Islamic banking principles are committed to establishing a welfare-oriented banking
system that meets the needs of low income and underprivileged citizens.
It supports the establishment of an
economic system based on social emancipation and equitable distribution of
wealth, encouraging the economic progress of socially deprived people. It also
works to create employment opportunities and the development of agriculture and
creating employment opportunities and developing agriculture in rural
communities, the aim of traditional Islamic banks is to, support the
establishment of a unique economic system. This economic system is based on
social emancipation and an equitable distribution of wealth so that socially
deprived people experience economic progress. This is done through the
development and the creation of employment opportunities in agricultural and
Al-Arafah Islami Bank Limited (AAIBL) was established in 1995 and is
headquartered in Dhaka in Bangladesh. This bank operates on the traditional
Islamic principles and they face many key challenges. For the successful
operation of any bank, they have to ensure that the vital pillars of IT
security and confidentiality are ceaselessly infallible.
has an enormous responsibility to ensure that its IT security and
confidentiality is being upheld, due to the fact that it has a customer base
that is expansive and wide-ranging. It has customers from 199 branches
throughout Bangladesh, ranging from busy city environments to remote rural
The Challenges that has Compromised the
Reputation of the Bank
has, for many years faced many pertinent challenges that are both internal and
external. The external challenges include having to operate in an environment
with weak national infrastructure, making them prone to cyberattacks that could
compromise the IT security and confidentiality of the organisation. The
internal challenges include having lack of a central management or policies
that govern the individual use of PCs and other devices.
regrettably caused many virus, worm and Trojan attacks on both the local head
office network and across the branch network. This resulted in the
inconvenient, time-consuming and expensive task of having to transport the
affected PCs from remote, rural branches back to Dhaka to be cleaned and
particular, the uncontrolled access to virus-laden web pages and the widespread
use of unauthorised USB devices resulted in numerous large scale and
debilitating infections. At their most severe, these problems could result in
the complete shutdown of branches, with core banking software ceasing to
operate. This could be extremely inimical as it could affect customer’s
transactions, doing irrevocable damage to the organisation’s reputation. These
issues were also preventing AAIBL’s efforts to establish online banking
Mr. D. M. Jahangir Rabbani, Senior
Principal Officer, ICT Division, said that whilst these events were now routine
to the organisation, it was vital that they find a solution to the problems
since it was causing severe disruptions to their consumers and creating a bad
reputation for the organisation.
from Kaspersky Lab that Brought AAIB Back to their Feet Again
The bank selected Kaspersky Lab and its
award-winning Dhaka-based partner, Officextracts, for the performance of its
software. The systems put in place by Kaspersky included virus detection and
management. Kaspersky Lab’s world class,
multi-layered anti-malware protection for AAIB’s systems, servers and more than
1,000 individual users was combined with firewall, application, device and web
control, mobile security, device and systems management capabilities.
The solution has provided a comprehensive
remedy for the bank’s longstanding and damaging virus problems. Network traffic
is now running smoothly and centralised controls on the use of devices such as
USBs, CDs and DVDs is preventing local infections out in the branch network.
Websites and applications presenting
dangers to the bank are being identified and blocked centrally by the IT team
from head office, managing threat and risk levels and significantly reducing
bandwidth consumption. It can manage the system, monitor endpoint security,
review threat and risk levels and generate reports for executives, all from the
Kaspersky Lab central console.
The results are significant for both the
bank and its customers. Core banking services are streamlined and operating
smoothly for customers resulting in efficiency of operations allowing staff to
focus on activities that can improve customer satisfaction. The IT team can now
focus on enhancing and innovating so that their colleagues and customers can
benefit from them.
Said Mr. Rabbani that as a bank their
operations must be secure and confidential at all times. While they have
resolved their security issues thanks to Kaspersky Lab, they have to continue
to be resilient and control central access to web pages, applications and
Taiwan City Science Lab @ Taipei Tech demonstrated a series of cutting-edge AI applications. The lab exhibit advanced AI applications and their research and development results, such as the mobile robot, a AI robotic fish and Campus Rover.
