The first stars and black holes

大爆炸后4亿年，宇宙在缓慢膨胀的过程中显得黑暗而空洞。突然间，第一批恒星形成了，照亮了宇宙，形成了我们今天看到的星系。我们对这个时代的观察很少，它弥补了我们知识中超过10亿年的空白。理论表明，这是一个恒星诞生的时代，其质量高达太阳的一百倍，第一个黑洞开始出现。这些第一批物体发出热和光，在周围的氢气中产生气泡，我们将首次使用射电望远镜观察到这些气泡。这些气泡是如何形成的，它们是如何成长的，这将告诉我们第一批恒星和黑洞是如何诞生、生存和死亡的。搜寻这种信号是一项挑战，因为我们不知道它是什么样子的，当我们调谐到无线电波时，我们也会探测到从附近爆炸的恒星到移动的手机的信号。这种噪声覆盖了第一批恒星信号的1000倍，使得寻找未知信号成为一个真正的大海捞针的挑战。我已经开发出了消除噪音并找到第一颗星星信号的必要方法，尽管我不知道它是什么样子。利用我的工作来设计SKA，这是澳大利亚数百万个天线的阵列，我们将进行观测，追溯到我们宇宙的黑暗时代，创造一部我们宇宙成长超过10亿年的电影。该项目将涉及工作与真实的数据从SKA探路者称为LOFAR。我们将使用技术来消除这个微小信号上的大量噪声，并使用机器学习和贝叶斯统计来提取我们所能获得的关于天体物理学的一切信息。这个项目适合那些想要直接进入数据的人，同时使用我们学到的知识来通知下一代设施，这是我们的高跟鞋。

400 million years after the Big Bang, the Universe appeared dark and empty as it slowly expanded. Suddenly the first stars formed, lighting up the Universe and forming the galaxies we see today. We have few observations of this era and it makes up over a billion year gap in our knowledge. Theories show that this is the era when stars were born with up to a hundred times the mass of our Sun, and the first black holes began to appear. These first objects gave out heat and light, making bubbles in the surrounding hydrogen gas that we will observe for the first time using radio telescopes. How these bubbles are shaped and how they grow will tell us how those first stars and black holes were born, lived and died. Searching for this signal is challenging since we don't know what it looks like and when we tune in to the radio waves, we also detect signals from everything from nearby exploding stars to mobile phones. This noise covers the first stars signal a 1000 times over, making the search for an unknown signal a true needle-in-a-haystack challenge. I have developed methods essential for removing that noise and finding that first star's signal, even despite not knowing what it looks like. Using my work to design the SKA, an array of millions of antennas in Australia, we will make observations stretching back into the Dark Ages of our Universe, creating a movie of our Universe growing up over a billion years. This project will involve working with real data from an SKA pathfinder called LOFAR. We will use techniques to remove the overwhelming amount of noise on top of this tiny signal, and extract everything we can about the astrophysics, using machine learning and Bayesian statistics. This project is suitable for someone who wants to get straight into the data, while using what we learn to inform the next generation facility, which is hot on our heels.