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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 J144459.2-604207

Tomoshi Takeda11affiliation: Department of Physics, Tokyo University of Science, 1-3 Kagurazaka, Shinjuku, Tokyo 162-8601, Japan 22affiliation: RIKEN Cluster for Pioneering Research (CPR), 2-1 Hirosawa, Wako-shi, Saitama 351-0198, Japan    Toru Tamagawa    22affiliationmark: 33affiliation: RIKEN Nishina Center, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan 11affiliationmark:    Teruaki Enoto    44affiliation: Department of Physics, Kyoto University, Kitashirakawa Oiwake, Sakyo, Kyoto 606-8502, Japan 22affiliationmark:    Takao Kitaguchi    22affiliationmark:    Yo Kato22affiliationmark:    Tatehiro Mihara22affiliationmark:    Wataru Iwakiri55affiliation: International Center for Hadron Astrophysics, Chiba University, 1-33 Yayoi, Inage, Chiba, Chiba 263-8522, Japan    Masaki Numazawa66affiliation: Department of Physics, Tokyo Metropolitan University, 1-1 Minamiosawa, Hachioji, Tokyo 192-0397, Japan    Naoyuki Ota11affiliationmark: 33affiliationmark:    Sota Watanabe11affiliationmark: 22affiliationmark:    Arata Jujo11affiliationmark: 33affiliationmark:    Amira Aoyama11affiliationmark: 22affiliationmark:    Satoko Iwata11affiliationmark: 33affiliationmark:    Takuya Takahashi11affiliationmark: 33affiliationmark:    Kaede Yamasaki11affiliationmark: 33affiliationmark:    Chin-Ping Hu77affiliation: Department of Physics, National Changhua University of Education, Changhua, Changhua 50007, Taiwan    Hiromitsu Takahashi88affiliation: Department of Physics, Hiroshima University, 1-3-1 Kagamiyama, Higashi-Hiroshima, Hiroshima 739-8526, Japan    Akira Dohi22affiliationmark: 99affiliation: Interdisciplinary Theoretical and Mathematical Sciences Program (iTHEMS), RIKEN, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan    Nobuya Nishimura1010affiliation: Center for Nuclear Study (CNS), The University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo 113-0033, Japan 22affiliationmark: 1111affiliation: National Astronomical Observatory of Japan (NAOJ), 2-21-1 Osawa, Mitaka, Tokyo 181-8588, Japan    Ryosuke Hirai22affiliationmark: 1212affiliation: School of Physics and Astronomy, Monash University, Clayton, VIC 3800, Australia 1313affiliation: OzGrav: The ARC Centre of Excellence for Gravitational Wave Discovery, Clayton, VIC 3800, Australia    Yuto Yoshida11affiliationmark: 33affiliationmark:    Hiroki Sato1414affiliation: Department of System Engineering and Science, Shibaura Institute of Technology, 307 Fukasaku, Minuma, Saitama, Saitama 337-8570, Japan 33affiliationmark:    Syoki Hayashi11affiliationmark: 33affiliationmark:    Yuanhui Zhou11affiliationmark: 33affiliationmark:    Keisuke Uchiyama11affiliationmark: 33affiliationmark:    Hirokazu Odaka1515affiliation: Department of Earth and Space Science, Osaka University, 1-1 Machikaneyama, Toyonaka, Osaka 560-0043, Japan    Tsubasa Tamba1616affiliation: Institute of Space and Astronautical Science, JAXA, 3-1-1 Yoshinodai, Chuo, Sagamihara, Kanagawa 252-5210, Japan    Kentaro Taniguchi22affiliationmark: [email protected]
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 J144459.2-604207 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 \sim100 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 \sim20 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 (1σ\sigma errors), and the peak amplitude increased by 44%. The burst recurrence time Δtrec\Delta t_{\rm rec} also became longer from 2 hr to 10 hr, following the relation of ΔtrecFper0.84\Delta t_{\rm rec}\propto F_{\rm per}^{-0.84}, where FperF_{\rm per} 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 Δtrec\Delta t_{\textrm{rec}} and FperF_{\rm per} 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 1826-24, 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 Δtrec\Delta t_{\rm rec} and the persistent flux FperF_{\rm per} with a constant CC:

