The low-surface-brightness Universe: a new frontier in the study of galaxy evolution (2001.01728v1)

Abstract: New and forthcoming deep-wide surveys, from instruments like the HSC, LSST and EUCLID, are poised to revolutionize our understanding of galaxy evolution, by revealing aspects of galaxies that are largely invisible in past wide-area datasets. These surveys will open up the realm of low-surface-brightness (LSB) and dwarf galaxies -- which dominate the galaxy number density -- for the first time at cosmological distances. They will also reveal key, unexplored LSB structures which strongly constrain our structure-formation paradigm, such as merger-induced tidal features and intra-cluster light. However, exploitation of these revolutionary new datasets will require us to address several data-analysis challenges. Data-processing pipelines will have to preserve LSB structures, which are susceptible to sky over-subtraction. Analysis of the prodigious data volumes will require machine-learning (in particular unsupervised techniques), to augment or even replace traditional methods. Cosmological simulations, which are essential for a statistical understanding of the physics of galaxy evolution, will require mass and spatial resolutions that are high enough to resolve LSB/dwarf galaxies and LSB structures. And finally, estimation of physical properties (e.g. stellar masses and star formation rates) will require reliable redshift information. Since it is unlikely that even next-generation spectrographs will provide complete spectral coverage in the LSB/dwarf regime outside the nearby Universe, photometric redshifts may drive the science from these datasets. It is necessary, therefore, that the accuracy of these redshifts is good enough (e.g. < 10 per cent) to enable statistical studies in the LSB/dwarf regime. I outline the tremendous discovery potential of new/forthcoming deep-wide surveys and describe techniques which will enable us to solve the data-analysis challenges outlined above. (Abridged)

• The paper highlights the transformative potential of upcoming deep-wide surveys like HSC, LSST, and EUCLID for exploring the low-surface-brightness universe and advancing galaxy evolution studies.
• Exploiting these surveys faces significant data processing challenges, including preserving faint features and managing large datasets with traditional methods, as well as limitations in current cosmological simulation resolution.
• These new data offer potential insights into unresolved issues like the dwarf galaxy problem and core-cusp challenge, providing critical empirical data to calibrate simulations and advance statistically-driven studies.

Insights into the Low-Surface-Brightness Universe and Its Implications for Galaxy Evolution Studies

The paper of galaxy evolution has long been a central theme in astrophysics, but recent advancements in observational techniques and simulations have pointed to a new domain of significant interest: the low-surface-brightness (LSB) universe. Sugata Kaviraj’s paper addresses the potential of upcoming deep-wide surveys to facilitate a substantial advancement in our understanding of the LSB universe and galaxy evolution.

Main Contributions and Findings

Kaviraj’s work underscores the transformative potential of forthcoming surveys, such as those conducted by the Hyper-Suprime-Cam (HSC), Large Synoptic Survey Telescope (LSST), and the EUCLID mission, which aim to catalog and investigate both LSB galaxies and the fine structures within high-surface-brightness (HSB) systems. These surveys will significantly extend the surface-brightness limits that have constrained previous observations, thus enabling the exploration of galaxies beyond the traditional observational bounds.

An essential focus of the paper is on the LSB and dwarf galaxies, which are critical due to their numerical dominance in the galaxy population. Nevertheless, these galaxies have remained largely inaccessible at cosmological distances due to their faintness, which limits their detection in legacy surveys like the Sloan Digital Sky Survey (SDSS). Furthermore, the paper discusses the importance of LSB structures such as merger-induced tidal features and intra-cluster light (ICL) as crucial test beds for theories of galaxy assembly and structure formation.

Challenges and Technical Considerations

The exploitation of these new survey capabilities comes with substantial challenges, particularly in data processing and analysis. Traditional data-processing pipelines are insufficient for preserving LSB features, prone to issues such as sky over-subtraction and shredding by de-blending algorithms. Other notable challenges include managing the voluminous datasets generated by these surveys, which necessitate machine-learning techniques—specifically unsupervised learning methodologies—to streamline data reduction and analysis workflows.

Additionally, the resolution required by cosmological simulations to model LSB galaxies and their environments remains a pressing challenge. Existing simulations often lack the granularity necessary to capture the nuances of LSB regimes fully. These simulations require enhanced spatial and mass resolution to bring theoretical models in line with the new observational data these surveys will provide.

Implications and Future Directions

The paper offers a forward-looking perspective on how deep-wide surveys can shift theoretical paradigms in galaxy evolution. There is potential for definitive insights into unresolved issues such as the apparent scarcity of dwarf galaxies in simulations and the core-cusp problem. Moreover, these surveys could provide critical empirical data to calibrate cosmological simulations, particularly by offering statistical insights into the properties and distribution of LSB galaxies.

Finally, the reliance on photometric redshifts, due to anticipated incomplete spectroscopic coverage, opens new avenues for statistically driven studies of galaxy evolution. These developments will necessitate improvements in photometric redshift accuracy to extract robust implications from the gathered data.

Conclusion

Kaviraj concludes by acknowledging the substantial potential of these forthcoming surveys to advance the field, contingent upon overcoming significant data-analysis hurdles. As a result, the community may soon enter an era of unprecedented empirical and theoretical exploration, deeply informed by the interplay between innovative observational campaigns and enhanced computational simulations. This paper thus serves as both a roadmap and a call to action for researchers to prepare for and engage with the forthcoming data revolution in extragalactic astronomy.