The cross-disciplinary R&D and teaching laboratory aims to be a global technology and talent exchange platform. Massachusetts Institute of Technology (MIT) and Taipei Tech are coming together to jointly established City Science Lab @ Taipei Tech.
“Through developing advanced AI technology and big data system, we plan to make Taiwan the island of high-end technology,” said Yao Leehter, Taipei Tech Chair Professor of the Department of Electrical Engineering.
Yao indicated that Taipei Tech alums highly support the lab. The lab also collaborates with Kent Larson, the leader of MIT City Science Lab, the City Science Lab @ Taipei Tech aims to be an international platform for technology and talent exchange.
Taipei Tech adopts and jointly promotes with MIT to implement the Undergraduate Scientific Research Programme. Known as UROP, the programme provides sufficient resources for students and cultivates a new generation of scientific researchers. The collaboration was initially rolled out in 1969 by MIT’s first President, William Rogers.
For students to learn the most modern and state-of-the-art technology applications, the lab provides advanced equipment for R&D purposes, such as mobile robots. The agile, mobile robot can adapt to complex terrains and is equipped with LIDAR, infrared, and stereo vision sensors, which can draw 3D point cloud maps in real-time and detect and dodge obstacles. The mobile robot is used in decommissioned nuclear power plants, factories, construction sites, and offshore drilling oil platforms. Another mobile robot use case is for patrol, troubleshooting, and leak detection.
In addition, the lab also showcased its R&D results which are the AI robotic fish to the advanced instrumental equipment. The robotic fish is a streamlined robot designed to resemble a real fish. The fish robot comprehends and mimics the motion model of swimming fish through machine learning.
The robot can swim underwater in a simulated way. To perfectly mimic the fish movement, researchers have spent significant time collecting massive movement data from real fish, documenting, and analysing the swimming performance. Afterwards, they utilised AI technology and programme coding to control the motoric movement of the robotic fish.
The team then spent a year adjusting the robotic fish to make the swim movement look like a real fish. Machinery fish propulsion efficiency and excellent swimming performance are considered one of the most critical subjects in bionics.
“The robotic fish is useful for biological research and can also be used to carry out underwater operations and examine water quality,” said Yao.
Recently, the fish robot was involved in movie production. During the designing process, the production house team suggested adding a “cloth” on the fish with fish skin and fish scale to make it more lifelike. The company also came up with the idea to use a magnet to stick the fish scale on the body of the robotic fish. Taiwan Textile Research Institute and the local design research group joined the brainstorming and production process to finish the golden fish’s final look onscreen.
Moreover, The Campus Rover, developed by the team of Professor Yao in cooperation with the Taipei Tech Department of Industrial Design, demonstrated practical AI applications in real life. For example, campus or express hospital service can use the self-charging robot to ensure delivery safety.
Dr Andrew Lensen from the School of Engineering and Computer Science and Dr Marcin Betkier from the Law School are eager to ensure AI has a significant role in the justice system. The researchers based in New Zealand built an Artificial Intelligence (AI) algorithm that predicts the length of court sentences.
But the question that may arise is whether the AI algorithm is fair enough to hand down the sentences. In the current justice system, society trusts judges to hand down fair sentences to the accused based on their knowledge and experience.
But how about AI? Can it judge better because it can eliminate the potential for bias and discrimination? And can AI substitute the judge’s knowledge and experience with its ability to analyse and predict large amounts of data?
Dr Andrew is optimistic that AI can help better sentencing performance in the court. The confidence comes from the use of AI to predict some criminal behaviour, such as financial fraud. Even though he has not tested the algorithm model in the courtroom to deliver sentences, he is confident in his idea that AI can have a role in the sentencing process.
Dr Andrew says when judges handle a case in the court, they have some “inconsistency” when passing a sentence for a convicted criminal. The inconsistency comes from a judge’s consideration of individual circumstances, societal norms and the sense of justice.
