reception date \Acceptedacception date \Publishedpublication date
X-rays: burst — X-rays: binaries — stars: neutron — nuclear reactions, nucleosynthesis, abundances
\LETTERLABELNinjaSat monitoring of Type-I X-ray bursts from the clocked burster SRGA J144459.2604207
Abstract
The CubeSat X-ray observatory NinjaSat was launched on 2023 November 11 and has provided opportunities for agile and flexible monitoring of bright X-ray sources. On 2024 February 23, the NinjaSat team started long-term observation of the new X-ray source SRGA J144459.2604207 as the first scientific target, which was discovered on 2024 February 21 and recognized as the sixth clocked X-ray burster. Our 25-day observation covered almost the entire decay of this outburst from two days after the peak at 100 mCrab on February 23 until March 18 at a few mCrab level. The Gas Multiplier Counter onboard NinjaSat successfully detected 12 Type-I X-ray bursts with a typical burst duration of 20 s, shorter than other clocked burster systems. As the persistent X-ray emission declined by a factor of five, X-ray bursts showed a notable change in its morphology: the rise time became shorter from 4.4(7) s to 0.3(3) s (1 errors), and the peak amplitude increased by 44%. The burst recurrence time also became longer from 2 hr to 10 hr, following the relation of , where is the persistent X-ray flux, by applying a Markov chain Monte Carlo method. The short duration of bursts is explained by the He-enhanced composition of accretion matter and the relation between and by a massive neutron star. This study demonstrated that CubeSat pointing observations can provide valuable astronomical X-ray data.
1 Introduction
Type-I X-ray bursts (hereafter, X-ray bursts) are explosive transients in low-mass X-ray binaries (LMXBs), characterized by recurrent rapid increases in luminosity by order of magnitude, with emission continuing for several seconds to hours. These energetic bursts are triggered by nuclear burning in the accreting layer of a neutron star (NS) (for a review, see [Galloway & Keek (2021)]). Key observables of X-ray bursts, such as rise time, peak flux, duration, and recurrence time, depend on the properties of the accreted matter, particularly the mass accretion rate and composition (e.g., [Galloway et al. (2008)]). Among over 115 known X-ray bursters (Galloway et al., 2020), only six sources exhibit notably regular burst recurrence times, called clocked bursters. GS 182624, which was first identified by Ginga X-ray satellite (Makino, 1988), is a prominent example of clocked bursters of which the periodic features were observationally confirmed by BeppoSAX and RXTE (e.g., Ubertini et al. (1999); Galloway et al. (2004)).
Clocked bursters are valuable for constraining theoretical models, as well as understanding the properties of the NS and companion star (Heger et al. (2007); Meisel (2018); Dohi et al. (2020, 2021); Hu et al. (2021); Lam et al. (2022); Dohi et al. (2022)). Due to their regular burst recurrence times, one can investigate the relationship between the mass accretion rate and the physical conditions on the NS surface. The relation between the burst recurrence time and the persistent flux with a constant :
(1) |
serves as a powerful diagnostic. Assuming a critical fuel mass for burst ignition, a power-law index is derived, where the persistent flux is proportional to the mass accretion rate. Previous studies have found values ranging from approximately 1 to values greater than 1.111For instance, for GS 182624 (Galloway et al., 2004), for MXB 1730335 (Bagnoli et al., 2013), for IGR J174802446 (Linares et al., 2012), and for 1RXS J180408.9342058 (Dohi et al., 2024a). Theoretical studies using various X-ray burst models (e.g., Lampe et al. (2016); Dohi et al. (2024a)) suggest that depends on the physical properties of binary systems, although this remains under-explored. To further constrain the – relation, long-term monitoring of X-ray bursts is essential.
Due to their flexibility, CubeSats operating within the nanosatellite framework are well-suited for such long-term monitoring of X-ray bursts. NinjaSat, a 6U-size () CubeSat X-ray observatory, was launched into a sun-synchronous orbit at an altitude of approximately 530 km on 2023 November 11 (Enoto et al., 2020; Tamagawa et al., 2024). Equipped with two Xe-based proportional counters (Gas Multiplier Counters; GMCs) covering the 2–50 keV energy range, NinjaSat’s total effective area of approximately 32 cm2 at 6 keV is over two orders of magnitude larger than the X-ray detectors on previously launched CubeSats (Takeda et al., 2023). This significant area allows for monitoring of persistent flux and detection of X-ray bursts, even within a CubeSat mission. NinjaSat enables agile follow-up observations of bright X-ray transients and facilitates extended long-term monitoring.
