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Supermassive Black Hole Precursor Detected in Archival Hubble Data

An international team of astronomers using archival data from the NASA/ESA Hubble Space Telescope and other space- and ground-based observatories have discovered a unique object in the distant, early Universe that is a crucial link between star-forming galaxies and the emergence of the earliest supermassive black holes. This object is the first of its kind to be discovered so early in the Universe’s history, and had been lurking unnoticed in one of the best-studied areas of the night sky.

Astronomers have struggled to understand the emergence of supermassive black holes in the early Universe ever since these objects were discovered at distances corresponding to a time only 750 million years after the Big Bang [1]. Rapidly growing black holes in dusty, early star-forming galaxies are predicted by theories and computer simulations but until now they had not been observed. Now, however, astronomers have reported the discovery of an object — which they name GNz7q — that is believed to be the first such rapidly growing black hole to be found in the early Universe. Archival Hubble data from the Advanced Camera for Surveys helped the team study the compact ultraviolet emission from the black hole’s accretion disc and to determine that GNz7q existed just 750 million years after the Big Bang.

Our analysis suggests that GNz7q is the first example of a rapidly-growing black hole in the dusty core of a starburst galaxy at an epoch close to the earliest super massive black hole known in the Universe,” explains Seiji Fujimoto, an astronomer at the Niels Bohr Institute of the University of Copenhagen in Denmark and lead author of the paper describing this discovery. “The object’s properties across the electromagnetic spectrum are in excellent agreement with predictions from theoretical simulations.”

Supermassive Black Hole Precursor
Supermassive Black Hole Precursor Detected in Archival Hubble Data: Crop of the GNz7q in the Hubble GOODS-North field. An international team of astronomers using archival data from the NASA/ESA Hubble Space Telescope and other space- and ground-based observatories have discovered a unique object in the distant, early Universe that is a crucial link between young star-forming galaxies and the earliest supermassive black holes. This object is the first of its kind to be discovered so early in the Universe’s history, and had been lurking unnoticed in one of the best-studied areas of the night sky.  The object, which is referred to as GNz7q, is shown here in the centre of the image of the Hubble GOODS-North field. Credit: NASAESA, G. Illingworth (University of California, Santa Cruz), P. Oesch (University of California, Santa Cruz; Yale University), R. Bouwens and I. Labbé (Leiden University), and the Science Team, S. Fujimoto et al. (Cosmic Dawn Center [DAWN] and University of Copenhagen)

Current theories predict that supermassive black holes begin their lives in the dust-shrouded cores of vigorously star-forming “starburst” galaxies before expelling the surrounding gas and dust and emerging as extremely luminous quasars. Whilst they are extremely rare, examples of both dusty starburst galaxies and luminous quasars have been detected in the early Universe. The team believes that GNz7q could be the “missing link” between these two classes of objects.

GNz7q provides a direct connection between these two rare populations and provides a new avenue towards understanding the rapid growth of supermassive black holes in the early days of the Universe,” continued Fujimoto. “Our discovery is a precursor of the supermassive black holes we observe at later epochs.

Whilst other interpretations of the team’s data cannot be completely ruled out, the observed properties of GNz7q are in strong agreement with theoretical predictions. GNz7q’s host galaxy is forming stars at the rate of 1600 solar masses of stars per year [2] and GNz7q itself appears bright at ultraviolet wavelengths but very faint at X-ray wavelengths. The team have interpreted this — along with the host galaxy’s brightness at infrared wavelengths — to suggest that GNz7q is harbors a rapidly growing black hole still obscured by the dusty core of its accretion disc at the center of the star-forming host galaxy.

Supermassive Black Hole Precursor Detected in Archival Hubble Data: GNz7q in the Hubble GOODS-North field. An international team of astronomers using archival data from the NASA/ESA Hubble Space Telescope and other space- and ground-based observatories have discovered a unique object in the distant, early Universe that is a crucial link between young star-forming galaxies and the earliest supermassive black holes. This object is the first of its kind to be discovered so early in the Universe’s history, and had been lurking unnoticed in one of the best-studied areas of the night sky.  The object, which is referred to as GNz7q, is shown here in the centre of the cutout from the Hubble GOODS-North field. Credit: NASAESA, G. Illingworth (University of California, Santa Cruz), P. Oesch (University of California, Santa Cruz; Yale University), R. Bouwens and I. Labbé (Leiden University), and the Science Team, S. Fujimoto et al. (Cosmic Dawn Center [DAWN] and University of Copenhagen)

As well as GNz7q’s importance to the understanding of the origins of supermassive black holes, this discovery is noteworthy for its location in the Hubble GOODS North field, one of the most highly scrutinised areas of the night sky [3].

GNz7q is a unique discovery that was found just at the centre of a famous, well-studied sky field — showing that big discoveries can often be hidden just in front of you,” commented Gabriel Brammer, another astronomer from the Niels Bohr Institute of the University of Copenhagen and a member of the team behind this result. “It’s unlikely that discovering GNz7q within the relatively small GOODS-N survey area was just ‘dumb luck’ rather the prevalence of such sources may in fact be significantly higher than previously thought.