Δtrec=CFperη\Delta t_{\rm rec}=CF_{\rm per}^{-\eta} (1)

serves as a powerful diagnostic. Assuming a critical fuel mass for burst ignition, a power-law index η=1\eta=1 is derived, where the persistent flux FperF_{\rm per} is proportional to the mass accretion rate. Previous studies have found η\eta values ranging from approximately 1 to values greater than 1.111For instance, η=1.05±0.02\eta=1.05\pm{0.02} for GS 1826-24 (Galloway et al., 2004), η=0.95±0.03\eta=0.95\pm{0.03} for MXB 1730-335 (Bagnoli et al., 2013), η1\eta\gtrsim 1 for IGR J17480-2446 (Linares et al., 2012), and η>1.35\eta>1.35 for 1RXS J180408.9-342058 (Dohi et al., 2024a). Theoretical studies using various X-ray burst models (e.g., Lampe et al. (2016); Dohi et al. (2024a)) suggest that η\eta depends on the physical properties of binary systems, although this remains under-explored. To further constrain the Δtrec\Delta t_{\rm rec}FperF_{\rm per} 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 (10×20×30cm310\times 20\times 30~{}{\rm cm}^{3}) 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 J144459.2-604207 (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 1.7hr\sim 1.7~{}{\rm hr} to 2.9hr\sim 2.9~{}{\rm hr} 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 7.9hr\sim 7.9~{}{\rm hr} and the decrease of the persistent X-ray intensity, resulting in a significant low power-law index in equation (1), with η0.8\eta\sim 0.8 (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 1σ\sigma 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 ×\times 10 ×\times 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 μ\mus. NinjaSat is capable of conducting the pointing observation of X-ray sources with an accuracy of less than 0\fdg10\fdg 1, utilizing an X-ray collimator with a 2\fdg12\fdg 1 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 30\sim 30^{\circ} south and 40\sim 40^{\circ} north, excluding the South Atlantic Anomaly. Currently, the astronomical operational area covers \sim 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. =221\fdg24558=221\fdg 24558, Decl.=60\fdg69869=-60\fdg 69869 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 RR—which are subsequently downlinked. Charged particle events entering perpendicular to the readout pad can be rejected because their signal rise time (\simμ\mus) is longer than that of X-ray events (400\sim 400 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 RR. 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 RR is also useful for the common-mode electrical noise simultaneously triggered in both channels, which typically has a value of R0.95R\sim 0.95.

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 RR 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 ±\pm 0.003 counts s-1 and 2.26 ±\pm 0.02 counts s-1, respectively. The event cut based on the correlation coefficient RR 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 ±\pm 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 1σ\sigma 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 1\sim 1 Crab and lasting \sim 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 \sim 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.