The moral decision and the sense of humanity are based on their experience and even sometimes change the law. Each judge uses their prudence in deciding the outcome of a case. Another “undesirable inconsistency” occurs as bias or even extraneous factors like hunger. Research in Israeli courts has shown that the percentage of favourable decisions drops to nearly zero before lunch.
Judges must ensure similar offences should receive similar penalties in different courts with different judges. Usually, to enhance sentence consistency, the justice system has prepared guidelines as a reference. This inconsistency area is the pain point where AI can help.
How AI Helps Judges
Most modern AI is machine learning, a machine learning algorithm that could learn the patterns in a database to predict patterns and outcomes. Therefore, AI can provide better sentence suggestions after the computer algorithm learns the patterns within a set of data.
Dr Andrew’s machine learning algorithm trained with 302 New Zealand assault cases. The sentences in those cases are between 0 and 14.5 years of imprisonment. The model quantifies sentences based on certain phrases and terms when calculating the sentence. Then the algorithm built a model that can predict the length of a sentence for a new case and explain why it made certain predictions.
The relatively simple model worked quite well within the average error of the model in under 12 months. The model associates the words or phrases such as “sexual”, “young person”, “taxi” and “firearm” with longer sentences. While shorter sentences were given to cases with words like “professional”, “career”, “fire” and “Facebook”.
Beyond Decision Making
In the future, AI could be used as an evaluation tool for judges. They could understand better their sentencing decisions and perhaps remove extraneous factors. The models also have the potential to be used by lawyers, providers of legal technology and researchers, to analyse the sentencing and justice system. Moreover, AI also can be used for controversial sentences and help create some transparency around controversial decisions.
Of course, the use of AI in the justice system may still be controversial. Most people are still keen that the final assessments and decisions on justice and punishment should be made by human experts. But maybe it is the right time need to give an opportunity to an “algorithm” or “AI” in the judicial system for the common good.
New Zealand is not the only country that explores the use of Artificial Intelligence (AI) in courtrooms. Several other countries like China and Malaysia have done similar things. In China, robot judges can decide on a small case. While in Malaysia, some courts have used AI to recommend sentences for offences such as drug possession.
An international team led by The Chinese University of Hong Kong (CUHK)’s Faculty of Medicine (CU Medicine) has successfully developed the world’s first artificial intelligence (AI) model that can detect Alzheimer’s disease solely through fundus photographs or images of the retina. The model is more than 80% accurate after validation.
Fundus photography is widely accessible, non-invasive and cost-effective. This means that the AI model incorporated with fundus photography is expected to become an important tool for screening people at high risk of Alzheimer’s disease in the community. Details have been published in The Lancet Digital Health under the international journal The Lancet.
Limitations of Alzheimer’s disease current detection methods
In Hong Kong, 1 in 10 people aged 70 or above suffers from dementia, with more than half of those cases attributed to Alzheimer’s disease. This disease is associated with an excessive accumulation of abnormal amyloid plaque and neurofibrillary tangles in the brain, leading to the death of brain cells and resulting in progressive cognitive decline.
The Clinical Professional Consultant of the Division of Neurology in CU Medicine’s Department of Medicine and Therapeutics stated that memory complaints are common among middle-aged and elderly people, and are often considered a sign of Alzheimer’s disease.
It is sometimes difficult to make an accurate diagnosis of Alzheimer’s disease based on cognitive tests and structural brain imaging. However, methods to detect Alzheimer’s pathology, such as an amyloid-PET scan or testing of cerebrospinal fluid collected via lumber puncture, are invasive and less accessible.
To address the current clinical gap, CU Medicine has led several medical centres and institutions from Singapore, the United Kingdom and the United States to successfully develop an AI model using state-of-the-art technologies which can detect Alzheimer’s disease using fundus photographs alone.
Studying disorders of the central nervous system via the retina
The S.H. Ho Professor of Ophthalmology and Visual Sciences and Chairman of CU Medicine’s Department of Ophthalmology and Visual Sciences explained that the retina is an extension of the brain in terms of embryology, anatomy and physiology. In the entire central nervous system, only the blood vessels and nerves in the retina allow direct visualisation and analysis.
