward PSR B1557-50 and applied a least-squares fitting to refine the results.

According to Heiles & Troland (2003a), the optical depth spectrum τ(v)\tau(v) along the LOS with a set of NN Gaussian components can be written as

τ(v)=ln(I/I0)=n=0N1τ0,ne4ln2(vv0,n)2/Δvn2,\tau(v)=-\ln(I/I_{0})=\sum_{n=0}^{N-1}\tau_{0,n}\cdot e^{-4\ln{2}\left(v-v_{0,n}\right)^{2}/\Delta v_{n}^{2}}, (B1)

where τ0,n\tau_{0,n}, v0,nv_{0,n}, Δvn\Delta v_{n} are the the peak optical depth, central velocity and FWHM of the nnth component.

The optical depth spectrum is only contributed by the CNM, while the brightness temperature-calibrated Toff(v)T^{\rm off}(v) consists of both the CNM and the WNM,

Toff(v)=TB,CNM(v)+TB,WNM(v),T^{\rm off}(v)=T_{B,\rm CNM}(v)+T_{B,\rm WNM}(v), (B2)

where TB,CNMT_{B,\rm CNM} is the Hi\rm H\textsc{i} emission contributed by the CNM and TB,WNMT_{B,\rm WNM} is the Hi\rm H\textsc{i} emission contributed by the WNM. The Hi emission contributed by NN CNM components can be written as

TB,CNM(v)=n=0N1Ts,n(1eτn(v))em=0Mτm(v),T_{B,\rm CNM}(v)=\sum_{n=0}^{N-1}T_{s,n}\left(1-e^{-\tau_{n}(v)}\right)e^{-\sum_{m=0}^{M}\tau_{m}(v)}, (B3)

where mm represents MM absorption clouds lying in front of the nnth cloud, and Ts,nT_{s,n} represents the spin temperature for the nnth component. For the Hi emission contributed by the WNM TB,WNM(v)T_{B,\rm WNM}(v), can be considered as a set of KK Gaussian functions. For each kkth component, there is a fraction k\mathscr{F}_{k} of the WNM is located in front of all CNM components.

TB,WNM(v)=k=0K1[k+(1k)eτ(v)]T0,ke4ln2(vv0,k)2Δvk2.T_{B,\rm WNM}(v)=\sum_{k=0}^{K-1}\left[\mathscr{F}_{k}+\left(1-\mathscr{F}_{k}\right)e^{-\tau(v)}\right]\cdot T_{0,k}e^{\frac{-4\ln{2}\left(v-v_{0,k}\right)^{2}}{\Delta v_{k}^{2}}}. (B4)

where v0,kv_{0,k}, Δvk\Delta v_{k}, and T0,kT_{0,k} represent the central velocity, FWHM, and peak brightness temperature for the kkth emission component.

Based on the method described in Murray et al. (2018), spin temperatures can be estimated with the following steps:

  1. 1.

    Decompose Gaussian components for τ(v)\tau(v) with GaussPy+ to make initial guesses. Following the method described in Riener et al. (2019), we applied the algorithm on the optical depth profiles constructed from absorption lines of PSR B1557-50. We chose the two-phase smoothing parameters α1\alpha_{1} = 2.58 and α2\alpha_{2} = 5.14, and a minimum S/N of 5 for signal peaks in the data to decompose the optical depth profiles.

  2. 2.

    Fit NN components to τ(v)\tau(v) via least-squares fitting using the LMFIT package (Newville et al., 2019). The components were selected from step (1), restricted to those with line widths less than 20 km s-1, and which are well separated. The mean velocities, widths, and amplitudes are allowed to vary by ±20%\pm 20\% with respect to the GaussPy+ fit parameters. τ(v)\tau(v) was decomposed into 10 and 12 components for E1 and E2, respectively, which are shown as blue lines in the third panels of Figure  2.

    Refer to caption
    Refer to caption
    Figure 2: For two epoch observations (left: E1, right: E2). The brightness temperature-calibrated Hi emission spectrum Toff(v)T^{\rm off}(v) and the fitting spectra Toff,Gpy+(v)T^{\rm off,Gpy+}(v) in the red line (the top panels), the fitting residual of the Hi emission spectrum (the top second panels), the optical depth profile τ(v)\tau(v), the decomposed Gaussian components in the dashed blue lines, and the fitting optical depth profile τGpy+(v)\tau_{Gpy+}(v) in the red line (the top third panels), and the fitting residual of the optical depth profile (the bottom panels). The spectra were binned in velocity to a channel width of 0.6 km s-1 .
  3. 3.

    Fit the NN components from τ(v)\tau(v) to Toff(v)T^{\rm off}(v) via least-squares fitting. The mean velocities and widths are allowed to vary by ±10%\pm 10\%. TsT_{s} are constrained to between 0<Ts,nTk,max,n=21.866Δvn20<T_{s,n}\leq T_{k,{\rm max},n}=21.866\cdot\Delta v_{n}^{2}, to produce physically realistic spin temperatures.