X-ray bursts from the new LMXB SRGA J144459.2604207 (hereafter, SRGA J1444) are suitable targets for NinjaSat. SRGA J1444 was discovered in outburst by the Mikhail Pavlinsky ART-XC telescope onboard the Spectrum Roentgen Gamma (SRG) observatory on 2024 February 21 (Molkov et al., 2024), and later confirmed by MAXI (Mihara et al., 2024) and Swift (Chandra, 2024). Prompt follow-up observations by NICER discovered the pulsation at 447.9 Hz and X-ray bursts, along with an orbital period at 5.22 h, identifying SRGA J1444 as an accretion-powered millisecond X-ray pulsar (Ng et al., 2024). X-ray bursts from SRGA J1444 were also reported by Swift (Mariani et al., 2024), MAXI (Negoro et al., 2024), INTEGRAL (Sanchez-Fernandez et al., 2024a), Insight-HXMT (Li et al., 2024), and IXPE (Papitto et al., 2024). A quasi-periodic burst occurrence ranging from to was initially recognized in the INTEGRAL observations (Sanchez-Fernandez et al., 2024b), thus identifying SRGA J1444 as the sixth clocked burster. Subsequent IXPE observations reported the increase of recurrence time up to and the decrease of the persistent X-ray intensity, resulting in a significant low power-law index in equation (1), with (Papitto et al., 2024). NinjaSat also observed SRGA J1444 and detected 12 X-ray bursts—the first detection of X-ray bursts by a CubeSat (Takeda et al., 2024).
In this letter, we report on the long-term monitoring campaign of SRGA J1444 conducted with the newly launched CubeSat X-ray observatory NinjaSat. Throughout this letter, all errors are given at the 1 confidence level unless otherwise specified.
2 Observations and data reduction
The GMC is a non-imaging gas X-ray detector equipped with a space-proven gas electron multiplier (Tamagawa et al., 2009). Each GMC fits into a compact 1U-size (10 10 10 cm3) with a mass of 1.2 kg, making it suitable for CubeSats. The time-tagging resolution for each X-ray photon is 61 s. NinjaSat is capable of conducting the pointing observation of X-ray sources with an accuracy of less than , utilizing an X-ray collimator with a field of view (full-width at half-maximum) equipped with each GMC.
Because of the high charged-particle background, we limit the GMC operation to a low-background region, with latitudes between south and north, excluding the South Atlantic Anomaly. Currently, the astronomical operational area covers 37% of the total, although the observation efficiency for each source is also affected by the Earth occultation and the battery charging of the satellite. In the payload commissioning phase, we observed the Crab Nebula for the detector calibration and successfully detected the pulsation at 33.8 ms, confirming that the absolute time is correctly assigned to each X-ray photon with an accuracy of at least sub-milliseconds (Tamagawa et al., 2024).
NinjaSat observed SRGA J1444 for one day on 2024 February 23 (MJD 60363) and started a monitoring campaign on February 26 (MJD 60366), which continued through 2024 March 18 (MJD 60387) until the end of the outburst. The observation was conducted with one GMC (GMC1), whose detector calibration was more advanced at the end of the initial observation phase. We also observed the Crab Nebula before and after the monitoring campaign of SRGA J1444, from 2024 February 23 to February 26 and on March 19. The detector calibration with the Crab Nebula indicates that using background data from the same observation period is more appropriate than using blank sky data from different periods under the current background model. Therefore, we estimated the background level using data from the SRGA J1444 observation period when the satellite was not pointed at either SRGA J1444 or the Earth. We got effective exposures of 197.5 ks, 104.7 ks, and 16.6 ks for SRGA J1444, background data, and the Crab Nebula, respectively. Photon arrival times were corrected to the solar system barycenter with FTOOLS barycen using the DE-405 planetary ephemeris with source coordinates R.A. , Decl. obtained by Chandra (Illiano et al., 2024).
