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Instant scholar: From rock star to astrophysicist, Brian May's celestial pursuit of interplanetary dust
Instant scholar: From rock star to astrophysicist, Brian May's celestial pursuit of interplanetary dust

Time of India

time7 days ago

  • Entertainment
  • Time of India

Instant scholar: From rock star to astrophysicist, Brian May's celestial pursuit of interplanetary dust

Representative image In the world of rock music, Brian May is celebrated as the iconic guitarist of Queen , a band that redefined the musical landscape of the 1970s and 80s. With his homemade 'Red Special' guitar and genre-defining solos in hits like Bohemian Rhapsody, We Will Rock You, and I Want It All, May carved his name into rock legend. But behind the stage lights and amplifiers, another passion flickered—one rooted not in music, but in the mysteries of the cosmos. This lesser-known chapter of Brian May's life came full circle in 2007, when he completed a long-delayed PhD in astrophysics, nearly four decades after beginning it. Titled "A Survey of Radial Velocities in the Zodiacal Dust Cloud", May's doctoral thesis is a blend of meticulous observational astrophysics and theoretical analysis, focusing on the interplanetary dust that forms the Zodiacal Cloud—a faint, diffuse band of light visible in the night sky under ideal conditions. This dust, remnants of comets and asteroid collisions, orbits the Sun and plays a crucial role in our understanding of the solar system's formation and dynamics. A Thesis Interrupted by Stardom Brian May first began work on his PhD at Imperial College London in the early 1970s. A physics and mathematics graduate, he was fascinated by solar system phenomena. Under the supervision of renowned astronomer Jim Ring, May embarked on an ambitious observational programme to measure the Doppler shifts in the light scattered by dust particles in the zodiacal cloud. His goal: to determine the radial velocities—essentially, the speed and direction of motion—of these dust particles relative to Earth. by Taboola by Taboola Sponsored Links Sponsored Links Promoted Links Promoted Links You May Like When Knee Pain Hits, Start Eating These Foods, and Feel Your Pain Go Away (It's Genius) Click Here However, May's academic journey was soon interrupted. Queen's rise to stardom was meteoric, and the demands of touring, recording, and public life meant his research took a back seat. For over 30 years, the project was left incomplete, but it remained on his mind. 'I never gave up hope that one day I would return to it,' he said in interviews. In 2006, with encouragement from former collaborators and Imperial College faculty, he dusted off his old notes, updated the literature review to reflect decades of advances, and resumed his research. The Science Behind the Stardust The zodiacal cloud is composed of countless micron-sized dust particles that orbit the Sun in the plane of the ecliptic. This dust reflects sunlight, producing a faint glow known as zodiacal light, most easily seen just before sunrise or after sunset in areas with minimal light pollution. Though ethereal in appearance, the dust has significant scientific relevance. Its dynamics help scientists understand the processes that shaped the early solar system, and its interaction with planetary bodies and solar radiation continues to influence space weather. Brian May's approach to studying this dust was through spectroscopic analysis—specifically, by examining the Doppler shifts in the Mg I (magnesium) absorption line at 5183.6 angstroms in the scattered sunlight. This shift provides information about the velocity of the scattering dust particles along the line of sight. If a particle moves toward Earth, the absorption line shifts slightly toward the blue end of the spectrum; if it moves away, the line shifts toward the red. To collect the necessary data, May constructed and deployed a pressure-scanned Fabry–Pérot interferometer—a highly precise optical instrument designed to measure very small wavelength shifts in light. Observations were conducted at the Teide Observatory on Tenerife in the Canary Islands, where the high altitude and clear skies made it an ideal location. Over two extended observing sessions in 1971 and 1972, May gathered more than 250 Fabry–Pérot scans of the zodiacal light from various points along the ecliptic. These spectra were later digitized and analysed for shifts in the Mg I line, allowing the determination of radial velocities of the dust in different parts of the sky. Findings and Implications May's results showed that the dust particles in the zodiacal cloud do not move in a purely circular fashion. Rather, their motion exhibited asymmetries that suggest a mixture of orbital inclinations and eccentricities. In particular, he found evidence for a retrograde component—a subset of particles moving in the opposite direction to the planets' orbits—as well as signs of interstellar dust inflow, consistent with theories that our solar system continuously sweeps up material from the interstellar medium. His measurements also supported a Keplerian motion model, wherein the dust follows elliptical orbits governed by the Sun's gravity, but also pointed to the influence of Poynting–Robertson drag—a process by which dust spirals slowly toward the Sun due to the combined effect of solar radiation pressure and the particles' own orbital motion. In the broader context of planetary science, May's work adds observational depth to the understanding of how dust evolves within the solar system. These insights are vital for calibrating space instruments, planning interplanetary missions, and understanding the debris environment through which spacecraft travel. A Thesis Completed—and a Scientist Reborn In 2007, May successfully defended his thesis and was awarded a PhD by Imperial College London, more than 36 years after beginning the work. The completed dissertation included a new literature review covering the intervening decades of research on interplanetary dust, updated data analysis methods, and critical comparisons with satellite missions like IRAS (Infrared Astronomical Satellite) and Helios, which provided additional context and validation. The completion of the thesis also marked May's full-circle return to science. He co-authored the popular science book Bang! – The Complete History of the Universe with astronomer Sir Patrick Moore and astrophysicist Chris Lintott, and later became Chancellor of Liverpool John Moores University. His blend of music and science has inspired students and fans alike, showcasing that intellectual curiosity knows no genre. Reflections on Dual Legacies Brian May's doctoral thesis is more than a scientific contribution—it is a testament to perseverance, intellectual ambition, and the bridging of two seemingly disparate worlds. In a sense, May represents the rare individual who refused to choose between his passions and instead found harmony between them. 'I'm a scientist at heart,' May has said. 'Music is my emotional outlet, but science is my way of understanding the universe.' Through 'A Survey of Radial Velocities in the Zodiacal Dust Cloud', May has made a meaningful mark in the field of astrophysics, proving that rock stars can reach for the stars in more ways than one. Ready to navigate global policies? Secure your overseas future. Get expert guidance now!

