1. New catalogue of Pre-Main Sequence objects using Gaia
Miguel Vioque
University of Leeds
R. D. Oudmaijer (University of Leeds, UK), M. Schreiner (Desupervised, Denmark), D. Baines (ESAC, Spain), and R. Pérez-Martínez (Isdefe,Spain)
Gaia’s view of Pre-Main Sequence Evolution, Leeds, 20th of June 2019

2. Looking for new Pre-Main Sequence (PMS) objects in Gaia!
Main characteristics of PMS objects:
Infrared excesses
H𝛼 emission
Photometric variability

3. Looking for new Pre-Main Sequence (PMS) objects in Gaia!
Main characteristics of PMS objects:
Infrared excesses
H𝛼 emission
Photometric variability
High mass PMS objects (Herbig Be stars) are very similar to Classical Be stars

4. Perform an homogeneous selection, distance and position independent!
Looking for new Pre-Main Sequence (PMS) objects in Gaia!
Perform an homogeneous selection, distance and position independent!
Main characteristics of PMS objects:
Infrared excesses
H𝛼 emission
Photometric variability
High mass PMS objects (Herbig Be stars) are very similar to Classical Be stars

5. Distance and position independent!
We used a Neural Network for this:
Selection of the characteristics:
From Gaia: 𝑩 𝒑 , 𝑮, 𝑹 𝒑 and 2 variability indicators
From AllWISE: 𝑱, 𝑯, 𝑲 𝒔 , 𝑾𝟏,𝑾𝟐,𝑾𝟑,𝑾𝟒
From IPHAS & VPHAS+: 𝒓− 𝑯 𝜶
Distance and position independent!
Create all possible colours
Remove all linear dependency
(PCA)

6. Cross-match Gaia DR2 x AllWISE x IPHAS and VPHAS+
Input Sample = 4,151,538 sources
Construction of the Training Set:
848 Pre-Main Sequence objects
163 are Herbig Ae/Be stars
(high mass end, all available)
775 Classical Be stars (all available).
471,111 random sources with all the characteristics.

7. Cross-match Gaia DR2 x AllWISE x IPHAS and VPHAS+
Input Sample = 4,151,538 sources
Construction of the Training Set:
848 Pre-Main Sequence objects
163 are Herbig Ae/Be stars
(high mass end, all available)
775 Classical Be stars (all available).
471,111 random sources with all the characteristics.

8. Training the Neural Network

9. Trained Neural Network
Input Sample = 4,151,538 sources

10. Probability Map

11. 693 Classical Be candidates
Probability Map
693 Classical Be candidates
1309 either
8470 PMS candidates
4,140,511 other

12. 693 Classical Be candidates Classical Be Completeness 𝟖𝟐.𝟑±𝟏.𝟏%
Probability Map
693 Classical Be candidates
Evaluation on Test Set
PMS Completeness 𝟕𝟖.𝟖±𝟏.𝟒%
Classical Be Completeness 𝟖𝟐.𝟑±𝟏.𝟏%
1291 either
8470 PMS candidates
4,140,511 other

13. There is a large contamination between categories!
Cross-match Gaia DR2 x AllWISE x IPHAS and VPHAS+
Input Sample = 4,151,538 sources
Construction of the Training Set:
There is a large contamination between categories!
848 Pre-Main Sequence objects
163 are Herbig Ae/Be stars
(high mass end, all available)
775 Classical Be stars (all available).
471,111 random sources with all the characteristics.

14. There is a large contamination between categories!
Cross-match Gaia DR2 x AllWISE x IPHAS and VPHAS+
This algorithm cannot assess itself, we need a totally independent analysis
Input Sample = 4,151,538 sources
Construction of the Training Set:
There is a large contamination between categories!
848 Pre-Main Sequence objects
163 are Herbig Ae/Be stars
(high mass end, all available)
775 Classical Be stars (all available).
471,111 random sources with all the characteristics.

15. HR diagram
Training Set
PMS candidates

16. HR diagram
Training Set
Classical Be candidates

18. 3131 potential Herbig Ae/Be (682 with good Gaia solution)

19. Coordinates

20. Coordinates

21. Coordinates

22. Coordinates

23. Spatial distribution
Within 1.5 Kpc
All (up to 5 Kpc)

24. Spatial distribution
Within 1.5 Kpc
All (up to 5 Kpc)

25. Mid-IR excess vs. Mass (lower limits)
Candidates
Training Set

26. Variability vs. Mass (lower limits)
Candidates
Training Set

27. Variability vs. Mass (lower limits)
Candidates
Training Set
Vioque et al. 2018

28. Candidates
Cody & Hillenbrand, 2018

29. Caveats
Candidates
H𝛼 emission
Main Sequence
Reddening

30. Caveats
Candidates
H𝛼 emission
Main Sequence
Reddening

31. Caveats Planetary Nebula! H𝛼 emission Main Sequence Reddening
Candidates
H𝛼 emission
Main Sequence
Reddening

32. Results:
We retrieve 8470 new PMS candidates (682) potential Herbig Ae/Be stars.
We retrieve 693 new Classical Be stars candidates.
We retrieve 1309 candidates of belonging to either one of the two categories.
Completeness 𝟕𝟖.𝟖±𝟏.𝟒%
Completeness 𝟖𝟐.𝟑±𝟏.𝟏%
Near- and mid- infrared excesses are the most important characteristics followed by H𝛼 and variability which are equaly important