The main background component in the GMC data is the non-X-ray background, which includes charged particle events and electrical noise events. The GMC has two readout pads: a circular inner readout electrode with a radius of 25.0 mm and an annular outer electrode with radii between 25.1 mm and 33.5 mm. Signals from each readout pad are digitized by a 12-bit analog-to-digital converter at a sampling rate of 25 MHz, followed by onboard analysis to extract waveform parameters—such as pulse height, rise time, and the Pearson correlation coefficient between the inner and outer waveforms —which are subsequently downlinked. Charged particle events entering perpendicular to the readout pad can be rejected because their signal rise time ( 1 s) is longer than that of X-ray events ( ns) due to the difference in the distributed length of the electron cloud along the drift direction. On the other hand, events from the parallel direction leave signals on both pads, resulting in a relatively high correlation coefficient . This allows them to be distinguished from X-ray events, except when X-rays enter between the pads. Additionally, the event cut based on the correlation coefficient is also useful for the common-mode electrical noise simultaneously triggered in both channels, which typically has a value of .
In this letter, we only use the X-ray count rate for the following analysis without the response for the spectral discussions. The persistent count rate in the 2–10 keV band is converted to X-ray intensity in units of mCrab, i.e., a flux referenced to the count rate of the Crab Nebula. Because the detector calibration is still ongoing, we employed a tentative event selection criterion: selecting events with signal rise times in the range of 200–800 ns and correlation coefficients of less than 0.4. The average raw background rates of the inner and outer regions in the 2–10 keV band are 6.0 counts s-1 and 10.6 counts s-1, respectively. After applying the event cut, the rates are reduced to 0.294 0.003 counts s-1 and 2.26 0.02 counts s-1, respectively. The event cut based on the correlation coefficient between the inner and outer waveforms is sensitive to events that are simultaneously triggered in both regions. Consequently, it functions similarly to the anti-coincidence method, making it more effective in the inner region, which is surrounded by the outer region. Thus, we analyzed only event data of the inner region for the persistent flux evaluation to obtain a higher signal-to-noise ratio, while we used event data extracted from both regions for the X-ray burst analysis. The background-subtracted rate of the Crab Nebula in the 2–10 keV band is estimated to be 11.89 0.03 counts s-1. The time variation in the 3.0-hr binned background light curve shows a slight periodicity at approximately 1 and 6 days and follows a Gaussian distribution with a 1 width of 0.025 counts s-1, corresponding to 2.5 mCrab.
3 Data analysis and results
To search for X-ray bursts, we extracted 10-s binned light curves from all screening data and clearly detected 12 X-ray bursts (IDs 1–12), as listed in table 3. These bursts exhibit a peak X-ray intensity of Crab and lasting 20 s (Takeda et al., 2024), consistent with observations reported by NICER and SRG (Ng et al., 2024; Molkov et al., 2024).
Figure 1 shows the persistent light curve of SRGA J1444 in the 2–10 keV band observed with NinjaSat, compared with that from MAXI (Matsuoka et al., 2009). The 2–10 keV X-ray intensity observed with MAXI was calculated using publicly available data.222http://maxi.riken.jp/top/slist.html The outburst of SRGA J1444 reached the maximum X-ray intensity at 100 mCrab at MJD 60361 and then gradually decayed for nearly 30 days to the background level. The NinjaSat monitoring campaign covered almost the entire outburst decay phase until MJD 60387. The flux evolution observed with NinjaSat is consistent with that of MAXI, achieving comparable statistical errors with 3.0-hr bins to those of the MAXI daily light curve. NinjaSat enables us to track finer variations in the persistent X-ray intensity.
