Close Pairs and Mergers

Contact

MaNGA merger 7443-12703
Lihwai Lin
Academia Sinica, Institute of Astronomy and Astrophysics (ASIAA)
Fangting Yuan
Shanghai Astronomical Observatory

Summary

A sample of Close Pair and Merger candidates either from the MaNGA main sample or from the related ancillary programs.

Finding Targets

An object whose MANGA_TARGET3 or MNGTARG3 value includes one or more of the bitmasks in the following table was targeted for spectroscopy as part of this ancillary target program. See SDSS bitmasks to learn how to use these values to identify objects in this ancillary target program.

Program (bit name) Bit number Target Description
Pair_1ifu_enlarge 7 Close pairs require enlarging bundles
Pair_1ifu_recenter 8 Close pairs require IFU recentering
Pair_1ifu_masters 9 Close pairs from the Galaxy Zoo: Mergers sample
Pair_2ifu 10 Close pairs require a separate IFU from overlapping tiles

Description

Hydrodynamic simulations show that the star formation rate can be boosted due to gas infall induced by the tidal force during mergers (Mihos & Hernquist 1996; Cox et al. 2008). Gas infall can also trigger active galactic nuclei (AGN), heating and blowing out the remaining gas (Hopkins et al. 2006). Observationally, the majority of local star-bursting galaxies (e.g., ultra-luminous infrared galaxies) and QSOs are found to be associated with galaxy mergers (Sanders et al. 1988; Borne et al. 1999), which supports the idea that mergers play an important role in consuming the gas of galaxies and in triggering AGNs, leading to the death of star formation activity.

This pair sample includes early-, intermediate-, and late-stage of mergers with various combinations of galaxy types (gas-rich, gas-poor, and mixed mergers) in different environments. The spatially resolved stellar populations delivered by MaNGA will provide insights into where the star formation is triggered and ceased, while the kinematics maps will help pinpoint the phase of mergers and improve our understanding in the physical processes occurring during mergers.

Target Selection

(Note: the number quoted below may not reflect the actual numbers in the final MaNGA sample as the final MaNGA footprint will only be 1/3 of the SDSS DR7 area.)

The close pair sample contains a list of objects with their projected separation (rp) < 50 kpc/h and line-of-sight velocity (dV) < 500 km/s, selected based on the NSA catalog (v1_0_1) and Xiaohu Yang’s SDSS group catalog (Yang et al. 2007 and in private communication with X. Yang) whenever the redshifts of the two merging components are available. We divide the sample into two classes:

(A) Single IFU sample

For pairs with at least one component being targeted by MaNGA and with 1.5 * r501 + angsep 1.5 * r502 < 32.5″ (32.5” is the largest MaNGA bundle size in diameter), where r50 represents the Petrosian radius and angsep is the angular separation between the two nuclei, they can be accommodated with one MaNGA IFU bundle. Depending on the observing strategy, these sample can be further divided into three classes:

A1. Pair_1ifu_enlarge (bit = 7): This includes 187 pairs that require an enlargement of the MaNGA IFU.

A2. Pair_1ifu_recenter (bit = 8): This includes 125 pairs that can be fit with the original assigned MaNGA IFU but require re-centering

A3. The remaining 106 pairs which can be accommodated by the original assigned IFU sizes and centers. In other words, they are automatically included in the MaNGA main sample and hence are not included in the sample with the bit in ‘MANGA_TARGET3’ described here.

Apart from the above three classes, we also include 5 pairs provided by Karen Masters:

A4. Pair_1ifu_masters (bit = 9): Galaxy Zoo Mergers. This is a list of 5 objects which were part of the Galaxy Zoo: Mergers Sample (Hollincheck et al. 2016; list provided by Karen Masters). These galaxies have detailed N-body simulations matched to their observed morphology which predict the expected velocity field MaNGA will observe. These targets can all be fit within one bundle. One object was in the MaNGA target sample (the companion galaxy does not have redshift measurement and therefore not included in A1, A2 or A3), while the remaining 4 objects require allocation of new IFUs. Contact person for this subsample: Karen Masters (Karen.Masters@port.ac.uk).

(B) Two IFU sample (pairs that require a separate IFU from overlapping tiles)

Pairs that will fit into a single IFU must either be very close in projected separation or at the highest redshifts in the MaNGA sample. To compensate this effect, we have also constructed the “Two IFU sample ”, which asks for a separate IFU from overlapping tiles. This sample either has better spatial resolution than the ones that can be fit within one IFU for a fixed physical separation, allowing for more detailed mapping of the merger sample, or they are more physically separated. It is worth noting that the MaNGA main sample already include ~125 pairs with both components being targeted by MaNGA. However, they are biased toward red-red mergers, which are not optimal for the study of interaction-triggered star formation. In order to have enough statistics in each parameter bin, we consider pairs where at least one component is blue. The full criteria are:

  • dv < 300 km/s (this is to reduce the interlopers)
  • at least one component has the blue color (u_abs – r_abs) < 2.2
  • the stellar mass ratio < 3
  • located in overlapping tiles
  • 2*1.5*r50 < 32.5″
  • remove stars

This yield 174 out of 2601 pairs which require a second IFU. In addition to the pairs, we also include 24 n-tuples where primary and tertiary are already targeted and at least 3 MaNGA tiles cover the field.

B1: Pair_2ifu (bit = 10): This includes 174 pairs plus 24 n-tuples.

Results

Example of papers using products from this program have been published by Pan et al. (2018) and Pan et al. (2019).

REFERENCES

Borne, K. D., et al. 1999, Ap&SS, 266, 137
Cox, T. J. et al. 2008, MNRAS, 384, 386
Hopkins, P. F. et al. 2006, ApJS, 163, 1
Holincheck, A. et al. 2016, MNRAS, 459, 720
Mihos, J. C. & Hernquist, L. 1996, ApJ, 464, 641
Pan, H.-A. et al. 2018, ApJ, 868, 132
Pan, H.-A. et al. 2019, ApJ, 881, 119
Sanders, B., Soifer, B. T., Elias, J. H., Madore, B. F., Matthews, K., Neugebauer, G., & Scoville, N. Z. 1988, ApJ, 325, 74
Yang, X., Mo, H. J., van den Bosch, F. C., et al. 2007, ApJ, 671, 153