Plus ultra: extreme supernovae beyond the standard paradigm of cosmic explosions

超新星(SNE)是恒星生命的最后阶段的恒星爆炸，在恒星演化、星系形成的反馈、恒星和行星形成的几乎所有元素和原材料的合成和分配等天体物理领域发挥着重要作用。在过去的十年里，随着广域观测的到来，随着瞬变现象的惊人发现，开辟了时间域天文学的新参数空间，打破了我们对恒星爆炸的理解。它们可以分为三类：1-一群超亮的“超光速”超新星，亮度约为经典超新星类型的100倍，为高红移宇宙提供了新的探测器，并有可能形成一种新的标准蜡烛；2-瞬变显示出快速上升和随后的快速衰变，不像任何常见的河外瞬变；3-发生在低金属丰度或低光度环境中的具有极端能量或复杂演化的瞬变。共识是金属丰度、初始质量和多重性影响我们观察到的SN的类型，但它们的确切作用尚未得到描述。这阻碍了我们使用SN作为宇宙中恒星形成的探测器，也阻碍了我们理解这种极端超新星的质量和爆炸机制中是否编码了先驱金属丰度。对这种近地天体及其宿主的详细分析，无论是在其局部(超新星位置)还是全球(宿主星系作为一个整体)环境中，通常仅限于少数几个通常发光的宿主星系。然后，将使用机器学习方法来检索环境信息和从超新星检索到的信息之间的任何联系。如果数据集足够丰富，就可以建立一个人工智能算法来预测未来极端超新星的宿主可能是哪种星系，并在维拉鲁宾天文台将于2023年提供的22TB数据流中足够早地识别它们。在这个项目中，博士生将收集与环境属性相关的超新星爆炸知识，以及蟒蛇编程技能、机器学习/人工智能、观测天文学和统计学方面的经验。

Supernovae (SNe), stellar explosions staging the final act of a star's life, play an important role in many astrophysical domains, for instance stellar evolution, feedback in galaxy formation, synthesis and distribution of almost all the elements and raw materials for both star and planet formation. The last ten years, with the advent of wide-field surveys, have opened up a new parameter space in time-domain astronomy with the surprising discovery of transients defying our understanding of how stars explode. These can be grouped into three categories: 1- a population of ultra-bright 'superluminous' supernovae, some 100 times brighter than classical supernova types, offering new probes of the high redshift universe and the potential for a new class of standard candle; 2 - transients showing fast rise and subsequent rapid decay that do not resemble any common class of extragalactic transient; 3 - transients with extreme energetics or complex evolution happening in low-metallicity or low-luminosity environments.The consensus is that metallicity, initial mass and multiplicity influence the type of SN we observe but their precise role has not been characterised. This impedes our ability to use SN as probes of star formation across the Universe and to understand if the progenitor metallicity is encoded within the mass and explosion mechanism of such extreme supernovae. Detailed analyses of such SNe and their hosts, both in their local (supernova position) and global (host galaxy as a whole) environment, have generally been restricted to a few, usually luminous, host galaxies. Machine learning approaches will then be used to retrieve any link between the environmental information and those retrieved from the supernova. If the dataset will be rich enough, an Artificial intelligence algorithm can be built to predict what kind of galaxy will likely be the host of future extreme Supernovae and to recognize them early enough in the 22 Terabyte stream of data (per observing night) that the Vera Rubin observatory will deliver from 2023.In this project, the PhD student will gather knowledge of supernova explosions linked to the environment properties as well as programming skills in python, machine learning / Artificial Intelligence, experience in observational astronomy and statistics.