Properties of Type-I X-ray bursts from SRGA J1444 observed with NinjaSat. ID MJD ∗*∗*footnotemark: †\dagger†\daggerfootnotemark: (hr) §\S§\Sfootnotemark: (s) ∥\|∥\|footnotemark: (s) #\##\#footnotemark: (s) ∗∗**∗∗**footnotemark: (counts s-1) Fluence (counts) ††\dagger\dagger††\dagger\daggerfootnotemark: (s) §§\S\S§§\S\Sfootnotemark: (hr) /d.o.f. 1 60367.19877 - 5.3 0.6 9.6 0.8 5.8 1.5 12.0 1.1 216 29 18.1 2.9 - 82.1/105 2 60367.73134 12.782 0.6 1.7 10.2 2.1 11.9 2.0 12.9 1.3 289 48 22.4 4.3 2.130 107.8/103 3 60368.68673 22.929 3.4 1.4 13.6 1.4 3.0 1.0 11.4 1.0 209 29 18.3 3.0 2.293 82.1/102 4 60369.67668 23.759 5.2 0.7 4.0 1.1 8.8 1.5 14.1 1.5 216 35 15.4 3.0 2.970 103.4/102 5 60370.85748 28.339 1.4 1.6 3.5 1.9 9.4 1.4 18.2 2.3 248 54 13.6 3.4 3.149 113.7/104 6 60372.57310 41.175 3.5 0.8 10.4 1.3 6.0 1.1 13.3 1.2 241 31 18.2 2.9 3.167 91.2/102 7 60373.76358 28.571 1.9 0.4 9.4 1.0 5.7 1.4 14.7 1.3 237 33 16.0 2.6 3.571 101.0/103 8 60374.94451 28.342 0.3 1.3 10.4 1.7 6.2 1.3 14.1 1.3 235 39 16.7 3.2 4.049 116.4/103 9 60376.16844 29.374 0.5 0.1 8.0 1.3 9.3 2.3 13.6 1.3 239 43 17.6 3.6 4.896 114.7/102 10 60376.72939 13.463 0.5 0.8 9.6 1.3 5.2 1.2 16.1 1.4 244 36 15.1 2.6 6.731 110.5/100 11 60377.05892 7.909 0.5 1.0 7.2 1.2 5.1 1.2 18.0 1.7 227 39 12.6 2.4 7.909 111.2/103 12∥∥\|\|∥∥\|\|footnotemark: 60380.40925 80.408 - - - - - - 10.051 - 1–11 - - 1.4 0.4 9.8 0.6 7.7 0.5 13.3 0.4 241 13 18.1 1.2 - 103.9/105 1–3 - - 4.4 0.7 10.9 1.1 5.3 0.9 12.0 0.7 232 23 19.3 2.3 - 91.4/105 4–6 - - 4.2 0.6 4.9 1.4 8.9 0.9 15.1 1.2 241 32 15.9 2.4 - 122.5/105 7–8 - - 2.5 0.4 9.0 0.7 5.9 0.7 14.3 0.8 230 19 16.1 1.6 - 106.1/105 9–11 - - 0.3 0.3 5.8 1.3 9.6 1.2 17.0 1.2 264 35 15.5 2.3 - 145.1/105 {tabnote}
MJD: burst onset time in Modified Julian Date (MJD).
†\dagger†\daggerfootnotemark: : elapsed time (hr) since the previous burst detected with NinjaSat.
§\S§\Sfootnotemark: : time to reach the peak from the onset in a unit of seconds.
∥\|∥\|footnotemark: : duration of the plateau (s).
#\##\#footnotemark: : decay time constant (s).
: burst amplitude, i.e., the count rate during the plateau (counts s-1).
††\dagger\dagger††\dagger\daggerfootnotemark: : equivalent duration (s), ratio of burst integrated fluence to peak flux.
§§\S\S§§\S\Sfootnotemark: : average burst recurrence time (hr) (see section 3.2).
∥∥\|\|∥∥\|\|footnotemark: Only the burst onset times, , and are listed because the burst was truncated by the boundary of the observations.

