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Challenges in Galaxy Observation with DESI

Astronomers tackle issues of galaxy observation using DESI's fiber assignment techniques.

D. Bianchi, M. M. S Hanif, A. Carnero Rosell, J. Lasker, A. J. Ross, M. Pinon, A. de Mattia, M. White, S. Ahlen, S. Bailey, D. Brooks, E. Burtin, E. Chaussidon, T. Claybaugh, S. Cole, A. de la Macorra, S. Ferraro, A. Font-Ribera, J. E. Forero-Romero, E. Gaztañaga, S. Gontcho A Gontcho, G. Gutierrez, J. Guy, C. Hahn, K. Honscheid, C. Howlett, S. Juneau, D. Kirkby, T. Kisner, A. Kremin, M. Landriau, L. Le Guillou, M. E. Levi, P. McDonald, A. Meisner, R. Miquel, J. Moustakas, N. Palanque-Delabrouille, W. J. Percival, F. Prada, I. Pérez-Ràfols, A. Raichoor, G. Rossi, E. Sanchez, D. Schlegel, M. Schubnell, R. Sharples, J. Silber, D. Sprayberry, G. Tarlé, M. Vargas-Magaña, B. A. Weaver, P. Zarrouk, R. Zhou, H. Zou

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DESI's Fiber Assignment DESI's Fiber Assignment Issues improve galaxy observation accuracy. Astronomers refine techniques to
Table of Contents

In the vast cosmos, millions of galaxies are waiting to be studied, but capturing their light is no easy task. Enter the Dark Energy Spectroscopic Instrument (DESI), a powerful tool designed to help astronomers explore the universe. However, like a clumsy waiter trying to juggle too many plates, DESI faces some challenges. One of these challenges is called "fiber assignment incompleteness."

What is Fiber Assignment?

To explain fiber assignment, imagine you have a big party (the universe), and you want to take pictures of all your friends (galaxies). You have a special camera (the DESI instrument) that can take pictures of many friends at once. The problem arises when you realize that not everyone can fit in your camera's view at the same time, especially when some friends are standing too close together.

DESI uses a set of robotic positioners, kind of like robotic arms, to place optical fibers in the right spot to collect the light from galaxies. But these robotic arms can only reach certain areas at a time, and sometimes they can't pick up the light from certain galaxies because they are too close to others. This leads to the fiber assignment issue – some galaxies are left out of the picture!

The Impact of Missing Galaxies

When some galaxies are missed, it affects the overall image we get of the universe. It’s like trying to paint a beautiful landscape but realizing you’ve left out some trees. The light we do capture doesn't give an accurate picture of how galaxies actually cluster together. This missing information can skew the data that helps scientists understand things like dark energy and the expansion of the universe.

How Do We Fix This?

Now, you might wonder, "How do we fix this problem?" Well, there are a few tricks up our sleeves. Astronomers have developed various techniques to account for the missing observations. Think of it as giving a little extra attention to the spots that got left out during the photo session.

Simulating Observations

One of the first steps we take is to create simulated galaxies that mimic the real ones but with perfect visibility. By using these simulated galaxies, scientists can understand how the fiber assignment works without the complications of fiber collisions.

The Fast Fiber Assignment Emulator

To speed things up, researchers developed a tool called the Fast Fiber Assignment (FFA) emulator. This handy tool allows scientists to generate thousands of simulated fiber assignments quickly, helping to assess the impact of the fiber assignment issues without breaking a sweat.

Strategies for Mitigation

Astronomers organize their strategies into two main categories: measurement stage and model stage.

Measurement Stage

During the measurement stage, techniques are applied directly to the data to address the missing galaxies. For example, one such technique is called "pairwise inverse probability weights." This is a fancy way of saying that when scientists look at pairs of galaxies, they give a little extra weight to those that might have been missed. This helps restore balance in the galaxy count.

Model Stage

In the model stage, scientists adjust their theoretical models to account for the fiber assignment incompleteness. This is like taking a step back and saying, "Okay, let’s tweak our approach to make sense of what we actually see."

One popular method involves excluding small angular separations where the collisions happen, making it easier to see the larger clustering patterns without getting bogged down by the close-up chaos.

The Cosmic Clue Hunt

With various techniques in place, scientists can now better analyze the distribution of galaxies. They use both mock catalogs and real data to quantify the effects of fiber assignment and validate the different strategies. This helps to ensure that the insights they gain from the DESI survey paint an accurate picture of how galaxies are distributed in the universe.