How AI Enables Marketers To Target Digital-Savvy Gen Z Customers
How AI Enables Marketers To Target Digital-Savvy Gen Z Customers

Forbes

time01-07-2025

  • Business
  • Forbes

How AI Enables Marketers To Target Digital-Savvy Gen Z Customers

Wolfgang Sixl, VP Strategy, Analytics & Client Solutions, MCE Systems - Bridging Technology and Commercialization. How can we understand the mindset of Gen Z consumers? We can ask Freddie Mercury. He seemed to anticipate their desire for immediate gratification when he wrote the song 'I Want It All.' Then, he nailed their longing for personalization with the song 'I Want To Break Free.' Obviously, the members of Queen were not thinking about the brand experiences of young digital consumers when they were penning lyrics 40 years ago. Nevertheless, these titles are uncannily relevant. Today's digital-savvy consumers are seeking not only customization, but also expediency. The data confirms it. According to a Salesforce consumer trend report, 81% of customers expect faster service as technology advances, and 73% expect better personalization. In response, companies are jockeying for a better position. They are exploring new technologies that grab attention and improve loyalty—and help them meet customers' demands. AI Makes It Personal This isn't a new trend. Marketers already use tools like programmatic digital ads to deliver custom promos, targeting users based on past brand interactions or shopping behavior. Now, AI tools are pushing this further. But mass customization marketing has had limited success, falling short of the true hyper-personalization consumers expect. Two years ago, it looked promising. ChatGPT dazzled with its capabilities, and 98% of companies felt a newfound urgency to adopt AI. Yet success remains elusive. While generating blogs, emails and ad copy is easy, creating personalized user journeys that drive real business results is another challenge entirely How can they change this? I think there are three key barriers to overcome: • Data Relevancy And Accuracy: In the marketing context, we want to prompt the customer to take an action. Here, AI engines can help. They can craft relevant offers calibrated to a user's specific tastes—but only if they are fed relevant and accurate enterprise-specific data. So, organizations must make sure the AI model has enough context to deliver the appropriate output. • Data Timeliness: Relevant data is only half the story. Data must be up to date too. Real-time user and market information is vital for personalization. Without it, we can imagine a scenario where an AI model advertises outdated offers or—worse—sends irrelevant promotions to customers who have already converted. • Execution: AI tools are new and unfamiliar to many organizations. It's very easy to implement a dysfunctional AI program. Common challenges in this space include aligning the tech to business objectives, overcoming staff resistance to change and ensuring the right people are leading the AI project. A Program To Prepare For Change To overcome the above challenges, stakeholders within a business must align. They must build guardrails that overcome model limitations, leverage enterprise-specific customer data and build an AI-native culture. Large enterprises should start by collecting enterprise data (which includes real-time and metadata) from their digital customer points of interaction. They can couple this with a basic LLM model to create a 'sandboxed' enterprise-grade AI application. The output of the resulting AI application will combine the customer's preference with live market data. Second, businesses must make sure their AI application is safe and focused—and avoids undesired outputs. They should prevent any exposure to malicious actors and make sure they follow all regulatory compliance to avoid violations and legal risks. Lastly, enterprises must unite all staff behind the new processes. This starts with senior leaders establishing a cohesive business-technology plan, and assembling the right team to manage the changes. The last point is vital. Workers across all industries are understandably anxious about AI. Nearly 60% of business leaders said in a survey this alarm was driven by a lack of knowledge. So organizations must foster a culture of openness to defuse employee concerns. A Success Story: Mobile Device Care For Mobile Carriers Even at this formative stage for AI, thousands of large enterprises are already seeing results. Javier Meza, Coca Cola's Europe CMO, shared last year how his company is using first-party data and AI to shift from a broad targeting style to a more consumer-centric approach. Mobile carriers are well-placed to follow this example. These businesses possess huge amounts of relevant real-time first-party data. Clearly, the raw materials are there to deliver personalized services that can drive loyalty, prevent churn and grow ARPU. Some carriers are already experimenting. Take the market for device care and upgrades, for example. Industry data that we see at my company shows that customers want intelligent, personalized offers when they encounter an issue with their device—such as a value added service (VAS) or a trade-in. But for years, this has been a clunky, manual process that has not served customers well. At MCE, we saw an opportunity to make device care digital and self-serve. First, we built a GenAI solution for chatbots in a mobile operator app. Then we connected it to an enterprise-grade application that sources live device data such as diagnostics, device configuration information and user conversational inputs. The final part was tapping into a carrier's existing marketing database, so we partnered with a third party to build out a chatbot for them. With this real-world application, we were able to reduce mobile app utility inertia, increase marketing promo actioning and drive more retail visit intent. When we applied GenAI to this solution, we noticed quite an uptick in engagement afterward—quadruple engagement with our pilot's custom journey, triple marketing promo engagement and nearly double the retail store locator opens. Conclusion AI is game-changing technology. It gives marketers the chance to deliver on the dream of genuine mass personalization. The examples are already out there. So, now is the time to get ahead of the game, embrace the tech, build a unified internal culture and give customers the products and services they deserve. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?

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