XMMSL1J063045.9-603110: a tidal disruption event fallen into the back burner

Mainetti, D.; Campana, S.; Colpi, M.

doi:10.1051/0004-6361/201628737

Cited by 10 publications

(9 citation statements)

References 67 publications

(84 reference statements)

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“…Komossa & Bade, 1999;Komossa & Greiner, 1999) and the only remaining credible galactic TDE impostor is a nova (e.g. Mainetti et al, 2016). The rate of galactic novae is estimated to be 50 per year (Shafter, 2017), and as the space density of galaxies is 0.0177 Mpc −3 (Driver et al, 2005) then the probability of one of these novae lying within 1.5" of the nucleus of a galaxy out to a distance of 127 Mpc is 3 × 10 −5 per year.…”

Section: Discussionmentioning

confidence: 99%

XMMSL2 J144605.0+685735: a slow tidal disruption event

Saxton

Read

Komossa

et al. 2019

A&A

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Aims. We investigate the evolution of X-ray selected tidal disruption events. Methods. New events are found in near real-time data from XMM-Newton slews, and are monitored by multi-wavelength facilities. Results. In August 2016, X-ray emission was detected from the galaxy XMMSL2 J144605.0+685735 (also known as 2MASX 14460522+6857311), that was 20 times higher than an upper limit from 25 years earlier. The X-ray flux was flat for ∼ 100 days and then fell by a factor of 100 over the following 500 days. The UV flux was stable for the first 400 days before fading by a magnitude, while the optical (U,B,V) bands were roughly constant for 850 days. Optically, the galaxy appears to be quiescent, at a distance of 127 ± 4 Mpc (z=0.029 ± 0.001) with a spectrum consisting of a young stellar population of 1-5 Gyr in age, an older population, and a total stellar mass of ∼ 6 × 10 9 M ⊙ . The bolometric luminosity peaked at L bol ∼ 10 43 ergs s −1 with an X-ray spectrum that may be modelled by a power law of Γ ∼ 2.6 or Comptonisation of a low-temperature thermal component by thermal electrons. We consider a tidal disruption event to be the most likely cause of the flare. Radio emission was absent in this event down to < 10µJy, which limits the total energy of a hypothetical off-axis jet to E < 5 × 10 50 ergs. The independent behaviour of the optical, UV, and X-ray light curves challenges models where the UV emission is produced by reprocessing of thermal nuclear emission or by streamstream collisions. We suggest that the observed UV emission may have been produced from a truncated accretion disc and the X-rays from Compton upscattering of these disc photons.

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Section: Discussionmentioning

confidence: 99%

XMMSL2 J144605.0+685735: a slow tidal disruption event

Saxton

Read

Komossa

et al. 2019

A&A

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“…NGC 3599 and SDSS J1323+48 were found in archival data (Esquej et al 2008) while SDSS J1201+30 (Saxton et al 2012), 2MASX J0619-65 (Saxton et al 2014) and XMMSL1 J0740-85 (Saxton et al 2017) were found at the peak of their emission and had their decays monitored with Swift and XMM-Newton. XMMSL1 J063045.9-603110, a bright slew transient from 2011, has been proposed as a TDE in a dwarf galaxy (Mainetti et al 2016). However, a contemporary optical spectrum localised the flare to the local group (B. Cenko, p.comm.)…”

Section: Xmm-newton Slew Surveymentioning

confidence: 99%

Tidal disruption events seen in the XMM-Newton slew survey

et al. 2016

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XMM-Newton performs a survey of the sky in the 0.2-12 keV X-ray band while slewing between observation targets. The sensitivity in the soft X-ray band is comparable with that of the ROSAT all-sky survey, allowing bright transients to be identified in near real-time by a comparison of the flux in both surveys. Several of the soft X-ray flares are coincident with galaxy nuclei and five of these have been interpreted as candidate tidal disruption events (TDE). The first three discovered had a soft X-ray spectrum, consistent with the classical model of TDE, where radiation is released during the accretion phase by thermal processes. The remaining two have an additional hard, power-law component, which in only one case was accompanied by radio emission. Overall the flares decay with the classical index of t−5/3 but vary greatly in the early phase.

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“…This behavior is confirmed by both MOS cameras. The source is associated with XMMSL1 J063045.9-603110, a peculiar transient source (Read et al 2011) proposed to be a tidal disruption event (Mainetti et al 2016), but later spectroscopically classified as a nova (Oliveira et al 2017). 3XMM J182422.8-301833 (obs.id.…”

Section: Interesting Sourcesmentioning

confidence: 99%

Exploring X-ray variability with unsupervised machine learning

Pasquato

Marelli

Luca

et al. 2022

A&A

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Context. XMM-Newton provides unprecedented insight into the X-ray Universe, recording variability information for hundreds of thousands of sources. Manually searching for interesting patterns in light curves is impractical, requiring an automated data-mining approach for the characterization of sources. Aims. Straightforward fitting of temporal models to light curves is not a sure way to identify them, especially with noisy data. We used unsupervised machine learning to distill a large data set of light-curve parameters, revealing its clustering structure in preparation for anomaly detection and subsequent searches for specific source behaviors (e.g., flares, eclipses). Methods. Self-organizing maps (SOMs) achieve dimensionality reduction and clustering within a single framework. They are a type of artificial neural network trained to approximate the data with a two-dimensional grid of discrete interconnected units, which can later be visualized on the plane. We trained our SOM on temporal-only parameters computed from 10 5 detections from the EXTraS (Exploring the X-ray Transient and variable Sky) catalog. Results. The resulting map reveals that the ≈ 2500 most variable sources are clustered based on temporal characteristics. We find distinctive regions of the SOM map associated with flares, eclipses, dips, linear light curves, and others. Each group contains sources that appear similar by eye. We single out a handful of interesting sources for further study. Conclusions. The condensed view of our dataset provided by SOMs allowed us to identify groups of similar sources, speeding up manual characterization by orders of magnitude. Our method also highlights problems with fitting simple temporal models to light curves and can be used to mitigate them to an extent. This will be crucial for fully exploiting the high data volume expected from upcoming X-ray surveys, and may also help with interpreting supervised classification models.

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XMMSL1J063045.9-603110: a tidal disruption event fallen into the back burner

Cited by 10 publications

References 67 publications

XMMSL2 J144605.0+685735: a slow tidal disruption event

XMMSL2 J144605.0+685735: a slow tidal disruption event

Tidal disruption events seen in the XMM-Newton slew survey

Exploring X-ray variability with unsupervised machine learning

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