Thus, it is widely considered a window through which disorders in the central nervous system can be studied. Through non-invasive fundus photography, a range of changes in the blood vessels and nerves of the retina that are associated with Alzheimer’s disease can be detected.
The team developed and validated their AI model using nearly 13,000 fundus photographs from 648 Alzheimer’s disease patients (including patients from the Prince of Wales Hospital) and 3,240 cognitively normal subjects. Upon validation, the model showed 84% accuracy, 93% sensitivity and 82% specificity in detecting Alzheimer’s disease. In the multi-ethnic, multi-country datasets, the AI model achieved accuracies ranging from 80% to 92%.
Accessibility, non-invasiveness and high cost-effectiveness of the AI model using fundus photography help the detection of Alzheimer’s cases both in the clinic and the community
A Professor of Medicine and Director of the Therese Pei Fong Chow Research Centre for Prevention of Dementia at CU Medicine stated that in addition to its accessibility and non-invasiveness, the accuracy of the new AI model is comparable to imaging tests such as magnetic resonance imaging (MRI).
It shows the potential to become not only a diagnostic test in clinics but also a screening tool for Alzheimer’s disease in community settings. Looking ahead, the team aims to validate its efficacy in identifying high-risk cases of the disease hidden in the community, so that various preventive treatments such as anti-amyloid drugs can be initiated early to slow down cognitive decline and brain damage.
The Associate Professor in the Department of Ophthalmology and Visual Sciences at CU Medicine said that in addition to applying novel AI technologies in the model, the team also tested it in different scenarios. Notably, their AI model retained a robust ability to differentiate between subjects with and without Alzheimer’s disease, even in the presence of concomitant eye diseases like macular degeneration and glaucoma which are common in city-dwellers and the older population.
Their results further support the hypothesis that the team’s AI analysis of fundus photographs is an excellent tool for the detection of memory-depriving Alzheimer’s disease. To move this research towards clinical application, the team is developing an integrated, AI-based platform to combine information from both blood vessels and nerves of the retina captured by fundus photography and optical coherence tomography for the detection of Alzheimer’s disease. Their findings should provide more evidence to move AI from code to the real world.
The Indonesian government disclosed four potential uses of Big Data and AI to improve its e-government programmes. These two technologies, they feel, have the potential to support disaster identification and preventive action, prevention of illegal activities and cyber-attacks and increase workforce effectiveness.
The Director General of Informatics Applications, Semuel A. Pangerapan, explained several scenarios for Big Data. According to him, the government can use Big Data to improve critical event management and the quality of the response by identifying problem points through Big Data Analytics. For example, the agencies can be better prepared to prevent and mitigate natural disasters such as drought, epidemics or massive accidents occur.
In addition, Big Data can also enhance the government’s ability to prevent money laundering and fraud through better surveillance to detect such illegal activities.
Furthermore, Big Data significantly reduces the possibility of cyber-attacks. Cyber-attacks can come from external parties, data leaks or internally for a variety of reasons. An analysis of patterns and unusual activities can help in preventing or managing such cyber issues.
Big Data and analytics can contribute to workforce effectiveness by increasing monitoring. In addition, it can be used for policy design, decision-making and gaining insights.
Semuel stressed the importance of data analysis after collecting all data in the right fashion. Data is only valuable if it is collected correctly and then analysed – data will only provide benefits if processed in the right way. “In its implementation, AI helps analyse existing Big Data, providing data understanding or insight to help make decisions,” he explained.
Another advantage of AI is the ability to speed up new implementation services and corrections in real-time. At the evaluation stage, AI can also provide suggestions for adjustments and improvements to subsequent policies.
Currently, the encourages the improvement of the quality of Big Data and AI innovation through the development of e-government. The Indonesian government is also open to third parties to accelerate Big Data and AI use.
E-government has made progress in recent years and received appreciation from the United Nations in 2020. The UN said that Indonesia’s e-government development index rose to rank 88 from previously ranked 107 in 2018. Indonesia’s e-participation index has also increased from rank 92 in 2018 to 57 in 2022.