\tbl

Properties of Type-I X-ray bursts from SRGA J1444 observed with NinjaSat. ID MJD ∗*∗*footnotemark: * Δtpre\Delta t_{\rm pre}†\dagger†\daggerfootnotemark: \dagger (hr) triset_{\rm rise}§\S§\Sfootnotemark: §\S (s) tplt_{\rm pl}∥\|∥\|footnotemark: \| (s) τD\tau_{\rm D}#\##\#footnotemark: #\# (s) AA∗⁣∗**∗⁣∗**footnotemark: ** (counts s-1) Fluence (counts) τ\tau†⁣†\dagger\dagger†⁣†\dagger\daggerfootnotemark: \dagger\dagger (s) Δtrec\Delta t_{\rm rec}§​§\S\S§​§\S\Sfootnotemark: §§\S\S (hr) χ2\chi^{2}/d.o.f. 1 60367.19877 - 5.3 ±\pm 0.6 9.6 ±\pm 0.8 5.8 ±\pm 1.5 12.0 ±\pm 1.1 216 ±\pm 29 18.1 ±\pm 2.9 - 82.1/105 2 60367.73134 12.782 0.6 ±\pm 1.7 10.2 ±\pm 2.1 11.9 ±\pm 2.0 12.9 ±\pm 1.3 289 ±\pm 48 22.4 ±\pm 4.3 2.130 107.8/103 3 60368.68673 22.929 3.4 ±\pm 1.4 13.6 ±\pm 1.4 3.0 ±\pm 1.0 11.4 ±\pm 1.0 209 ±\pm 29 18.3 ±\pm 3.0 2.293 82.1/102 4 60369.67668 23.759 5.2 ±\pm 0.7 4.0 ±\pm 1.1 8.8 ±\pm 1.5 14.1 ±\pm 1.5 216 ±\pm 35 15.4 ±\pm 3.0 2.970 103.4/102 5 60370.85748 28.339 1.4 ±\pm 1.6 3.5 ±\pm 1.9 9.4 ±\pm 1.4 18.2 ±\pm 2.3 248 ±\pm 54 13.6 ±\pm 3.4 3.149 113.7/104 6 60372.57310 41.175 3.5 ±\pm 0.8 10.4 ±\pm 1.3 6.0 ±\pm 1.1 13.3 ±\pm 1.2 241 ±\pm 31 18.2 ±\pm 2.9 3.167 91.2/102 7 60373.76358 28.571 1.9 ±\pm 0.4 9.4 ±\pm 1.0 5.7 ±\pm 1.4 14.7 ±\pm 1.3 237 ±\pm 33 16.0 ±\pm 2.6 3.571 101.0/103 8 60374.94451 28.342 0.3 ±\pm 1.3 10.4 ±\pm 1.7 6.2 ±\pm 1.3 14.1 ±\pm 1.3 235 ±\pm 39 16.7 ±\pm 3.2 4.049 116.4/103 9 60376.16844 29.374 0.5 ±\pm 0.1 8.0 ±\pm 1.3 9.3 ±\pm 2.3 13.6 ±\pm 1.3 239 ±\pm 43 17.6 ±\pm 3.6 4.896 114.7/102 10 60376.72939 13.463 0.5 ±\pm 0.8 9.6 ±\pm 1.3 5.2 ±\pm 1.2 16.1 ±\pm 1.4 244 ±\pm 36 15.1 ±\pm 2.6 6.731 110.5/100 11 60377.05892 7.909 0.5 ±\pm 1.0 7.2 ±\pm 1.2 5.1 ±\pm 1.2 18.0 ±\pm 1.7 227 ±\pm 39 12.6 ±\pm 2.4 7.909 111.2/103 12∥∥\|\|∥∥\|\|footnotemark: \|\| 60380.40925 80.408 - - - - - - 10.051 - 1–11 - - 1.4 ±\pm 0.4 9.8 ±\pm 0.6 7.7 ±\pm 0.5 13.3 ±\pm 0.4 241 ±\pm 13 18.1 ±\pm 1.2 - 103.9/105 1–3 - - 4.4 ±\pm 0.7 10.9 ±\pm 1.1 5.3 ±\pm 0.9 12.0 ±\pm 0.7 232 ±\pm 23 19.3 ±\pm 2.3 - 91.4/105 4–6 - - 4.2 ±\pm 0.6 4.9 ±\pm 1.4 8.9 ±\pm 0.9 15.1 ±\pm 1.2 241 ±\pm 32 15.9 ±\pm 2.4 - 122.5/105 7–8 - - 2.5 ±\pm 0.4 9.0 ±\pm 0.7 5.9 ±\pm 0.7 14.3 ±\pm 0.8 230 ±\pm 19 16.1 ±\pm 1.6 - 106.1/105 9–11 - - 0.3 ±\pm 0.3 5.8 ±\pm 1.3 9.6 ±\pm 1.2 17.0 ±\pm 1.2 264 ±\pm 35 15.5 ±\pm 2.3 - 145.1/105 {tabnote}

∗*∗*footnotemark: *

MJD: burst onset time in Modified Julian Date (MJD).
†\dagger†\daggerfootnotemark: \dagger Δtpre\Delta t_{\rm pre}: elapsed time (hr) since the previous burst detected with NinjaSat.
§\S§\Sfootnotemark: §\S triset_{\rm rise}: time to reach the peak from the onset in a unit of seconds.
∥\|∥\|footnotemark: \| tplt_{\rm pl}: duration of the plateau (s).
#\##\#footnotemark: #\# τD\tau_{\rm D}: decay time constant (s).