Finding GNz7q hiding in plain sight was only possible thanks to the uniquely detailed, multi-wavelength datasets available for GOODS-North. Without this richness of data GNz7q would have been easy to overlook, as it lacks the distinguishing features usually used to identify quasars in the early Universe. The team now hopes to systematically search for similar objects using dedicated high-resolution surveys and to take advantage of the NASA/ESA/CSA James Webb Space Telescope’s spectroscopic instruments to study objects such as GNz7q in unprecedented detail.

Fully characterising these objects and probing their evolution and underlying physics in much greater detail will become possible with the James Webb Space Telescope.” concluded Fujimoto. “Once in regular operation, Webb will have the power to decisively determine how common these rapidly growing black holes truly are.”

Supermassive Black Hole Precursor
Supermassive Black Hole Precursor Detected in Archival Hubble Data: Artist’s Impression of GNz7q. An international team of astronomers using archival data from the NASA/ESA Hubble Space Telescope and other space- and ground-based observatories have discovered a unique object in the distant, early Universe that is a crucial link between young star-forming galaxies and the earliest supermassive black holes. This object is the first of its kind to be discovered so early in the Universe’s history, and had been lurking unnoticed in one of the best-studied areas of the night sky. Current theories predict that supermassive black holes begin their lives in the dust-shrouded cores of vigorously star-forming “starburst” galaxies before expelling the surrounding gas and dust and emerging as extremely luminous quasars. Whilst they are extremely rare, examples of both dusty starburst galaxies and luminous quasars have been detected in the early Universe. The team believes that GNz7q could be the “missing link” between these two classes of objects. Credit: ESA/Hubble, N. Bartmann

Notes

[1] Whilst light travels imperceptibly quickly in day-to-day life, the vast distances in astronomy mean that as astronomers look at increasingly distant objects, they are also looking backwards in time. For example, light from the Sun takes around 8.3 minutes to reach Earth, meaning that we view the Sun as it was 8.3 minutes ago. The most distant objects are the furthest back in time, meaning that astronomers studying very distant galaxies are able to study the earliest periods of the Universe.

[2] This does not mean that 1600 Sun-like stars are produced each year in GNz7q’s host galaxy, but rather that a variety of stars are formed each year with a total mass 1600 times that of the Sun.

[3] GOODS — the Great Observatories Origins Deep Survey — is an astronomical survey that combines multi-wavelength observations from some of the most capable telescopes ever built, including Hubble, ESA’s Herschel and XMM-Newton space telescopes, NASA’s Spitzer Space Telescope and Chandra X-ray Observatory, and powerful ground-based telescopes.

Supermassive Black Hole Precursor: more information

The Hubble Space Telescope is a project of international cooperation between ESA and NASA.

These results have been published in Nature.

The international team of astronomers in this study consists of S. Fujimoto (Cosmic Dawn Center [DAWN] and Niels Bohr Institute, University of Copenhagen, Denmark), G. B. Brammer (DAWN and Niels Bohr Institute, University of Copenhagen, Denmark), D. Watson (DAWN and Niels Bohr Institute, University of Copenhagen, Denmark), G. E. Magdis (DAWN, DTU-Space at the Technical University of Denmark, and Niels Bohr Institute at the University of Copenhagen, Denmark), V. Kokorev (DAWN and Niels Bohr Institute, University of Copenhagen, Denmark), T. R. Greve (DAWN and DTU-Space, Technical University of Denmark, Denmark), S. Toft (DAWN and Niels Bohr Institute, University of Copenhagen, Denmark),  F. Walter ( DAWN, Denmark, the Max Planck Institute for Astronomy, Germany, and the National Radio Astronomy Observatory, USA), R. Valiante (INAF-Osservatorio Astronomico di Roma, Rome, Italy), M. Ginolfi (European Southern Observatory, Garching, Germany), R. Schneider (INAF-Osservatorio Astronomico di Roma, Rome, Italy and Dipartimento di Fisica, Universitá di Roma La Sapienza, Rome, Italy), F. Valentino (DAWN and Niels Bohr Institute, University of Copenhagen, Denmark), L. Colina (DAWN, Copenhagen, Denmark and Centro de Astrobiología (CAB, CSIC-INTA), Madrid, Spain), M. Vestergaard (Niels Bohr Institute, University of Copenhagen, Denmark, and Steward Observatory, University of Arizona, USA), R. Marques-Chaves (Geneva Observatory, University of Geneva, Switzerland), J. P. U. Fynbo (DAWN and Niels Bohr Institute, University of Copenhagen, Denmark), M. Krips (IRAM, Domaine Universitaire, Saint-Martin-d’Hères, France), C. L. Steinhardt (DAWN and Niels Bohr Institute, University of Copenhagen, Denmark), I. Cortzen (IRAM, Domaine Universitaire, Saint-Martin-d’Hères, France), F. Rizzo (DAWN and Niels Bohr Institute, University of Copenhagen, Denmark), and P. A. Oesch (DAWN, Copenhagen, Denmark and Geneva Observatory, University of Geneva, Switzerland).