  4. 4.

    Subtract the best-fit model in step (3) from Toff(v)T^{\rm off}(v) to produce a residual emission spectrum, which contains only WNM components not previously modeled.

  5. 5.

    Fit KK components to the residual emission spectrum from (4) with GaussPy+, using α1\alpha_{1} = 2.58, α2\alpha_{2} = 5.14, and S/N=740.

  6. 6.

    Use least-squares fitting to fit Toff(v)T^{\rm off}(v) with N+KN+K Gaussian components from steps (2) and (5). The mean velocities and widths are allowed to vary by 10%10\% with respect to the previously fitted values. The amplitudes are constrained so that Toff(v)>0T^{\rm off}(v)>0. The final estimation of TsT_{s} for the NN absorption components and the Gaussian parameters of the KK emission-only components is computed based on Equations on B2B4.

When the absorption components overlap in velocity significantly, the order of each component will affect TB,CNM(v)T_{B,\rm CNM}(v) (Heiles & Troland 2003a; Stanimirović et al. 2010; Murray et al. 2018). There are a maximum of N!N! different orderings of components along the LOS. In the case of our optical depth profiles, the components are well separated, so we did not need to consider the ordering phenomenon during fitting. Previous studies have indicated that the values of k\mathscr{F}_{k} have significant effect on the derived spin temperature (Heiles & Troland 2003a; Stanimirović et al. 2010; Murray et al. 2018). We followed previous analyses to estimate the spin temperature by assigning value of 0.0, 0.5, or 1.0 to k\mathscr{F}_{k}. Therefore, there are three possible cases for the final fit of Toff(v)T^{\rm off}(v) from our data.

The final spin temperatures are calculated by estimating the weighted mean and standard deviation over the three iterations following Heiles & Troland 2003a. For the spectra observed in E1 and E2, we fit 10 (NN =10) and 12 (NN =12) absorption components and 27 (KK =27) and 26 (KK =26) emission-only components, respectively. Figure 2 illustrates the Gaussian decomposition and fitting of the Hi emission and optical depth profiles for E1 and E2, respectively. The main results of the CNM decomposition are shown in Table 1.