Pairwise Weights and Angular Upweighting

By employing pairwise weights, astronomers can correct the counts of galaxies while taking into account the probability of each galaxy being observed. It’s a bit like giving every galaxy a star rating, ensuring that those galaxies that were less likely to be observed are still counted fairly.

Angular upweighting is another technique used to tackle the small-scale effects that arise from fiber assignment. It adjusts the weights of galaxy pairs based on the density of galaxies in a given region. This means that while some regions might have fewer observed galaxies, their influence is lessened, allowing for a more accurate interpretation of the clustering patterns.

Hunting for the Truth

As scientists analyze the data, they aim to recover the "true" clustering of galaxies. It’s like piecing together a jigsaw puzzle without having the box to guide you. But with the tools and techniques developed, researchers are continuously making strides toward uncovering the mysteries of the cosmos.

The Results Are In!

The good news is that the techniques and methods applied have shown promising results. Scientists are able to measure the galaxy distribution more accurately, even achieving the first successful power spectrum measurements with real data using the PIP weights approach!

A Bright Future Ahead

As DESI continues its journey through the universe, astronomers remain hopeful that by refining these techniques and exploring new strategies, they will keep unlocking the secrets of the cosmos. This work is crucial for our understanding of dark energy, the accelerating universe, and ultimately our place in it.

So next time you gaze at the stars, remember that there’s a lot of behind-the-scenes work happening to ensure that cosmic images tell the right story. Just like a good party, sometimes it takes a bit of juggling and teamwork to make it all come together!


And there you have it! A cosmic adventure on the challenges and triumphs of galaxy observation with a sprinkle of humor. Dive deep into the universe, and who knows what wonders you might find!

Original Source

Title: Characterization of DESI fiber assignment incompleteness effect on 2-point clustering and mitigation methods for DR1 analysis

Abstract: We present an in-depth analysis of the fiber assignment incompleteness in the Dark Energy Spectroscopic Instrument (DESI) Data Release 1 (DR1). This incompleteness is caused by the restricted mobility of the robotic fiber positioner in the DESI focal plane, which limits the number of galaxies that can be observed at the same time, especially at small angular separations. As a result, the observed clustering amplitude is suppressed in a scale-dependent manner, which, if not addressed, can severely impact the inference of cosmological parameters. We discuss the methods adopted for simulating fiber assignment on mocks and data. In particular, we introduce the fast fiber assignment (FFA) emulator, which was employed to obtain the power spectrum covariance adopted for the DR1 full-shape analysis. We present the mitigation techniques, organised in two classes: measurement stage and model stage. We then use high fidelity mocks as a reference to quantify both the accuracy of the FFA emulator and the effectiveness of the different measurement-stage mitigation techniques. This complements the studies conducted in a parallel paper for the model-stage techniques, namely the $\theta$-cut approach. We find that pairwise inverse probability (PIP) weights with angular upweighting recover the "true" clustering in all the cases considered, in both Fourier and configuration space. Notably, we present the first ever power spectrum measurement with PIP weights from real data.

Authors: D. Bianchi, M. M. S Hanif, A. Carnero Rosell, J. Lasker, A. J. Ross, M. Pinon, A. de Mattia, M. White, S. Ahlen, S. Bailey, D. Brooks, E. Burtin, E. Chaussidon, T. Claybaugh, S. Cole, A. de la Macorra, S. Ferraro, A. Font-Ribera, J. E. Forero-Romero, E. Gaztañaga, S. Gontcho A Gontcho, G. Gutierrez, J. Guy, C. Hahn, K. Honscheid, C. Howlett, S. Juneau, D. Kirkby, T. Kisner, A. Kremin, M. Landriau, L. Le Guillou, M. E. Levi, P. McDonald, A. Meisner, R. Miquel, J. Moustakas, N. Palanque-Delabrouille, W. J. Percival, F. Prada, I. Pérez-Ràfols, A. Raichoor, G. Rossi, E. Sanchez, D. Schlegel, M. Schubnell, R. Sharples, J. Silber, D. Sprayberry, G. Tarlé, M. Vargas-Magaña, B. A. Weaver, P. Zarrouk, R. Zhou, H. Zou

Last Update: 2024-11-18 00:00:00

Language: English

Source URL: https://arxiv.org/abs/2411.12025

Source PDF: https://arxiv.org/pdf/2411.12025

Licence: https://creativecommons.org/licenses/by/4.0/

Changes: This summary was created with assistance from AI and may have inaccuracies. For accurate information, please refer to the original source documents linked here.

Thank you to arxiv for use of its open access interoperability.

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