∗⁣∗**∗⁣∗**footnotemark: **

AA: burst amplitude, i.e., the count rate during the plateau (counts s-1).
†⁣†\dagger\dagger†⁣†\dagger\daggerfootnotemark: \dagger\dagger τ\tau: equivalent duration (s), ratio of burst integrated fluence to peak flux.
§​§\S\S§​§\S\Sfootnotemark: §§\S\S Δtrec\Delta t_{\rm rec}: average burst recurrence time (hr) (see section 3.2).
∥∥\|\|∥∥\|\|footnotemark: \|\| Only the burst onset times, Δtpre\Delta t_{\rm pre}, and Δtave\Delta t_{\rm ave} are listed because the burst was truncated by the boundary of the observations.

Refer to caption
Figure 1: 2–10 keV light curves of SRGA J1444 monitored by NinjaSat (red) and MAXI (black) with the binsizes of 3.0 hr and 24 hr, respectively. The NinjaSat light curve is calculated using event data from the inner region with a subtraction of the averaged background rate (0.294 counts s-1). The time intervals between 5-s before and 50-s after the burst onset are excluded to show the persistent flux decay, while these burst onsets are indicated by red vertical lines. Alt text: Time series plot.

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 (f(t)f(t) as a function of time, tt), described by

f(t) =
{cpertt0Atrise(tt0)+cpert0<tt0+triseA+cpert0+trise<tt0+trise+tplAexp(tt0trisetplτD)+cpert>t0+trise+tpl,
\begin{array}[]{l}\leftline{\hbox{$f(t)$ =}}\\ \begin{cases}c_{\rm per}&t\leq t_{0}\\ \frac{A}{t_{\rm rise}}\left(t-t_{0}\right)+c_{\rm per}&t_{0}<t\leq t_{0}+t_{\rm rise}\\ A+c_{\rm per}&t_{0}+t_{\rm rise}<t\leq t_{0}+t_{\rm rise}+t_{\rm pl}\\ A\exp\left(-\frac{t-t_{0}-t_{\rm rise}-t_{\rm pl}}{\tau_{D}}\right)+c_{\rm per}&t>t_{0}+t_{\rm rise}+t_{\rm pl}\end{cases},\end{array}
(2)

where cperc_{\rm per} is the persistent rate (countss1\rm counts~{}s^{-1}), AA is the burst amplitude (countss1\rm counts~{}s^{-1}), t0t_{0} is the burst onset time (s), triset_{\rm rise} is the time to reach the peak from the onset (s), tplt_{\rm pl} is the duration of the plateau (s), and τD\tau_{D} 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 (triset_{\rm rise}) became faster, and its amplitude (AA) increased as the outburst decayed. In addition, we evaluated the burst fluence using the fit results and then employed the equivalent burst duration τ\tau, 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 triset_{\rm rise}, AA, the burst fluence, and τ\tau 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 trise=4.4±0.7t_{\rm rise}=4.4\pm 0.7 s to trise=0.3±0.3t_{\rm rise}=0.3\pm 0.3 s, and the amplitude increased by 44% from A=11.8±0.7A=11.8\pm 0.7 countss1\rm counts~{}s^{-1} to A=17.0±1.2A=17.0\pm 1.2 countss1\rm counts~{}s^{-1}. In contrast, the fluence showed no significant changes, with average values of 240 counts. The equivalent duration marginally decreased from τ=19.3±2.3\tau=19.3\pm 2.3 s to τ=15.5±2.3\tau=15.5\pm 2.3 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 Δtpre\Delta t_{\rm pre} range from 7.9097.909 hr to 80.40880.408 hr.

Refer to caption
Figure 2: Average profiles of X-ray burst IDs 1–11, 1–3, 4–6, 7–8, and 9–11 (top to bottom). These light curves are calculated in the 2–20 keV energy band at 1 s resolution after subtraction of the persistent emission based on fitting results. The blue solid line represents the best-fit model of the linear rise, plateau, and exponential decay (see section 3.1) applied to the overall average profile (a). In the lower four panels, the red solid lines are the best-fit models for each burst profile (b)–(e), where the best-fit average profile (blue solid line) is also shown for comparison. The best-fit parameters are summarized in table 3. Alt text: Five line graphs.
Refer to caption
Figure 3: Flux dependence of the various parameters in burst profiles: time to reach the peak flux triset_{\rm rise}, burst amplitude AA, burst fluence, and equivalent duration τ\tau (top to bottom). The X-axis represents the persistent X-ray intensity estimated by linear interpolation and averaging the NinjaSat light curves at each interval (IDs 1–3, 4–6, 7–8, and 9–11). Alt text: Four scatter plots.