“The two rankings show an increase in the quality of Indonesia’s e-government and the level of community activity in using e-government services,” said Semuel.
However, the government faced challenges in implementing these two technologies. Overlapping and data replication is one of the main problems. “Regulatory obstacles in the procurement of government Big Data infrastructure also need to be overcome. Then compliance with international standards for the national Big Data ecosystem is also still the government’s homework.”
To optimise AI use, Semuel emphasised the need for a skilled workforce, regulations governing the ethics of using AI, infrastructure, and industrial and public sector adoption of AI innovations.
The government is implementing several solutions to overcome challenges. First, they have provided suitable facilities in the form of National Data Centres (NDCs) in four separate locations. The NDCs will accommodate Government Cloud and contain national data across sectors.
Optimisation of data centre utilisation needs to be supported by staff with qualified expertise. For this reason, the government is holding digital skills training on AI and Big Data through the Digital Talent Scholarship (DTS) and Digital Leadership Academy (DLA) programs.
Apart from facilities and upskilling, Indonesia is looking to develop a business ecosystem that utilises AI and Big Data. Support for this comes from the National Movement of 1000 Digital Startups, Startup Studio Indonesia (SSI) and HUB.ID.
Thailand’s Electronic Transactions Development Agency (ETDA) has recently launched the AI Governance Clinic (AIGC) which will serve as a source of Thai and overseas knowledge and expertise on governance related to artificial intelligence (AI) and its adoption.
ETDA is joining forces with the nation’s National Electronics and Computer Technology Centre (NECTEC), the Ministry of Public Health’s Department of Medical Services, and the Department of Health Service Support. A memorandum of understanding (MoU) between ETDA and the three partners was signed during the nation’s “Building Trust and Partnership in AI Governance” event.
AI is currently having a significant impact on almost every aspect of people’s lives, including work, business, education, finance, health, and electronic transactions, according to ETDA Executive Director Dr Chaichana Mitrpant. “These issues all involve the application of AI.”
A six-year national AI implementation plan for national development between 2022 and 2027 was recently approved by the Cabinet. The adoption of AI with governance along with pertinent laws and regulations is one strategy outlined in the plan for ensuring that users understand social responsibility.
Thailand is getting ready to adopt AI, another cutting-edge technology that is gaining popularity and relevance. ETDA is an organisation that supports a secure and reliable ecosystem for electronic transactions.
To achieve the objectives outlined in the implementation plan, the agency is collaborating with NECTEC. A study on Thailand’s AI standard landscape to develop AI adoption measures and a study on measures to assess AI-based computer programmes to increase the capacity of Thai entrepreneurs in all industries in accordance with international standards are among their important joint projects.
To create a framework for AI governance regarding electronic transactions that are in line with Thailand’s context and international standards, ETDA and its partners – both in Thailand and abroad – established the Clinic.
The Clinic is collaborating with the Academy of Digital Transformation by ETDA to provide resources for capacity development at all levels. Additionally, the AIGC has a substantial library of knowledge sources on pertinent topics, as well as experts from numerous nations who are prepared to provide guidance on AI policies and governance.
An additional Memorandum of Understanding (MoU) was signed by ETDA and its partners NECTEC, the Department of Medical Services, and the Department of Health Service Support for the joint development of an AI governance framework that is appropriate for the Thai context for the country’s healthcare industry.
The collaboration aims to advance the sharing of innovation and AI technology knowledge among the participating agencies and to inform pertinent agencies about AI governance. Thailand’s AI strategy was inspired by a desire to boost the nation’s economy and the quality of life for its people as well as a competitive spirit.
Thailand strives to develop the human capacity and skills required for an AI ecosystem despite the difficulties it faces in developing AI capabilities. They created a formal network and consortium as a result. Thailand will train future AI professionals through structured academic programmes in Thai universities, in addition to bridging the gap between existing academic and industrial experts.