3.1 Evolution of burst profiles
Figure 2(a) shows the overall averaged burst profile in the 2–20 keV band. The burst profile is characterized by a fast linear rise, followed by a plateau and an exponential decay, similar to those reported in NICER and SRG observations (Ng et al., 2024; Molkov et al., 2024). To investigate the evolution of the burst profile during the outburst with better statistics, we combined the first 11 bursts into four intervals based on the persistent X-ray intensity. Then we fitted each light curve with a burst model ( as a function of time, ), described by
(2) |
where is the persistent rate (), is the burst amplitude (), is the burst onset time (s), is the time to reach the peak from the onset (s), is the duration of the plateau (s), and is the decay time constant (s). Figures 2(b)–(e) show the averaged profiles at each interval (IDs 1–3, 4–6, 7–8, and 9–11) with the best-fit models. The corresponding best-fit parameters are given in table 3. The X-ray burst profiles showed a significant evolution in morphology; The burst rise time () became faster, and its amplitude () increased as the outburst decayed. In addition, we evaluated the burst fluence using the fit results and then employed the equivalent burst duration , which is defined as the ratio of burst fluence to peak intensity, as a useful indicator independent of the uncertainty of the distance. Figure 3 shows the dependence of , , the burst fluence, and on the persistent level, which are estimated by linear interpolation and averaging the NinjaSat light curve (figure 1) at each interval. While the persistent level, which is proportional to the mass accretion rate, decreased from approximately 63 mCrab to 13 mCrab, the rise time decreased from s to s, and the amplitude increased by 44% from to . In contrast, the fluence showed no significant changes, with average values of 240 counts. The equivalent duration marginally decreased from s to s. The best-fit parameters for each burst are also listed in table 3. The burst onset times are determined with an accuracy of approximately 1 s, and the elapsed times since the previous burst range from hr to hr.


We assessed the systematic uncertainties in our burst profile analysis based on count rates without performing spectral analysis. Fu et al. (2024) reported Insight-HXMT’s observations of photospheric radius expansions (PREs) in 14 out of a total of 60 bursts. They presented the evolution of blackbody temperature, which is kept high (roughly 2–3 keV) in spite of the occurrence of PREs. This fact justifies our approach using the count rate rather than the bolometric flux. This stems from a small variation of count rate to bolometric flux conversion factor by only 15% during bursts, given the effective area of the GMC, which is comparable to the 1 statistical error of light curves as shown in figure 2. Furthermore, since the PRE effects on the spectrum are remarkable in a short timescale of less than 3 s, they little affect characteristic parameters such as in an overall burst.
3.2 MCMC inference of burst recurrence time as a function of persistent flux
During the campaign, NinjaSat monitored the evolution of persistent X-ray intensity and detected 12 X-ray bursts (figure 1). Based on a comparison with IXPE observations (Papitto et al., 2024), only burst IDs 10 and 11 were confirmed to be consecutive, with the burst recurrence time of hr. To quantify the – relation in SRGA J1444, we developed a new method using a Markov chain Monte Carlo (MCMC) approach, which is applicable even when several bursts fall within observation gaps and are consequently missed.
3.2.1 MCMC method
The empirical – relation in equation (1) indicates the existence of the conserved quantity for a pair of bursts. When the persistent flux varies substantially between bursts, equation (1) cannot be applied directly to the observed data and must instead be transformed into an integral form. Moreover, due to incomplete observational coverage, bursts are often missed, as in the case of the NinjaSat observations. Even in such cases, the integral of over the interval between burst detection times should be equal to multiplied by the actual number of burst-to-burst intervals, , between two detected bursts:
(3) |
where and are the burst onset times. We employed an MCMC algorithm to estimate the parameters in equation (3) that best match the observations. Bayesian statistics using MCMC methods have recently been applied to a wide range of fields in astrophysics, including modeling X-ray bursts demonstrated in several studies (e.g., Goodwin et al. (2019); Johnston et al. (2020); Galloway et al. (2024)). We used a discretized form of equation (3) to evaluate the simple Gaussian likelihood function, , by comparing predicted values with the observational data, , using
(4) |
where is measured in units of the overall averaged X-ray intensity of mCrab, is the time width to interpolate the persistent flux in units of 1 day, is the observational error, and is iterated over each observed burst.
