Ask the machine: systematic detection of wind-type outflows in low-mass X-ray binaries

The systematic discovery of outflows in the optical spectra of low-mass X-ray binaries opened a new avenue for the study of the outburst evolution in these extreme systems. However, the efficient detection of such features in a continuously growing data base requires the development of new analysis techniques with a particular focus on scalability, adaptability, and automatization. In this pilot study, we explore the use of machine learning algorithms to perform the identification of outflows in spectral line profiles observed in the optical range. We train and test the classifier on a simulated data base constructed through a combination of disc emission line profiles and outflow signatures, emulating typical observations of low-mass X-ray binaries. The final, trained classifier is applied to two sets of spectra taken during two bright outbursts that were particularly well covered, those of V404 Cyg (2015) and MAXI J1820+070 (2018). The resulting classification gained by this novel approach is overall consistent with that obtained through traditional techniques, while simultaneously providing a number of key advantages over the latter, including the access to low-velocity outflows. This study sets the foundations for future studies on large samples of spectra from low-mass X-ray binaries and other compact binaries.

[1]  C. Knigge,et al.  The origin of optical emission lines in the soft state of X-ray binary outbursts: The case of MAXI J1820+070 , 2023, Monthly Notices of the Royal Astronomical Society.

[2]  Stefan M. Wild,et al.  DeepAstroUDA: semi-supervised universal domain adaptation for cross-survey galaxy morphology classification and anomaly detection , 2023, Mach. Learn. Sci. Technol..

[3]  M. Huertas-Company,et al.  The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys , 2022, Publications of the Astronomical Society of Australia.

[4]  David Sánchez,et al.  A correlation between H α trough depth and inclination in quiescent X-ray transients: evidence for a low-mass black hole in GRO J0422+32 , 2022, Monthly Notices of the Royal Astronomical Society.

[5]  D. Steeghs,et al.  Discovery of optical and infrared accretion disc wind signatures in the black hole candidate MAXI J1348-630 , 2022, Astronomy & Astrophysics.

[6]  Z. D. Beurs,et al.  A Comparative Study of Machine-learning Methods for X-Ray Binary Classification , 2022, The Astrophysical Journal.

[7]  J. Casares,et al.  Hard-state Optical Wind during the Discovery Outburst of the Black Hole X-Ray Dipper MAXI J1803–298 , 2022, The Astrophysical Journal Letters.

[8]  D. Huppenkothen,et al.  Light curve fingerprints: an automated approach to the extraction of X-ray variability patterns with feature aggregation -- an example application to GRS 1915+105 , 2021, 2110.10063.

[9]  The University of Manchester,et al.  Dynamical confirmation of a stellar mass black hole in the transient X-ray dipping binary MAXI J1305-704 , 2021, 2104.07042.

[10]  F. Jiménez-Ibarra,et al.  Optical nebular emission following the most luminous outburst of Aquila X-1 , 2021, Astronomy & Astrophysics.

[11]  B. Nord,et al.  DeepMerge II: Building Robust Deep Learning Algorithms for Merging Galaxy Identification Across Domains , 2021, Monthly Notices of the Royal Astronomical Society.

[12]  R. Kotak,et al.  Transient-optimized real-bogus classification with Bayesian convolutional neural networks – sifting the GOTO candidate stream , 2021, Monthly Notices of the Royal Astronomical Society.

[13]  Aniruddha Kembhavi,et al.  A Machine Learning Approach For Classifying Low-mass X-ray Binaries Based On Their Compact Object Nature , 2020, Monthly Notices of the Royal Astronomical Society.

[14]  G. Ponti,et al.  Discovery of optical outflows and inflows in the black hole candidate GRS 1716−249 , 2020, 2007.13775.

[15]  T. Muñoz-Darias,et al.  Near-infrared emission lines trace the state-independent accretion disc wind of the black hole transient MAXI J1820+070 , 2020, Astronomy & Astrophysics.

[16]  P. Jonker,et al.  The Binary Mass Ratio in the Black Hole Transient MAXI J1820+070 , 2020, The Astrophysical Journal.

[17]  G. Nelemans,et al.  Potential kick velocity distribution of black hole X-ray binaries and implications for natal kicks , 2019, Monthly Notices of the Royal Astronomical Society.

[18]  F. Jiménez-Ibarra,et al.  An equatorial outflow in the black hole optical dipper Swift J1357.2−0933 , 2019, Monthly Notices of the Royal Astronomical Society.

[19]  E. Kotze,et al.  Hot, dense HeII outflows during the 2017 outburst of the X-ray transient Swift J1357.2-0933 , 2019, Monthly Notices of the Royal Astronomical Society: Letters.

[20]  P. Jonker,et al.  Dynamical Confirmation of a Black Hole in MAXI J1820+070 , 2019, The Astrophysical Journal.

[21]  R. Fender,et al.  Accretion and outflow in V404 Cyg , 2019, Monthly Notices of the Royal Astronomical Society.

[22]  D. Steeghs,et al.  Hard-state Accretion Disk Winds from Black Holes: The Revealing Case of MAXI J1820+070 , 2019, The Astrophysical Journal.

[23]  Germain Forestier,et al.  Deep learning for time series classification: a review , 2018, Data Mining and Knowledge Discovery.

[24]  The University of Manchester,et al.  The 1989 and 2015 outbursts of V404 Cygni: a global study of wind-related optical features , 2018, Monthly Notices of the Royal Astronomical Society.