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 ±\pm15% during bursts, given the effective area of the GMC, which is comparable to the 1σ\sigma 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 \sim3 s, they little affect characteristic parameters such as τ\tau 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 Δtrec=7.9\Delta t_{\rm rec}=7.9 hr. To quantify the Δtrec\Delta t_{\rm rec}FperF_{\rm per} 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 Δtrec\Delta t_{\rm rec}FperF_{\rm per} relation in equation (1) indicates the existence of the conserved quantity CC 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 FperηF_{\rm per}^{\eta} over the interval between burst detection times should be equal to CC multiplied by the actual number of burst-to-burst intervals, nin_{i}, between two detected bursts:

titi+1Fperηdt=niC,\int_{t_{i}}^{t_{i+1}}F_{\rm per}^{\eta}{\rm d}t=n_{i}C, (3)

where tit_{i} and ti+1t_{i+1} 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, p(D|η,C,ni)p(D|\eta,C,n_{i}), by comparing predicted values with the observational data, DD, using

p(D|η,C,ni)=i12πσi2exp[(FperηtbinniC)22σi2],p(D|\eta,C,n_{i})=\\ \prod_{i}\frac{1}{\sqrt{2\pi\sigma_{i}^{2}}}\exp\left[-\frac{(\sum F_{\rm per}^{\eta}t_{\rm bin}-n_{i}C)^{2}}{2\sigma_{i}^{2}}\right], (4)

where FperF_{\rm per} is measured in units of the overall averaged X-ray intensity of 2626 mCrab, tbint_{\rm bin} is the time width to interpolate the persistent flux in units of 1 day, σi\sigma_{i} is the observational error, and i(=1,2,,11)i\ (=1,2,\ldots,11) 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 tbin=t_{\rm bin}= 1/864 day (= 100 s). We employed flat prior distributions for η\eta and CC, setting broad acceptable ranges333The range of CC was set based on its maximum and minimum values obtained from two consecutive bursts (IDs 10–11), where assuming η\eta range of 0.5–1.5.: 0.5η1.50.5\leq\eta\leq 1.5 and 0.1C0.250.1\leq C\leq 0.25. Although nin_{i} is expected to be close to integer values, slight variations in CC from burst to burst shift the posterior distributions of nin_{i} away from integers. Therefore, for the prior of nin_{i}, 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 σ\sigma set so that 3σ=0.53\sigma=0.5. As an exceptional case, the center value was set to 1 for n10n_{10} because burst IDs 10 and 11 are consecutive, as described above. Additionally, we impose another constraint on the average burst recurrence times ΔtrecΔtpre/ni\Delta t_{\rm rec}\equiv\Delta t_{\rm pre}/n_{i}, setting Δtrec>2\Delta t_{\rm rec}>2 hr. This is based on the report that Δtrec\Delta t_{\rm rec} 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 2×1052\times 10^{5} steps. The walkers were uniformly initialized within the ranges of the flat prior distributions to comprehensively explore the (η,C,ni)(\eta,C,n_{i})-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 τ\tau is a reliable indicator for assessing the convergence of the MCMC chain. In emcee, running the chain for 50τ\tau samples generally ensures the convergence55footnotemark: 5. We estimated τ\tau using steps from the last half of the total and found τ1300\tau\sim 1300. Therefore, the initial 1.8×1051.8\times 10^{5} steps in each chain were discarded as burn-in to ensure full convergence.

Two-dimensional posterior distributions of η\eta, CC, and ni(i=1,5,10)n_{i}\ (i=1,5,10) 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 n101n_{10}\sim 1. This allows for multiple combinations of η\eta and CC that result in nin_{i} 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, η=0.840.01+0.02\eta=0.84^{+0.02}_{-0.01}. The ratios of the maximum likelihood estimates nin_{i} to their nearest integer values are well within ±\pm5%, except for n10n_{10}, which exhibits a residual of approximately 25%. The average burst recurrence times for each observed burst Δtrec\Delta t_{\rm rec} increased from 2.12.1 hr to 10.110.1 hr, as given in table 3.