ETDA is the primary agency responsible for developing, promoting, and supporting electronic transactions and it is part of the Ministry of Information and Communication Technology. Its primary responsibility is to research, study, and support the operation of the Electronic Transaction Committee and other related agencies, hence, it contributes to the development and promotion of Thailand’s electronic transactions.
The CSIRO’s Next Generation Graduates Programmes are industry-university partnerships aimed at developing a pipeline of home-grown, job-ready graduates to unlock the immense economic opportunity offered by AI and emerging technologies.
In this latest round, 14 programmes were funded, with RMIT leading four, including two by its Centre for Industrial AI Research and Innovation (CIAIRI), one by its Enterprise AI and Data Analytics Hub, and one by the Sir Lawrence Wackett Defence and Aerospace Centre. RMIT will also support a further three.
These programs will provide generous scholarships to domestic PhD students which allows them to be part of a multi-disciplinary team aimed at solving real-world challenges. The programmes are:
1. AI for Next Generation Food & Waste Systems (RMIT led, La Trobe supported)
This programme addresses the skills shortage in adopting advanced AI technologies in the areas of food and waste, a critical national manufacturing priority. This will boost food productivity, improve food quality control and logistics, reduce, and better manage waste generated during the life cycle of food production and consumption.
Through a range of industry-driven research activities, this program will produce a cohort of graduates that are not only equipped with practical AI skills but also ready to integrate into food and waste related industry sectors to generate real impact.
2. Developing Digital Capabilities to Support the Aged Care Sector (RMIT led, Victoria and Newcastle supported)
One of the recommendations in the Royal Commission into Aged Care Quality and Safety is to adopt technology to transform the aged care system so that carers’ time can be best used to deliver quality care. The report also recommended the use of technology to increase the connectedness of older Australians – to one another, families and carers, and to the broader community.
This programme aims to reimagine the role of technologies like AI, AR/VR and sensors which are critical in ensuring the sustainability of the sector. Our industry partners are driven by these challenges every day thus, the research undertaken in this programme will have great significance and impact.
3. AI Techniques for Emergency Management and Critical Infrastructure (RMIT led, Sydney Uni supported)
This programme will produce a cohort of graduates with much-needed skills in AI to support critical infrastructure and community safety. Some of the common AI techniques across the selected projects are:
- computer vision -creating 3D reconstructions from 2D images of interior designs and detecting potential hazards and threats via surveillance videos
- agent-based modelling and simulation (ABMS) – which is becoming increasingly popular to model and simulate the management of disaster events such as floods and bushfires and
- digital twin technology – which involves complementary approaches of digitising models of infrastructure, people, and business processes and one of the projects investigates the integration of all three aspects.
4. Applied AI and Digital Innovation for Defence and Aerospace Applications (RMIT led, Charles Darwin supported)
This programme will deliver graduates capable of tackling Australia’s pressing current and future challenges in the defence and aerospace sectors through the application of AI and digital technologies. It will expand opportunities for diverse communities of students and create workers skilled in emerging technologies, including applied AI, digital twins and threads, machine learning, robotics, cyber security, and modern manufacturing.
This interdisciplinary program builds on the strategic partnership between RMIT University and Charles Darwin University (CDU), which will see the creation of a joint Aerospace and Defence Industries 4.0 TestLab in the Northern Territory.
5. AI for Clean Energy and Sustainability (Monash led, RMIT supported)
Delivering clean and sustainable energy and enabling energy transition is a global challenge. AI is expected to play a significant role in this transition by enabling more effective models and tools, accurately predicting reliable supply, optimising maintenance and operations, making smarter decisions and assessing risk.
This programme will focus on the Recycling and Clean Energy National Manufacturing Priority to teach a variety of HDR students innovative AI technologies driven by these industry priorities.
6. Central Bank Digital Currency – Infrastructure & Applications (Macquarie led, RMIT and UTS supported)
A Central Bank Digital Currency (CBDC) would be a new digital form of money issued by the Reserve Bank. It could be designed for retail or general use, like a digital version of banknotes.
