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We present a method for estimating the amount of matter in large-scale (approximately 50 Mpc) filaments using the surrounding velocity infall pattern, based on 242 filaments in the Millennium simulation. We identify filaments using a minimal spanning tree to link large groups and clusters, and find the axis of each filament using a weighted principle component analysis. We improve our previous determination of a typical infall velocity profile by rescaling the profile for each filament by the distance where the infall speed reaches a maximum. We use the resulting average profile to determine a two-parameter piecewise function that can be used to estimate the maximum infall speed and location for individual filaments. Finally, we present the correlation between the maximum infall speed and the mass of the filament. These results will be used as part of the Arecibo Pisces-Perseus Supercluster Survey (APPSS), a project to map the infall pattern around the Pisces-Perseus Supercluster filament. This work is supported by NSF grant AST-1637339. 

The Pisces-Perseus Supercluster is one of the most massive and cosmologically significant structures in the local universe. The Arecibo Pisces-Perseus Supercluster Survey (APPSS) will provide observational constraints as to the mass-infall rate onto the main filament of the Supercluster through a detailed analysis of the mass and motion of galaxies within and around the cluster. The APPSS galaxy sample consists of over 2,000 galaxies detected during the ALFALFA survey (a blind, HI 21-cm emission line survey of the local universe) combined with galaxies identified through our recent targeted observing campaign - designed to probe below the HI mass cutoff of the ALFALFA survey. These APPSS-candidates were observed using the L-band Wide receiver at the Arecibo Observatory over the last 4 years; to date the APPSS targeted observing has led to an HI 21-cm emission line detection rate of ~70% - corresponding to ~500 galaxies with cz < 9,000 km/s. Combining these new observations with the ALFALFA galaxies gives a total of ~2,500 galaxies in the current APPSS sample. Here, we describe and demonstrate the methods used by the APPSS team to reduce and analyze these targeted observations and explore the properties of the entire APPSS galaxy sample (while comparing the properties of the ALFALFA galaxies with the detections from the APPSS targeted observing campaign). This work has been supported by NSF AST-1637339. 

Developments in open-source high-level programming languages enable undergraduate students to make vital contributions to modern astronomical surveys. The Arecibo Pisces-Perseus Supercluster Survey (APPSS) currently uses data analysis software written in Interactive Data Language (IDL). We discuss the conversion of this software to the Python programming language, which uses freely available standard libraries, and the conversion of the data to a standard form of the Single-Dish FITS (SDFITS) standard. Data Reduction Integrated Python Protocol (DRIPP) provides user-guided data reduction with an interface similar to the former software written in IDL. Converting to DRIPP would provide researchers with more accessible data processing capabilities for APPSS (or any similar radio spectral survey). This work has been supported by NSF AST-1637339. 

In preparation for comparison with the Arecibo Pisces-Perseus Supercluster Survey (APPSS), we present the theoretically expected density and velocity profiles for large-scale (~ 50 Mpc) filaments from the Millennium simulation. We use an observationally-friendly method to identify filaments using the positions of large groups of galaxies, and average filaments together to find the typical structure of a filament in terms of cylindrical density profile and velocity infall profile. Both profiles can be fit by simple functions, but show a large scatter across the population of filaments. We are in the process of categorizing filaments to facilitate comparison with observations of specific filaments, like the Pisces-Perseus Supercluster filament. This work has been supported by NSF grant AST-1637339. 

The Arecibo Pisces Perseus Supercluster Survey (APPSS) is an HI survey measuring galaxy infall into the filament and clusters. Galaxies were selected for HI observations based on their location within the Pisces Perseus supercluster and SDSS and GALEX colors predictive of cold gas content. Most of the HI observations were conducted at Arecibo using the L Band Wide receiver, with some high-declination coverage provided by Green Bank. The observations provide increased sensitivity compared to ALFALFA blind survey data. For this project, we investigated a subset of 132 APPSS galaxies with declinations near 27 degrees. Using custom data reduction and analysis tools developed for the Undergraduate ALFALFA Team, we determined the following information for galaxies in our subset: systemic velocity, line width, integrated flux density, HI mass, and gas fraction (or corresponding limits for non-detections). We calculate our HI detection fraction and mean gas fraction as a function of stellar mass and compare to previous results. We investigate the distribution of systemic velocities for our galaxies with their location on the sky. Finally, we discuss several interesting sources from our subset of APPSS galaxies. This work has been supported by NSF grants AST-1211005, AST-1637299, and AST-1637339