Refer to caption
Figure 4: Two-dimensional posterior distributions of power-law index η\eta, CC, and ni(i=1,5,10)n_{i}\ (i=1,5,10) with marginalized histograms along the diagonal. The confidence contours displayed in each 2-D panel correspond to 1, 2, and 3σ\sigma levels. The vertical bands in the marginalized histograms along the diagonal are the inferred 68% highest posterior density intervals. Alt text: Ten contour plots and five histograms.
\tbl

Maximum likelihood estimates for each 1D marginalized posterior distribution with 68% highest posterior density intervals. η\eta CC n1n_{1} n2n_{2} n3n_{3} n4n_{4} n5n_{5} n6n_{6} n7n_{7} n8n_{8} n9n_{9} n10n_{10} n11n_{11} 0.840.01+0.020.84^{+0.02}_{-0.01} 0.1980.002+0.0020.198^{+0.002}_{-0.002} 6.160.06+0.076.16^{+0.07}_{-0.06} 9.700.08+0.109.70^{+0.10}_{-0.08} 8.150.05+0.068.15^{+0.06}_{-0.05} 9.050.06+0.069.05^{+0.06}_{-0.06} 12.840.07+0.0912.84^{+0.09}_{-0.07} 8.160.05+0.068.16^{+0.06}_{-0.05} 7.070.05+0.057.07^{+0.05}_{-0.05} 6.100.05+0.056.10^{+0.05}_{-0.05} 1.930.03+0.021.93^{+0.02}_{-0.03} 0.760.02+0.020.76^{+0.02}_{-0.02} 7.960.13+0.147.96^{+0.14}_{-0.13} {tabnote}

3.3 Recurrence time variation with burst duration

The compositions of the accreted matter, i.e., mass fractions of hydrogen (XX), helium (YY), and heavier CNO elements or metallicity (ZCNOZ_{\rm CNO}), are reflected in the burst properties such as the burst equivalent duration τ\tau and the recurrence time Δtrec\Delta t_{\rm rec} (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 τ\tau and Δtrec\Delta t_{\rm rec} are independent of the distance, making these parameters useful for comparing observations and theoretical predictions across different sources. Figure 5 shows the τ\tauΔtrec\Delta t_{\rm rec} relation of SRGA J1444, alongside the clocked burster GS 1826-24, which has a near-solar composition (e.g., Johnston et al. (2020)), and the ultra-compact binary 4U 1820-303, 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 Δtrec\Delta t_{\rm rec} of GS 1826-24 and 4U 1820-303 are taken from table 2 in Galloway et al. (2017), and τEb/Fpk\tau\equiv E_{b}/F_{\rm pk} are calculated using the burst fluence EbE_{b} and peak flux FpkF_{\rm pk} from the same table. We also show the theoretical relation from our model HERES with X/Y=1.5X/Y=1.5 and ZCNO=0.015Z_{\rm CNO}=0.015 (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 τ\tauΔtrec\Delta t_{\rm rec} relations, we fitted a linear model (τ=aΔtrec+b\tau=a\Delta t_{\rm rec}+b) to each data. The average burst duration of SRGA J1444 is less than half that of GS 1826-24 and three times that of 4U 1820-303. Furthermore, the negative slope between τ\tau and Δtrec\Delta t_{\rm rec} becomes shallower for sources with shorter τ\tau values.

Refer to caption
Figure 5: Relation between the burst equivalent duration τ\tau and the recurrence time Δtrec\Delta t_{\rm rec}, with the best-fit linear model shown as a red dotted line. Eight filled-circles are calculated from our model HERES in case of X/Y=1.5X/Y=1.5, ZCNO=0.015Z_{\rm CNO}=0.015, and the accretion rate of M˙=(0.85)×109Myr1\dot{M}=\left(0.8\text{--}5\right)\times 10^{-9}~{}M_{\odot}~{}{\rm yr}^{-1} (Dohi et al., 2024b), where MM_{\odot} denotes the solar mass. The observed relations for GS 1826-24 and 4U 1820-303 are also shown in blue and green, respectively, taken from Galloway et al. (2017). The best-fit parameters are as follows: SRGA J1444 (a=0.8±0.5shr1,b=20±2s,χ2/d.o.f=3.22/8)(a=-0.8\pm 0.5{\rm~{}s~{}hr^{-1}},\ b=20\pm 2~{}{\rm s},\ \chi^{2}/{\rm d.o.f}=3.22/8), GS 1826-24 (a=3.5±1.1shr1,b=54±5s,χ2/d.o.f=0.13/1)(a=-3.5\pm 1.1{\rm~{}s~{}hr^{-1}},\ b=54\pm 5~{}{\rm s},\ \chi^{2}/{\rm d.o.f}=0.13/1) , and 4U 1820-303 (a=0.4shr1,b=7.3s)(a=-0.4{\rm~{}s~{}hr^{-1}},\ b=7.3~{}{\rm s}). Alt text: Graph showing the scatter plots with best-fit linear models.