The development and deployment of robust, efficient and trusted CBDC requires the design, engineering, proving and integration of a suite of technologies including blockchain, security and privacy-preserving solutions and regtech (surveillance, alerting and compliance) technologies and the skilled graduates to help implement them.
7. Artificial Intelligence of Things Empowering Industrial Digital Twin (La Trobe led, RMIT and Swinburne supported)
This programme will develop new digital twin solutions powered by a combination of AI and the Internet of Things (IoT), to meet the needs of industry partners, seeking improved productivity and reduced maintenance and management costs.
By representing physical objects digitally, digital twins can harness real-time IoT data and optimise performance using AI and data analytics. Several research and industry challenges will be addressed, including accurate 3D modelling, digital twin model optimisation, reliable connectivity between the physical world and the digital world, and edge AI models.
Astronomers from the California Institute of Technology (Caltech) have completely automated the classification of 1,000 supernovae using a machine-learning (ML) algorithm. The Zwicky Transient Facility, or ZTF, a sky survey instrument located at Caltech’s Palomar Observatory, collected data that the algorithm was then used to analyse.
“We needed a helping hand, and we knew that once we trained our computers to do the job, they would take a big load off our backs,” says Christoffer Fremling, a staff astronomer at Caltech and the mastermind behind the new algorithm tagged as SNIascore.
A year and a half after SNIascore classified its first supernova in April 2021, they are approaching the pleasant milestone of 1,000 supernovae. Every night, ZTF scans the night sky for alterations known as transient events. This covers everything, from asteroids in motion to recently devoured stars by black holes to exploding stars known as supernovae.
ZTF notifies astronomers worldwide of these transient events by sending out hundreds of thousands of alerts each night. Other telescopes are then used by astronomers to monitor and learn more about the nature of the shifting objects. Thousands of supernovae have so far been found thanks to ZTF data.
Members of the ZTF team cannot organise all the data on their own due to the constant flow of data that comes in every night. According to Matthew Graham, project scientist for ZTF and research professor of astronomy at Caltech, “the traditional notion of an astronomer sitting at the observatory and sieving through telescope images carries a lot of romanticism but is drifting away from reality.”
Instead, to help with the searches, the team has created ML algorithms. SNIascore was created to categorise potential supernovae. There are two main categories of supernovae: Type I and Type II. In contrast to Type II supernovae, Type I supernovae are devoid of hydrogen.
When material from a companion star flows onto a white dwarf star, causing a thermonuclear explosion, a Type I supernova is produced. When a massive star collapses due to its own gravity, a Type II supernova happens. Type Ia supernovae, or the “standard candles” in the sky, can be classified by SNIascore. These are dying stars that explode with a steady-state thermonuclear blast.
Astronomers can gauge the universe’s expansion rate thanks to Type Ia supernovae. Fremling and colleagues are currently expanding the algorithm’s capabilities to classify additional types of supernovae soon.
Every night, after ZTF has recorded sky flashes that may be supernovae, it sends the data to the SEDM spectrograph at Palomar, which is in a dome a short distance away (Spectral Energy Distribution Machine).
To determine which supernovae are likely Type Ias, SNIascore collaborates with SEDM. As a result, the ZTF team is working quickly to compile a more trustworthy data set of supernovae that will allow astronomers to conduct additional research and, ultimately, learn more about the physics of the potent stellar explosions.
“SNIascore is incredibly precise. We have observed the performance of the algorithm in the real world after 1,000 supernovae” says Fremling. Since the initial launch in April 2021, they have found no clearly misclassified events, and they are now planning to implement the same algorithm with other observing facilities.
According to Ashish Mahabal, who oversees ZTF’s machine learning initiatives and is the centre’s lead computational and data scientist at Caltech, their work demonstrates how ML applications are maturing in near real-time astronomy.
The SNIascore was created as part of the ZTF’s Bright Transient Survey (BTS), which is currently the most comprehensive supernova survey available to the astronomical community. The entire BTS dataset contains nearly 7000 supernovae, 90 per cent of which were discovered and classified by ZTF while the remaining 10 per cent were contributed by other groups and facilities.