 

Press release from ESA/Hubble Information Centre

Predicted versus observed epidemic curves over time. (copyright: Nature) Our model aggregates population outflow from Wuhan from January 1 to 24, 2020 to provide a reference growth pattern (i.e. epidemic curves) for COVID-19’s spread. Differences in the predicted and confirmed growth in confirmed cases can signal higher levels of COVID-19 community transmission.

An international research team led by the University of Hong Kong (HKU) developed a new method to accurately track the spread of COVID-19 using population flow data, and establishing a new risk assessment model to identify high-risk locales of COVID-19 at an early stage, which serves as a valuable toolkit to public health experts and policy makers in implementing infectious disease control during new outbreaks.  The study findings have been published in the journal Nature today (April 29).

Dr. Jayson Jia, Associate Professor of Marketing at the Faculty of Business and Economics of HKU and lead author of the study, and his co-authors used nation-wide data provided by a major national carrier in China to track population movement out of Wuhan between 1 January and 24 January 2020, a period covering the annual Chunyun mass migration before the Chinese Lunar New Year to a lockdown of the city to contain the virus. The movement of over 11 million people travelling through Wuhan to 296 prefectures in 31 provinces and regions in China were tracked.

Differing from usual epidemiological models that rely on historical data or assumptions, the team used real-time data about actual movements focusing on aggregate population flow rather than individual tracking. The data include any mobile phone user who had spent at least 2 hours in Wuhan during the study period.  Locations were detected once users had their phones on. As only aggregate data was used and no individual data was used, there was no threat to consumer privacy.

Combining the population flow data with the number and location of COVID-19 confirmed cases up to 19 February 2020 in China, Dr Jia’s team showed that the relative quantity of human movement from the disease epicentre, in this case, Wuhan, directly predicted the relative frequency and geographic distribution of the number of COVID-19 cases across China. The researchers found that their model can explain 96% of the distribution and intensity of the spread of COVID-19 across China statistically.

COVID-19 big data
Illustrative example of using model to track COVID-19 community spread risk. (copyright: Nature) Our model uses population movement to predict expected cases. The predicted spread of the SARS-CoV-2 virus can be used as a benchmark to identify which locales are ‘outliers’, which have significantly more or less cases than expected (given the movement data). The graph is an illustration of what our model showed on January 29. Prefectures to the left of the dashed line are outliers that have significantly more than expected cases, i.e., a higher level of unexplained or community transmission. Our model identified Wenzhou as having the most severe community transmission risk on January 29, 2020. The government announced a full quarantine of the prefecture on February 2, 2020.

The research team then used this empirical relationship to build a new risk detection toolkit. Leveraging on the population flow data, the researchers created an “expected growth pattern” based on the number of people arriving from the risk source, i.e. the disease epicentre. The team thereby developed a new risk model by contrasting expected growth of cases against the actual number of confirmed cases for each city in China, the difference being the “community transmission risk”.

“If there are more reported cases than the model expected, there is a higher risk of community spread. If there are fewer reported cases than the model expected, it means that the city’s preventive measures are particularly effective or it can indicate that further investigation by central authorities is needed to eliminate possible risks from inaccurate measurement,” explained Dr Jia.

“What is innovative about our approach is that we use misprediction to assess the level of community risk.  Our model accurately tells us how many cases we should expect given travel data.  We contrast this against the confirmed cases using the logic that what cannot be explained by imported cases and primary transmissions should be community spread. ” He added.

The approach is advantageous because it requires no assumptions or knowledge of how or why the virus spreads, is robust to data reporting inaccuracies, and only requires knowledge of relative distribution of human movement. It can be used by policy makers in any nation with available data to make rapid and accurate risk assessments and to plan allocation of limited resources ahead of ongoing disease outbreaks.

“Our research indicates that geographic flow of people outperforms other measures such as population size, wealth or distance from the risk source to indicate the gravity of an outbreak.” said Dr Jia.

Dr Jia is currently exploring with fellow researchers the feasibility of applying this toolkit to other countries, and extending it to situations where there are multiple COVID-19 epicentres. The team is working with other national telecom carriers and seeking additional data partners.

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Publication 

The study’s co-authors are Jianmin Jia, Presidential Chair Professor at the Chinese University of Hong Kong, Shenzhen (corresponding author); Nicholas A. Christakis, Sterling Professor of Social and Natural Science at Yale; Xin Lu, the National University of Defense Technology in Changsha, China, and the Karolinska Institutet in Stockholm, Sweden; Yun Yuan, Southwest Jiaotong University; Ge Xu, Hunan University of Technology and Business.

Press release from The University of Hong Kong.