References

  • Basu et al. (2016) Basu, R., Rożko, K., Lewandowski, W., Kijak, J., & Dembska, M. 2016, MNRAS, 458, 2509, doi: 10.1093/mnras/stw394
  • Coles et al. (2015) Coles, W. A., Kerr, M., Shannon, R. M., et al. 2015, ApJ, 808, 113, doi: 10.1088/0004-637X/808/2/113
  • Deshpande (2000) Deshpande, A. A. 2000, MNRAS, 317, 199, doi: 10.1046/j.1365-8711.2000.03631.x
  • Deshpande et al. (2000) Deshpande, A. A., Dwarakanath, K. S., & Goss, W. M. 2000, ApJ, 543, 227, doi: 10.1086/317104
  • Deshpande et al. (1992) Deshpande, A. A., McCulloch, P. M., Radhakrishnan, V., & Anantharamaiah, K. R. 1992, MNRAS, 258, 19P, doi: 10.1093/mnras/258.1.19P
  • Diamond et al. (1989) Diamond, P. J., Goss, W. M., Romney, J. D., et al. 1989, ApJ, 347, 302, doi: 10.1086/168119
  • Fich et al. (1989) Fich, M., Blitz, L., & Stark, A. A. 1989, ApJ, 342, 272, doi: 10.1086/167591
  • Foreman-Mackey et al. (2013) Foreman-Mackey, D., Hogg, D. W., Lang, D., & Goodman, J. 2013, PASP, 125, 306, doi: 10.1086/670067
  • Frail et al. (1994) Frail, D. A., Weisberg, J. M., Cordes, J. M., & Mathers, C. 1994, ApJ, 436, 144, doi: 10.1086/174888
  • Heiles (1997) Heiles, C. 1997, ApJ, 481, 193, doi: 10.1086/304033
  • Heiles & Troland (2003a) Heiles, C., & Troland, T. H. 2003a, ApJS, 145, 329, doi: 10.1086/367785
  • Heiles & Troland (2003b) —. 2003b, ApJ, 586, 1067, doi: 10.1086/367828
  • Hennebelle & Audit (2007) Hennebelle, P., & Audit, E. 2007, A&A, 465, 431, doi: 10.1051/0004-6361:20066139
  • Hill et al. (2005) Hill, A. S., Stinebring, D. R., Asplund, C. T., et al. 2005, ApJ, 619, L171, doi: 10.1086/428347
  • Hobbs et al. (2020) Hobbs, G., Manchester, R. N., Dunning, A., et al. 2020, PASA, 37, e012, doi: 10.1017/pasa.2020.2
  • Johnston et al. (2001) Johnston, S., Koribalski, B., Weisberg, J. M., & Wilson, W. 2001, MNRAS, 322, 715, doi: 10.1046/j.1365-8711.2001.04152.x
  • Johnston et al. (2003) Johnston, S., Koribalski, B., Wilson, W., & Walker, M. 2003, MNRAS, 341, 941, doi: 10.1046/j.1365-8711.2003.06468.x
  • Koyama & Inutsuka (2002) Koyama, H., & Inutsuka, S.-i. 2002, ApJ, 564, L97, doi: 10.1086/338978
  • Lauroesch & Meyer (2003) Lauroesch, J. T., & Meyer, D. M. 2003, ApJ, 591, L123, doi: 10.1086/377164
  • Lazio et al. (2009) Lazio, T. J. W., Brogan, C. L., Goss, W. M., & Stanimirović, S. 2009, AJ, 137, 4526, doi: 10.1088/0004-6256/137/5/4526
  • Manchester et al. (2005) Manchester, R. N., Hobbs, G. B., Teoh, A., & Hobbs, M. 2005, AJ, 129, 1993, doi: 10.1086/428488
  • McClure-Griffiths et al. (2009) McClure-Griffiths, N. M., Pisano, D. J., Calabretta, M. R., et al. 2009, ApJS, 181, 398, doi: 10.1088/0067-0049/181/2/398
  • McCullough & Benjamin (2001) McCullough, P. R., & Benjamin, R. A. 2001, AJ, 122, 1500, doi: 10.1086/322097
  • McKee & Zweibel (1992) McKee, C. F., & Zweibel, E. G. 1992, ApJ, 399, 551, doi: 10.1086/171946
  • Meyer (1990) Meyer, D. M. 1990, ApJ, 364, L5, doi: 10.1086/185861
  • Meyer & Blades (1996) Meyer, D. M., & Blades, J. C. 1996, ApJ, 464, L179, doi: 10.1086/310111
  • Minter et al. (2005) Minter, A. H., Balser, D. S., & Kartaltepe, J. S. 2005, ApJ, 631, 376, doi: 10.1086/432367
  • Mróz et al. (2019) Mróz, P., Udalski, A., Skowron, D. M., et al. 2019, ApJ, 870, L10, doi: 10.3847/2041-8213/aaf73f
  • Murray et al. (2018) Murray, C. E., Stanimirović, S., Goss, W. M., et al. 2018, ApJS, 238, 14, doi: 10.3847/1538-4365/aad81a
  • Newville et al. (2019) Newville, M., Otten, R., Nelson, A., et al. 2019, lmfit/lmfit-py 0.9.14, 0.9.14, Zenodo, doi: 10.5281/zenodo.3381550
  • Ray & Loeb (2017) Ray, A., & Loeb, A. 2017, ApJ, 836, 135, doi: 10.3847/1538-4357/aa5b7d
  • Riener et al. (2019) Riener, M., Kainulainen, J., Henshaw, J. D., et al. 2019, A&A, 628, A78, doi: 10.1051/0004-6361/201935519
  • Roy et al. (2012) Roy, N., Minter, A. H., Goss, W. M., Brogan, C. L., & Lazio, T. J. W. 2012, ApJ, 749, 144, doi: 10.1088/0004-637X/749/2/144
  • Rybarczyk et al. (2020) Rybarczyk, D. R., Stanimirović, S., Zweibel, E. G., et al. 2020, ApJ, 893, 152, doi: 10.3847/1538-4357/ab83f7
  • Stanimirović et al. (2010) Stanimirović, S., Weisberg, J. M., Pei, Z., Tuttle, K., & Green, J. T. 2010, ApJ, 720, 415, doi: 10.1088/0004-637X/720/1/415
  • Stanimirović & Zweibel (2018) Stanimirović, S., & Zweibel, E. G. 2018, ARA&A, 56, 489, doi: 10.1146/annurev-astro-081817-051810
  • van Straten & Bailes (2011) van Straten, W., & Bailes, M. 2011, PASA, 28, 1, doi: 10.1071/AS10021
  • Verbiest et al. (2012) Verbiest, J. P. W., Weisberg, J. M., Chael, A. A., Lee, K. J., & Lorimer, D. R. 2012, ApJ, 755, 39, doi: 10.1088/0004-637X/755/1/39
  • Weisberg et al. (1979) Weisberg, J. M., Boriakoff, V., & Rankin, J. 1979, A&A, 77, 204
  • Weisberg et al. (2008) Weisberg, J. M., Stanimirović, S., Xilouris, K., et al. 2008, ApJ, 674, 286, doi: 10.1086/523345
  • Yao et al. (2017) Yao, J. M., Manchester, R. N., & Wang, N. 2017, ApJ, 835, 29, doi: 10.3847/1538-4357/835/1/29