For the MCMC calculations, we used the open-source Python package emcee (Foreman-Mackey et al., 2013). To determine the persistent fluxes outside observation intervals, we linearly interpolated the values between observations with a time bin of 1/864 day (= 100 s). We employed flat prior distributions for and , setting broad acceptable ranges333The range of was set based on its maximum and minimum values obtained from two consecutive bursts (IDs 10–11), where assuming range of 0.5–1.5.: and . Although is expected to be close to integer values, slight variations in from burst to burst shift the posterior distributions of away from integers. Therefore, for the prior of , we employed a model based on multiple Gaussian functions, each centered on an integer value in the range of 1–20 with a standard deviation set so that . As an exceptional case, the center value was set to 1 for because burst IDs 10 and 11 are consecutive, as described above. Additionally, we impose another constraint on the average burst recurrence times , setting hr. This is based on the report that after MJD 60367, corresponding to the NinjaSat burst detection period, was longer than 2 hr (Molkov et al., 2024).
We ran the MCMC chains with 200 walkers for steps. The walkers were uniformly initialized within the ranges of the flat prior distributions to comprehensively explore the -parameter space. Given the possibility that the sampled distribution could be multimodal, we used a combination of moves, DEMove and DESnookerMove, with weights of 80% and 20%, respectively, as suggested in the emcee documentation.444https://emcee.readthedocs.io/en/stable/
3.2.2 Results
The integrated autocorrelation time is a reliable indicator for assessing the convergence of the MCMC chain. In emcee, running the chain for 50 samples generally ensures the convergence55footnotemark: 5. We estimated using steps from the last half of the total and found . Therefore, the initial steps in each chain were discarded as burn-in to ensure full convergence.
Two-dimensional posterior distributions of , , and are shown in figure 4, with marginalized histograms along the diagonal. Each parameter space exhibits multi-modal distributions, which can be attributed to the fact that the only strictly constrained value is . This allows for multiple combinations of and that result in values close to integers. Nevertheless, it is also evident that each parameter has a prominent peak. To evaluate the prominent peaks in multi-modal distributions, we calculate the highest posterior density (HPD) intervals, which are particularly suited for cases with multimodality or asymmetry (e.g., Gelman et al. (2014)), using the hdi function from ArviZ—a Python package for exploratory analysis of Bayesian models (Kumar et al., 2019)—with the ’multimodal’ option. The maximum likelihood estimates for each 1-D marginalized posterior, with 68% HPD intervals, are listed in table 3.2.2. The inferred power-law index is significantly lower than 1, . The ratios of the maximum likelihood estimates to their nearest integer values are well within 5%, except for , which exhibits a residual of approximately 25%. The average burst recurrence times for each observed burst increased from hr to hr, as given in table 3.

Maximum likelihood estimates for each 1D marginalized posterior distribution with 68% highest posterior density intervals. {tabnote}
3.3 Recurrence time variation with burst duration
The compositions of the accreted matter, i.e., mass fractions of hydrogen (), helium (), and heavier CNO elements or metallicity (), are reflected in the burst properties such as the burst equivalent duration and the recurrence time (Lampe et al., 2016). Although only an upper limit of 10.6 kpc on the source distance has been determined for SRGA J1444 (Ng et al., 2024), both and are independent of the distance, making these parameters useful for comparing observations and theoretical predictions across different sources. Figure 5 shows the – relation of SRGA J1444, alongside the clocked burster GS 182624, which has a near-solar composition (e.g., Johnston et al. (2020)), and the ultra-compact binary 4U 1820303, believed to be a pure-He burster with a low-mass He white dwarf (e.g., Cumming (2003); Galloway et al. (2008)). The burst recurrence times of GS 182624 and 4U 1820303 are taken from table 2 in Galloway et al. (2017), and are calculated using the burst fluence and peak flux from the same table. We also show the theoretical relation from our model HERES with and (for the HERES model, see Dohi et al. (2020)), which is in line with various observations of SRGA J1444 (Dohi et al., 2024b). To characterize the observed – relations, we fitted a linear model () to each data. The average burst duration of SRGA J1444 is less than half that of GS 182624 and three times that of 4U 1820303. Furthermore, the negative slope between and becomes shallower for sources with shorter values.