[25]  B. J. Shappee,et al.  ASASSN-18ey: The Rise of a New Black Hole X-Ray Binary , 2018, The Astrophysical Journal.

[26]  M. Giustini,et al.  The black hole binary V404 Cygni: a highly accreting obscured AGN analogue , 2016, 1607.02255.

[27]  D. Malesani,et al.  SN 2015bh: NGC 2770's 4th supernova or a luminous blue variable on its way to a Wolf-Rayet star? , 2016, 1606.09025.

[28]  K. Mooley,et al.  Regulation of black-hole accretion by a disk wind during a violent outburst of V404 Cygni , 2016, Nature.

[29]  Martín Abadi,et al.  TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , 2016, ArXiv.

[30]  Yuki Sugiura,et al.  Repetitive patterns in rapid optical variations in the nearby black-hole binary V404 Cygni , 2016, Nature.

[31]  J. Gladstone,et al.  WATCHDOG: A COMPREHENSIVE ALL-SKY DATABASE OF GALACTIC BLACK HOLE X-RAY BINARIES , 2015, 1512.00778.

[32]  F. Bauer,et al.  BlackCAT: A catalogue of stellar-mass black holes in X-ray transients , 2015, 1510.08869.

[33]  A. Merloni,et al.  High ionisation absorption in low mass X-ray binaries , 2015, 1510.08902.

[34]  L. Boirin,et al.  Accretion disc atmospheres and winds in low‐mass X‐ray binaries , 2015, 1510.03576.

[35]  T. Shahbaz,et al.  Swift J1357.2−0933: a massive black hole in the Galactic thick disc , 2015, 1509.05412.

[36]  R. Fender,et al.  The balance of power: accretion and feedback in stellar mass black holes , 2015, 1505.03526.

[37]  D. Steeghs,et al.  VLT spectroscopy of the black hole candidate Swift J1357.2−0933 in quiescence , 2015, 1503.08874.

[38]  R. Kotak,et al.  Machine learning for transient discovery in Pan-STARRS1 difference imaging , 2015, 1501.05470.

[39]  F. Rahoui,et al.  Optical and near-infrared spectroscopy of the black hole GX 339−4 – II. The spectroscopic content in the low/hard and high/soft states , 2014, 1405.3457.

[40]  J. Prieto,et al.  SN 2009ip and SN 2010mc: core-collapse Type IIn supernovae arising from blue supergiants , 2013, 1308.0112.

[41]  T. Shahbaz,et al.  A Black Hole Nova Obscured by an Inner Disk Torus , 2013, Science.

[42]  M. Wainwright,et al.  Using machine learning for discovery in synoptic survey imaging data , 2012, 1209.3775.

[43]  G. Ponti,et al.  Ubiquitous equatorial accretion disc winds in black hole soft states , 2012, 1201.4172.

[44]  J. Neilsen,et al.  Accretion disk winds as the jet suppression mechanism in the microquasar GRS 1915+105 , 2009, Nature.

[45]  P. Hartigan,et al.  Infrared [Fe II] Emission from P Cygni’s Nebula: Atomic Data, Mass, Kinematics, and the 1600 AD Outburst , 2005, astro-ph/0510836.

[46]  T. Belloni,et al.  A Unified Model for Black Hole X-Ray Binary Jets? , 2004, astro-ph/0506469.

[47]  H. Esenoglu,et al.  Spectral evolution of Nova (V1494) Aql and its high velocity jets , 2003 .

[48]  D. Steeghs,et al.  The Mass Donor of Scorpius X-1 Revealed , 2001, astro-ph/0107343.

[49]  R. Hunstead,et al.  Optical Spectroscopy of GRO J1655–40 , 1999, astro-ph/9911318.

[50]  C. Shrader,et al.  The 1993 August Minioutburst of GRO J0422+32 , 1997 .

[51]  J. Orosz,et al.  Orbital Parameters of the Candidate Black Hole Binary GRO J0422+32 , 1995 .

[52]  T. Marsh,et al.  Doppler tomography of the X-ray transient J0422+32 during the 1993 December mini-outburst , 1995 .

[53]  Michael R. Garcia,et al.  Observations of the X-ray Nova GRO J0422+32. 1: Outburst and the decay to quiescence , 1995 .

[54]  Charles D. Bailyn,et al.  Quiescent accretion disks in black hole X-ray novae , 1994 .

[55]  R. Narayan,et al.  Advection dominated accretion: Underfed black holes and neutron stars , 1994, astro-ph/9411059.

[56]  J. Casares,et al.  A 6.5-day periodicity in the recurrent nova V404 Cygni implying the presence of a black hole , 1992, Nature.

[57]  M. Valle,et al.  Evidence for a black hole in the X-ray nova Muscae 1991 , 1991, Nature.

[58]  K. Horne,et al.  Emission line formation in accretion discs , 1986 .

[59]  C. Canizares,et al.  On the origin of 4640-4650 A emission in X-ray stars , 1975 .

[60]  Luc De Raedt,et al.  Proceedings of the 22nd international conference on Machine learning , 2005 .

[61]  submitted to The Astrophysical Journal Preprint typeset using L ATEX style emulateapj v. 04/03/99 OPTICAL SPECTROSCOPY OF THE X-RAY TRANSIENT XTE J1118+480 IN OUTBURST 1 , 2000 .

[62]  E.P.J. van den Heuvel,et al.  UvA-DARE ( Digital Academic Repository ) A catalogue of low-mass X-ray binaries , 2022 .