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 τ=18.1±1.2\tau=18.1\pm 1.2 s (IDs 1–11), the latter of which is consistent with the values derived by the spectral analysis from other satellites, such as IXPE (τ=16.8±1.6\tau=16.8\pm 1.6 s, Papitto et al. (2024)) and SRG (τ16\tau\sim 16 s, Molkov et al. (2024)), with an accuracy of \sim10%. 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-α\alpha reaction, which proceeds on a much shorter time scale than the H burning via the hot-CNO cycle, rprp, and αp\alpha p 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 X/Y=1.5X/Y=1.5 and ZCNO=0.015Z_{\rm CNO}=0.015 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 η=0.840.01+0.02\eta=0.84^{+0.02}_{-0.01}. We also noted that nin_{i} are closely matched to integer values within ±\pm5%, except for n10n_{10}. The deviations from integers could be attributed to the average variation in CC 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 η0.8\eta\sim 0.8 with recurrence time deviations ranging from a few % to roughly 10%555The variation of the recurrence time is reflected in the deviation of nin_{i} from their nearest integer values in our formulation expressed in equation (3). (see also Fu et al. 2024).

The estimated index η=0.840.01+0.02\eta=0.84^{+0.02}_{-0.01} of SRGA J1444 is the lowest value observed among X-ray bursters (section 1). Dohi et al. (2024a) investigated the η\eta dependence on the equation of state (EOS) and NS masses in the range of 1.1M1.1M_{\odot} to 2.0M2.0M_{\odot}. They found that compacted NS models tend to have lower values of η\eta.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 η\eta and the composition of the accreted fuel was found (Lampe et al., 2016), their conclusion holds. The value of η=0.840.01+0.02\eta=0.84^{+0.02}_{-0.01} for SRGA J1444 does not match the model predictions for NS masses below 2.0M2.0M_{\odot}, suggesting that it is a more massive NS. A comparison of observed value η\eta in SRGA J1444 with theoretical models that encompass a broader range of masses above 2.0M2.0M_{\odot} 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 Δtrec\Delta t_{\rm rec}FperF_{\rm per} relation with η0.8\eta\sim 0.8 remained valid up to at least 7.97.9 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 tCNO=9.8hr(X/0.7)(ZCNO/0.02)1t_{\rm CNO}=9.8\ {\rm hr}\ (X/0.7)(Z_{\rm CNO}/0.02)^{-1} (Lampe et al. (2016)). Using the maximum recurrence time of Δtrec=7.9\Delta t_{\rm rec}=7.9 hr observed in SRGA J1444, we obtain a constraint 7.9 hr (1+z)tCNO\leq(1+z)t_{\rm CNO}, where 1+z1+z represents the gravitational redshift at the photosphere. Assuming a solar hydrogen abundance of X=0.74X=0.74, along with a canonical NS with a mass MNS=1.4MM_{\rm NS}=1.4M_{\odot} and the radius RNS=11.2R_{\rm NS}=11.2 km (giving 1+z=1.2591+z=1.259), we derive an upper limit for the CNO mass fraction as ZCNO0.033Z_{\rm CNO}\leq 0.033. Similarly, for a massive NS with MNS=2.0MM_{\rm NS}=2.0M_{\odot} and the radius RNS=11.2R_{\rm NS}=11.2 km, the upper limit becomes ZCNO0.038Z_{\rm CNO}\leq 0.038. Note that for the He-enhanced scenario with X/Y=1.5X/Y=1.5 and Z=0.015Z=0.015, as inferred by Dohi et al. (2024b), tCNOt_{\rm CNO} is estimated to be 11.0 hr, satisfying the constraint 7.9hr<(1+z)tCNO7.9~{}\text{hr}<(1+z)t_{\rm CNO} 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.

{ack}

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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