Software squeezes more data out of Webb Telescope for imaging exoplanets

With a huge and still-growing catalog of exoplanets, researchers are eager to take the next steps: trying to infer what conditions are like on them, either by imaging them directly or by capturing the light that has passed through their atmosphere. Both are substantial challenges, given the presence of stars that are far, far brighter than light that has come to us via the planet. While telescopes can be fitted with devices called coronagraphs that block out much of a star’s light, there are limits to their effectiveness, caused by noise from subtle variations in the telescope’s configuration and pointing between observations. Now, researchers have developed a new software package that optimizes a physical model of the telescope in a way that allows us to better model these differences, producing low-noise imaging of exoplanets.

随着系外行星目录的不断扩大，研究人员渴望迈出下一步：试图推断这些行星上的状况，无论是通过直接成像，还是通过捕捉穿过其大气层的光线。考虑到恒星的光芒远比行星传来的光线明亮得多，这两者都面临着巨大的挑战。虽然望远镜可以配备被称为“日冕仪”的装置来遮挡大部分恒星光线，但由于望远镜在观测期间的配置和指向存在细微变化，会产生噪声，从而限制了这些装置的有效性。现在，研究人员开发了一种新的软件包，可以优化望远镜的物理模型，从而使我们能够更好地模拟这些差异，生成低噪声的系外行星图像。

Point spreads Central to this work is the concept of a point spread function. If you use a telescope to image a single point of light, it will not appear that way in the actual image. Instead, it will be slightly blurry and spread out, due to imperfections in the telescope hardware and the limits of our ability to keep it pointed accurately through the time it takes to image something. The mathematical description of how a telescope distorts a point of light is called its point spread function. There are two ways to figure out what the point spread function is. One is to use the telescope to image a known target and use those observations to derive a mathematical description of the point spread function. This is effective at reducing noise but has its limits, as the behavior of the telescope hardware can change over time, and the point spread function may vary with the wavelengths being imaged.

点扩散函数（Point spreads）是这项工作的核心概念。如果你使用望远镜对一个光点进行成像，它在实际图像中并不会呈现为一个点。相反，由于望远镜硬件的缺陷以及我们在成像过程中保持精确指向的能力有限，它会变得稍微模糊并向外扩散。描述望远镜如何扭曲光点的数学描述被称为“点扩散函数”。确定点扩散函数有两种方法。一种是使用望远镜对已知目标进行成像，并利用这些观测结果推导出点扩散函数的数学描述。这种方法在降低噪声方面很有效，但也有其局限性，因为望远镜硬件的行为会随时间发生变化，且点扩散函数可能会随成像波长的不同而改变。

The alternative is to try to model the hardware’s behavior and use that model to derive a point spread function. But modern telescopes are incredibly complicated instruments, with many, many factors feeding into their performance in ways that can be difficult to ascertain, even with the full engineering schematics. As a result, these models simply contain too many parameters that need to be tuned to make this route computationally tractable. But anyone paying attention to the world of AI developments will recognize that we’ve become quite successful at tuning billions of parameters in neural networks, in part through a mathematical technique called gradient descent. And, because this is central to training large neural networks, people have put a lot of work into optimizing the performance of gradient descent on GPUs.

另一种方法是尝试对硬件行为进行建模，并利用该模型推导出点扩散函数。但现代望远镜是非常复杂的仪器，有许多因素会影响其性能，即使拥有完整的工程图纸，也很难确定这些因素。因此，这些模型包含的参数太多，需要进行调整，使得这种方法在计算上难以实现。但是，任何关注人工智能发展的人都会意识到，我们已经非常成功地调整了神经网络中的数十亿个参数，这在一定程度上归功于一种称为“梯度下降”的数学技术。而且，由于这是训练大型神经网络的核心，人们在优化 GPU 上的梯度下降性能方面投入了大量工作。

So a team of researchers has developed a process they’re calling FLIP (a non-acronym for Physics-based Learning for Imaging Planets) that uses a physical model of the hardware (in this case, the NIRCam on the James Webb Space Telescope) with 106 possible parameters, and then uses software to optimize all its parameters. The optimization starts with a simulated point spread function, then uses gradient descent to optimize its performance on data from an actual star. Run on a current GPU, this process only takes a bit more than half an hour. The researchers then fine-tune the results on the actual scientific image they are analyzing. This last part of the process is critical, since some variables will differ between observations.

因此，一个研究团队开发了一种他们称为 FLIP（基于物理的行星成像学习，Physics-based Learning for Imaging Planets 的非首字母缩写）的流程，该流程使用硬件（在本例中为詹姆斯·韦伯太空望远镜上的近红外相机 NIRCam）的物理模型，包含 106 个可能的参数，然后使用软件来优化所有参数。优化过程从模拟的点扩散函数开始，然后使用梯度下降法在实际恒星的数据上优化其性能。在当前的 GPU 上运行，这个过程只需要半个多小时。研究人员随后会对他们正在分析的实际科学图像的结果进行微调。过程的最后一部分至关重要，因为某些变量在不同的观测之间会有所不同。

Good results To test how this works, the researchers used observations of two stars with known exoplanets (HIP 65426 and 51 Eridani), along with a third (TYC 5899) that doesn’t have any known exoplanets but is known to vary a lot between observations. These are young stars, and two of them have massive planets that are still warm from their gravitational collapse, allowing them to have been imaged previously. Using Webb data, the team used FLIP to boost the signal-to-noise ratio by a factor of 1.5 to 2, depending on the observations. At a wavelength that can be used to detect carbon dioxide, they were able to spot one of the planets in the Webb data for the first time. For TYC 5899, the approach boosted the sensitivity of the observations by a factor of 15.

良好的结果：为了测试其效果，研究人员使用了两颗已知拥有系外行星的恒星（HIP 65426 和 51 Eridani）的观测数据，以及第三颗（TYC 5899）的观测数据，后者没有已知的系外行星，但已知在观测之间变化很大。这些都是年轻的恒星，其中两颗拥有巨大的行星，由于引力坍缩，它们仍然很热，因此之前曾被成像过。利用韦伯望远镜的数据，该团队使用 FLIP 将信噪比提高了 1.5 到 2 倍，具体取决于观测结果。在可用于探测二氧化碳的波长下，他们首次在韦伯数据中发现了其中一颗行星。对于 TYC 5899，该方法将观测灵敏度提高了 15 倍。

The researchers point out that the applications go beyond getting a better view of known planets. By lowering the noise, it should also be possible to spot dimmer (and hence older or lower-mass) ones that had previously eluded detection. They also think they can modify FLIP to highlight where it’s making the most dramatic changes to the model, which can potentially tell us useful things about what’s going on with the telescope’s hardware.

研究人员指出，其应用不仅限于更好地观察已知的行星。通过降低噪声，也有可能发现以前未被探测到的更暗（因此更古老或质量更小）的行星。他们还认为，他们可以修改 FLIP 以突出显示它对模型做出最显著改变的地方，这可能为我们提供有关望远镜硬件状况的有用信息。