My research focuses on measuring the properties of active galactic nuclei (AGN), specifically their black hole masses, inner structure, and variability characteristics. I am also interested in the role supermassive black holes play in galaxy evolution and the properties of outflowing gas in the inner regions of AGN.

**Measuring Black Hole Masses in AGN**

I work on measuring the masses of supermassive black holes (BHs) in AGN using the reverberation mapping technique. Reverberation mapping works by measuring the time lag between changes in 1) the variable ionizing AGN continuum flux generated in the accretion disk and 2) the flux from broad emission lines generated in the more distant broad line region (BLR). The time lag between changes in flux from the accretion disk versus the BLR is mainly due to light travel time and can thus be used as a measurement of the size of the BLR. Combining the size of the BLR with the velocity of the BLR gas measured from the width of the broad emission lines, we can measure a BH mass. Since the BLR cannot currently be spatially resolved, we do not know its detailed geometry or dynamics, information which is crucial for properly normalizing BH masses measured using the reverberation mapping technique. The traditional method for normalizing reverberation mapped BH masses is to assume they have the same relationship between the BH mass and the host galaxy stellar velocity dispersion as for quiescent galaxies in the local Universe, where we can measure the BH mass by resolving the motions of gas and stars within the BH sphere of influence. Using this traditional method for normalizing reverberation mapped BH masses introduces a large uncertainty in individual BH masses on the order of 0.4 dex (a factor of 2.5).

In order to improve the precision of reverberation mapped BH masses, we need to understand the detailed structure of the BLR. For my Ph.D. thesis, I developed a new method to analyze reverberation mapping data that simultaneously measures the BH mass while constraining the geometry and dynamics of the BLR. In this approach, the BH mass can be measured more precisely and the previously unknown properties of the BLR can be explored. The method involves modeling high-quality reverberation mapping datasets (including a time series of broad emission line profiles and a time series of AGN continuum flux measurements) using a geometric and dynamical model of the BLR within a Bayesian statistics framework. Development of this new technique was done in collaboration with Brendon Brewer and Tommaso Treu, and information about our code CARAMEL can be found here.

Papers on the method of dynamical modeling of reverberation mapping data:

- Pancoast, Brewer, & Treu 2014: Modelling reverberation mapping data – I. Improved geometric and dynamical models and comparison with cross-correlation results
- Pancoast, Brewer, & Treu, 2011: Geometric and Dynamical Models of Reverberation Mapping Data

**Figure 1:** Inferred BLR geometry model for five AGNs from the LAMP 2008 dataset (from Pancoast et al. 2014b). The left column shows an edge-on view of the BLR (observer is looking from the RHS or positive x-axis) and the right column shows a face-on view. The size of the points corresponds to how much line emission comes from each point.

So far we have applied this new dynamical modeling approach to a number of high-quality reverberation mapping datasets listed below. The BH mass measurements from this work are also given on the AGN Black Hole Mass Database.

**The Lick AGN Monitoring Project 2008** (Bentz et al. 2009, Walsh et al. 2009):

- We first tested the dynamical modeling approach on the most variable AGN in the sample, Arp 151. Brewer, Treu, Pancoast, et al. 2011: The Mass of the Black Hole in Arp 151 from Bayesian Modeling of Reverberation Mapping Data
- We later analyzed the five most variable AGN in the sample, including Arp 151, Mrk 1310, NGC 5548, NGC 6814, and SBS 1116+583A, using an improved model of the BLR. We found that 1) the geometry of the Hβ-emitting BLR is a thick disk with preferential emission from the far side of the BLR as shown in Figure 1 above, 2) the dynamics of the Hβ-emitting BLR generally range from near-circular orbits to inflowing orbits, and 3) the BH mass can be measured to within 0.15-0.3 dex uncertainty (compared to ~0.4 dex uncertainty with the traditional reverberation mapping analysis). Pancoast, Brewer, Treu, Park, Barth, Bentz, & Woo 2014: Modeling reverberation mapping data – II: Dynamical modeling of the Lick AGN Monitoring Project 2008 dataset
- This dataset is available for download here.

**The Lick AGN Monitoring Project 2011** (Barth et al. 2011, Barth et al. 2013, Barth et al. 2015):

- We have so far analyzed the most variable AGN in the sample, Mrk 50. We found that the geometry of the Hβ-emitting BLR is a thick disk viewed very close to face-on with a BH mass measurement constrained to within ~0.35 dex uncertainty. The face-on BLR geometry prevented us from measuring the BH mass more precisely in this object. Pancoast, Brewer, Treu, et al. 2012: The Lick AGN Monitoring Project 2011: Dynamical Modeling of the Broad-line Region in Mrk 50
- Analysis of the full sample, including Mrk 50, using an improved model of the BLR is forthcoming.

**The Ohio State University reverberation mapping collaboration 2010** (Grier et al. 2012): Work in progress with Catherine Grier for an additional four AGNs.

I also work on a number of other reverberation mapping campaigns, including the AGN Space Telescope and Optical Reverberation Mapping Program (AGN STORM), the LCOGT AGN Key Project, and the Lick AGN Monitoring Project (LAMP) 2016.

*Do you have high-quality reverberation mapping datasets that would benefit from dynamical modeling analysis or ideas for improving our geometric and dynamical model of the BLR?* Feel free to contact me!

**Measuring AGN Variability in the Optical
**

In addition to modeling the BLR, I have also been involved with the Lick AGN Monitoring Project (LAMP) 2011 collaboration measuring photometric AGN light curves in the optical for reverberation mapping. Using image subtraction software allows us to isolate the variable AGN flux from that of the host galaxy, while modeling the AGN light curves using Gaussian Processes (as we do in dynamical BLR modeling using CARAMEL as shown in Figure 2 below) allows us to align image subtraction light curves from different telescopes in post-processing.

**Figure 2**: Examples of modeling AGN optical continuum light curves (*B* and *V* band) using Gaussian Processes (from Pancoast et al. 2014b). The black points are the data and the brightly colored lines are different examples of the model interpolation drawn from the posterior PDF.