4 Discussion and conclusion
In this letter, we present the long-term monitoring of SRGA J1444 conducted with NinjaSat over a period of approximately 25 days. We found that SRGA J1444 exhibited X-ray bursts with a fast rise time of 5 s and a short duration of s (IDs 1–11), the latter of which is consistent with the values derived by the spectral analysis from other satellites, such as IXPE ( s, Papitto et al. (2024)) and SRG ( s, Molkov et al. (2024)), with an accuracy of 10%. The fast rise time and short duration are characteristic features of sources with relatively He-rich accreted fuel. This is because He burns during the burst via the triple- reaction, which proceeds on a much shorter time scale than the H burning via the hot-CNO cycle, , and processes. The observed burst duration in SRGA J1444 is longer than in pure-He bursters but shorter than sources with the solar composition (figure 5). Given that no photospheric radius expansion burst has been observed from SRGA J1444, including with observations from other satellites, it is reasonable to assume that SRGA J1444 has a relatively He-enhanced accreted fuel compared to the solar composition. The He-enhanced scenario with and is further supported by the theoretical predictions from the HERES model (Dohi et al., 2024b).
We show that the recurrence time in SRGA J1444 is roughly inversely proportional to the persistent X-ray intensity with a power-law index . We also noted that are closely matched to integer values within 5%, except for . The deviations from integers could be attributed to the average variation in between each detected burst and/or to limitations of the simplified burst model expressed in equation (3). Contributing factors may include incomplete observational coverage, potential variations in persistent flux on timescales of 3.0 hr or shorter, which corresponds to the time resolution of the light curve (figure 1), and long-term spectral variations associated with the outburst. Our results are consistent with the IXPE observation reported by Papitto et al. (2024), which shows with recurrence time deviations ranging from a few % to roughly 10%555The variation of the recurrence time is reflected in the deviation of from their nearest integer values in our formulation expressed in equation (3). (see also Fu et al. 2024).
The estimated index of SRGA J1444 is the lowest value observed among X-ray bursters (section 1). Dohi et al. (2024a) investigated the dependence on the equation of state (EOS) and NS masses in the range of to . They found that compacted NS models tend to have lower values of .666Note that their results were obtained for the case of H-rich accreted fuel, unlike the He-enhanced scenario we expected. However, because no significant correlation between the power-law index and the composition of the accreted fuel was found (Lampe et al., 2016), their conclusion holds. The value of for SRGA J1444 does not match the model predictions for NS masses below , suggesting that it is a more massive NS. A comparison of observed value in SRGA J1444 with theoretical models that encompass a broader range of masses above could provide valuable insights for constraining the mass and the EOS of NSs.
The maximum observed value of the burst recurrence time can be used to constrain the composition of the accreted fuel. Both the NinjaSat and IXPE observations indicate that the – relation with remained valid up to at least hr. This implies that during this period, H burned stably via the hot CNO cycle between each burst without depletion, followed by a mixed H/He burst (Case 1; Fujimoto et al. (1981)). In this bursting regime, the accreted H is depleted in time of (Lampe et al. (2016)). Using the maximum recurrence time of hr observed in SRGA J1444, we obtain a constraint 7.9 hr , where represents the gravitational redshift at the photosphere. Assuming a solar hydrogen abundance of , along with a canonical NS with a mass and the radius km (giving ), we derive an upper limit for the CNO mass fraction as . Similarly, for a massive NS with and the radius km, the upper limit becomes . Note that for the He-enhanced scenario with and , as inferred by Dohi et al. (2024b), is estimated to be 11.0 hr, satisfying the constraint regardless of the NS mass.
This study demonstrated that CubeSat pointing observations can provide valuable astronomical X-ray data. Even a compact detector, with an effective area of several tens of cm2 onboard a CubeSat, can successfully observe both burst and persistent emissions. Given the rarity of X-ray bursts, which occur during less than 1% of the total observation time (Galloway et al., 2020), CubeSats offer a complementary approach to large canonical observatories, providing long-term, flexible observations critical for detecting these transient events.
This project was supported by JSPS KAKENHI (JP23KJ1964, JP17K18776, JP18H04584, JP20H04743, JP20H05648, JP21H01087, JP23K19056, JP24H00008, JP24K00673). T.T. was supported by the JSPS Research Fellowships for Young Scientists. T.E. was supported by “Extreme Natural Phenomena” RIKEN Hakubi project. N.N. received support from the RIKEN Intensive Research Project (FY2024–2025).
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