CYGNUS 2022 online meeting

Jan 10, 2022, 9:00 PM → Jan 12, 2022, 12:10 AM GMT

Online meeting. Please join at the following Zoom link.

https://anu.zoom.us/j/83173169338?pwd=azBJWXN0bU42MGlwazVBSy85S0I5Zz09

Mon, January 10 ¶

9:00 PM → 9:05 PM Introduction¶

Introduction and plan for the meeting

Speaker: Gregory Lane (Australian National University)

9:05 PM → 9:15 PM Sheffield program overview¶

Overview of the Sheffield CYGNUS program

Speaker: Neil Spooner (University of Sheffield)

CYGNUS-Sheffield100122v2.pdf

9:15 PM → 9:25 PM Japan/Kobe program overview¶

Japan/Kobe program overview

Speaker: Kentaro Miuchi (Kobe University)

miuchi_202101_CYGNUS.pdf

9:25 PM → 9:35 PM Italian/CYGNO program overview¶

Italian/CYGNO program overview

Speaker: Elisabetta Baracchini (Istituto Nazionale di Fisica Nucleare)

CYGNO_status_CYGNUS_coll_meeting_Jan_2022.key

9:35 PM → 9:45 PM UNM program overview¶

The MIGDAL project and applications to directional DM and neutrino searches

Speaker: Prof. Dinesh Loomba (UNM)

UNM_CYGNUS_10Jan2022final_small.pdf

9:45 PM → 9:55 PM Hawaii program overview¶

Hawaii program overview

Speaker: Sven Vahsen (University of Hawaii)

Hawaii_program_overview.pdf

9:55 PM → 10:00 PM Australian program overview¶

Australian CYGNUS program overview

Speaker: Lindsey Bignell (Australian National University)

CygnusIntl_ExptUpdate.pdf

10:00 PM → 10:30 PM Dark matter, neutrinos, and directional detection¶

I will discuss a few theoretical issues of relevance to directional detection. Firstly, how confident are we that there is a DM wind pointing back towards Cygnus? Then, I will explain what the “neutrino fog” is - a term increasingly being used in place of “neutrino floor” by the community. I will then describe how using the neutrino fog as a way to rank techniques for directional detection naturally leads to gas TPCs as the optimal strategy.

Speaker: Ciaran O'Hare (University of Sydney)

cygnusmeeting.pdf

10:30 PM → 10:50 PM Hints of negative ion drift operation with CYGNO optical readout approach¶

Speaker: Elisabetta Baracchini (Istituto Nazionale di Fisica Nucleare)

Baracchini_CYGNUS_coll_meeting_2021_NID_hint.pdf

Tue, January 11 ¶

9:00 PM → 9:17 PM Clustering and energy response of CYGNO 50 L prototype LIME¶

Speaker: Emanuele Di Marco (Istituto Nazionale di Fisica Nucleare: Roma)

2022-01-11-clustering-linearity-cygnus.pdf

9:17 PM → 9:34 PM Development of low RI molecular sieve for radon removal from gas¶

Molecular sieve (MS) is expected to remove impurities such as radon from gases that require low radioactivity in dark matter search detectors. For practical use, it is necessary to reduce the amount of radioactive impurities released from itself to the utmost limit. In this talk, I report on the development of zeolite with radon adsorption capacity by ion exchange of low-radioactive MS.

Speaker: Hiroshi Ogawa (Nihon University)

2201-cygnus-ogawa-MS.pdf

9:34 PM → 9:51 PM Molecular sieve-based gas recycling system with radon reduction for rare-event gaseous detectors¶

A new molecular sieve-based gas recycling system is presented that provides for simultaneous removal of both radon and common impurities from SF6:CF4:He gases in TPCs, hence minimising the total amount of gas required. Removal of internally-produced radon and associated progeny is important for background suppression whilst removal of outgassing and leaked-in contaminants such as water, oxygen and nitrogen is required to suppress capture of interaction-produced electrons which causes gain suppression. The system utilises a Vacuum Swing Adsorption (VSA) technique, allowing continuous long-term operation. Studies are presented of a new low radioactive molecular sieve, developed for this work and found to emanate radon up to 98% less per radon captured than commercial material.

Speaker: Robert Renz Gregorio (University of Sheffield)

GREGORIO2022_CYGNUSMEETING.pdf

9:51 PM → 10:08 PM Electroluminescence and gas studies in CYGNO¶

Speaker: Giorgio Dho (Gran Sasso Science Institute)

CYGNUS_onlinemeeting_Dho_11_1_22.pdf

10:08 PM → 10:25 PM Studies on He-CF4-isobutane mixtures for the CYGNO TPC¶

Speaker: Cristina Monteiro (Universidade de Coimbra)

EL in He_CF4_isobutane_CristinaMonteiro.pdf

10:25 PM → 10:42 PM Towards Directional Dark Matter Detectors using High Gain Negative Ion TPCs¶

Speaker: Ali McLean (University of Sheffield)

10:42 PM → 10:59 PM Recent updates of C/N-1.0 chamber and its circular system¶

After the detection of the first light of a radiation source using C/N-1.0, we solved various remaining issues; e.g. gas leak, spark and detector readout problems. We will introduce about these update and also report the development of our circular system.

Speaker: Satoshi Higashino (Kobe University)

higashino_CYGNUS2021.pdf

10:59 PM → 11:11 PM Validation of gas gain with Garfield++ simulation for negative ion gas TPC¶

The basis for Garfield ++ simulation for negative ion gas was developed last year. Two types of detach models (Threshold model & Cross-section model) were prepared and we simulate the gas gain for each. We confirmed that the measured gain in GEM and the results of simulation are consistent.

Speaker: Ryo Kubota (University of Kobe)

kubota_CYGNUS_meeting_2022.pdf

https://github.com/hi6hi3/garfieldpp_nitpc_mod

↑

This is the url of github.

11:11 PM → 11:33 PM Machine learning studies for improved electron background rejection in pixel TPCs¶

We present the status of ongoing studies comparing the electron background rejection performance of multivariate combinations of physically motivated observables tuned to optimize electron rejection performance with a convolutional neural network (CNN) event classifier trained directly on reconstructed 3D charge distributions of recoil events. Using samples of simulated charge distributions containing O(1e7) electron recoils and O(2e5) fluorine recoils after 25cm of drift in an 80:10:10 mixture of He:CF4:CHF3 at 60 torr, binned into 100um^3 voxels and ranging in energies between 0.5 and 10.5 keVee, we first show that combining nine predefined observables using either a boosted decision tree (BDT) or a feed forward neural network (NN) to classify recoils in this detector, leads to between a 1.5 and 5 fold increase in the number of rejected electrons as a function of energy at 50% F-recoil efficiency compared to a previously published multivariate combination of these same observables that doesn't use machine learning. We further show that training a CNN with fluorine and electron recoil charge distributions re-binned into a 32 x 32 x 32 voxel grid leads to a noticeable improvement in electron rejection over both the BDT and NN combinations of predefined observables at nearly all energies. Finally, we briefly summarize the status of extending this work to measurements from miniature TPCs with pixel ASIC readout.

Speaker: Jeffrey Schueler (University of Hawaii at Manoa)

CYGNUS_electron_rejection_ML.pdf

11:33 PM → 11:40 PM Snowmass process and white paper¶

Discussion of the Snowmass process and potential contributions to the white paper

Speakers: Ciaran O'Hare (Zaragoza), Dinesh Loomba (UNM), Sven Vahsen (University of Hawaii)

snowmass.pdf